 Namaste and welcome back to the video course on Watershed Management. In module number 6 on use of modern techniques in watershed management, in lecture number 26 today we will discuss about the applications of knowledge based models in watershed management. So, in this lecture some of the topics covered include knowledge based modeling, multi-criteria decision analysis, fuzzy logic based modeling, fuzzy systems, applications of knowledge based systems in watershed management. Some of the important keywords knowledge based model, multi-criteria decision analysis, fuzzy logic based modeling. So, as we discussed earlier say the modern techniques like Geographic Information Systems, computer models, then remote sensing, then decision support system, all these things all these models all these modern models are very useful. In the effective management of many engineering problems say like watershed management or water resource management etcetera. So, we have seen the applications of computer models or numerical models, then the Geographic Information Systems, remote sensing and decision support systems earlier. So, we can have some specified system for various specific types of problems like a say irrigation management or the land use management or crop management related to watershed. We can combine this many of this modules together and then we can have a system called or a model called knowledge based models. So, this knowledge based models are very useful to decide or to say to decide which way we have to do various managing practices or which way we have to go for various plans as far as watershed disconsent. So, actually this knowledge based models are also one way say they are also decision support systems or decision support models, but in the knowledge based models we are using the artificial intelligence techniques like fuzzy logic or the techniques like genetical or the more artificial neural networks etcetera. So, that is why that these kinds of models are generally called as knowledge based models. So, now let us look into various aspects of knowledge based models. So, here in this slide I have given some say information on knowledge based models. So, knowledge based systems are computer systems that are programmed to imitate the human problem solving ability by means of artificial intelligence tools. So, actually we can have a certain artificial intelligence tools like fuzzy logic or the ANN or artificial neural network or genetic operators or genetic algorithm very similar to the human intelligence type of systems. So, that can imitate the human problem very effectively. So, that way the computer systems when it is the models are made that way we call those kinds of models as knowledge based systems or knowledge based models. So, human reasoning using natural language can be reproduced in knowledge based systems through various artificial intelligence tools like fuzzy logic. So, if this happens what will be the outcome so like that say or a between say between good and bad how the variation is it is not a very specific either good or bad, but between that what can happen. So, like that this with human reasoning kind of systems we can use to have a knowledge based models. So, in knowledge based models the knowledge can be derived from basic analysis or experts knowledge collected through surveys and heuristic information from the field. So, this knowledge based models can be obtained from the basic data analysis or experts knowledge or through heuristic information say are related to that particular problems. So, experts knowledge and heuristic information related to the specific problems are generally stored in the form of rule based. So, depending upon the problem depending upon the watershed or area which we are dealing we can generate specific rules and then we can store these rules in a rule based and then using those rule based we can generate scenarios or what can happen if this particular things is done. So, like that we can create various scenarios. So, those types of models are called a knowledge based models. So, here further on knowledge based modeling a knowledge based is an organized body of knowledge that provides a formal logical specification for the interpretation of information. So, say for example, if the watershed say if the land what is available whether it is say suitable for say paddy cultivation or midlet cultivation or in peace cultivation. So, like that we can consider various information on the particular area and then we can generate say the suitability of lands say depending upon the various details available. So, in the knowledge based modeling approach say for example, if watershed is concerned and watershed assessment is a multi criteria evaluation in which knowledge of the experts is used to define the factors characterizing the watershed and the logic relations between the factors. So, we can see that when we deal with the watershed assessment or watershed management a number of criteria like the land use then water availability then the present coping pattern then neighborhood to the ponds or water bodies or neighborhoods to the transport systems. So, like that we have to consider various criteria so called a multi criteria and then based upon this we can derive certain logical relationships using these factors and then say we can develop a knowledge based model for the particular type of problem. So, that way the knowledge based encapsulates the assessments criteria and the relationships in an explicit form so that they can be easily examined, modified or updated. So, depending upon the problems so we can create a say rule based based upon the database and then that rule based can be encapsulated within the systems which defines the various relationships and criteria and then based upon that we can have a knowledge based model and that can be used to say predict or to say that is this particular land can be used to form say this kind of cultivation or if say what kind of supplementary reactions to be generated for the cultivation of particular crop like that. So, that way a knowledge based modeling can be done. So, now let us look into various aspects of knowledge based systems. So, the knowledge based contains knowledge and experience for the subjects domain. So, whatever we are dealing that subject domains like a domain knowledge and specifies the local relations amongst topics of interest to an assessment. Say for example, if the assessment is a the whether particular crop is suitable for that particular lands. So, then we have to consider the type of soil, then the water availability, then various other factors which we have to consider or the domain knowledge which is required for that particular problems and then we can create the knowledge based. So, then inference engines performs knowledge based approximate reasoning to draw conclusions about the state of the systems. So, we can generate the inference engines and this inference engines performs knowledge based approximations and then based upon that we can generate particular scenarios or we can take particular decisions based upon this type of systems. So, by integrating knowledge based reasoning into GIS environment provide decision support for watershed management. So, as we discussed earlier say for example, if a geofill information systems say when we integrate these kinds of knowledge based models or knowledge based systems in a GIS environment, then we can see that. So, what is happening within the systems or what is happening for that particular problem all those within the given inputs and then generate outputs we can easily visualize the various aspects and then we can go for particular decision according to the requirement. So, that way the knowledge based model process various say various relationships for that particular problems and then that gives certain decisions or scenarios. So, that way as I mentioned the GIS applications provide data based management spatial analysis system interface and map display. So, when we integrate this knowledge based systems with GIS say we can have the data based management spatial analysis like a spatial variation we can easily identify then system interface say GIS itself is system interface and then even we can generate various maps for display. And then the assessment system allows users to evaluate the knowledge based for a specific spatial database and view the results. So, since when we integrate the knowledge based model or knowledge based systems within the GIS environment. So, we can easily see the say what is happening with the spatial variation or within the spatial database and then we can see within the GIS environment the results. So, that way it is much easier much useful for decision makers say like if this is done what will be happening. So, like that various decisions can be easily taken. So, now let us look into the knowledge based structure. So, what is the structure of a particular knowledge based. So, knowledge based structure is a hierarchy of dependency networks. So, say depending upon the problems say we have to go through various processes various hierarchy of the various networks within the system. So, each network evaluates a specific proposition above the state of say for example, watershed if watershed condition. So, if this particular watershed or particular area of the watershed whether it is suitable for say in paddy cultivation or any kind of cultivation or that particular crop then it has to go through say a series of the various networks and then each network is evaluated and then we identify whether that is suitable or not. So, knowledge based structure is designed to address the issues concerned by the watershed managers say for example, if you consider watershed and to reflect their opinions on the importance of each issue. So, in the knowledge based structure we can consider various relationships various issues and then we say the after effects if that particular thing is then what will happen. So, that if this is done what then what will happen. So, like that it will be given. So, that is will be that will be very useful for the watershed management management or say it will be useful to for the decision makers. So, at the top of the hierarchy is the network watershed conditions say for example, if you consider watershed then at the top of the hierarchy is the network watershed condition what is the condition of the watershed. For the proposition that overall condition of the watershed is suitable for sustaining healthy populations of the native say if you consider say for the the living condition of the particular watershed then we consider the watershed condition whether it is deteriorated or it is very good condition or it is bad condition and then we can come up with various aspects say related to particular decisions to be made whether related to cropping batten, land use or soil erosion or whatever type of problem we are looking for. So, the watershed conditions are generally depends on two lower level networks like upland or overland conditions say as we discussed earlier watershed we can consider as overland and then the stream or channel net channel condition. So, whenever we consider the watershed condition. So, the two lower level networks other than the overall watershed condition say two levels which we have to consider is how is the overland condition and the stream conditions. So, accordingly we can consider various aspects of the problem which we consider and then we can come up with a solution or come up with a scenario. So, in all these aspects when we deal with say for example, watershed management. So, we have to consider as we discussed various criteria we have to consider in the analysis. So, that way we can see that say this type of problems are very much say multi criteria based. So, we have to do a multi criteria decision analysis. So, MCDA or multi criteria decision analysis very important in knowledge based models. So, simulation models of various hydrologic components of a watershed say for example, rainfall runoff or soil moisture or the water sharing all these kinds of components. So, then integrated with artificial intelligence tools like FACILOGIC. So, has to make use of experts knowledge and heuristic information in decision making process. So, this is used to help the end users to arrive at the best suitable decisions related to reaction management. Say for example, if the watershed is concerned if we are dealing with reaction management. So, we can consider various hydrologic components of that particular watershed and then we can come up with this is the best reaction management practice or this is the best cropping pattern or say depending upon the requirement of the crops this is the possible reaction management schedule like that. So, then reaction assessment and management as I mentioned. So, that way is a multi criteria problem and then we have to go for multi criteria decision analysis. So, in this we have to use the knowledge based systems. So, knowledge based systems is very useful for multi criterion decision analysis. So, MCDA or multi criteria decision analysis in which the land suitability say for example, if land suitability criteria is to be considered, water availability, irrigation requirements and various other criteria to be evaluated. So, generally the objective function can be or objective can be maximize the agricultural production same when we are looking for irrigation management for the particular watershed. So, say if you consider MCDA or multi criteria decision analysis we can say our objective or objective function can be we have to maximize the agricultural production. So, accordingly we can go for the irrigation management or the various scheduling. So, that way MCDA or multi criteria decision analysis models are used in irrigation management to identify areas that can be related and then water release during different time periods and then best suitable cropping pattern for the concerned area. So, like that when we deal with the MCDA multi criteria decision analysis, we can identify same the water release for different seasons or different time periods and then what is the best suitable cropping pattern and then what is the irrigation schedule. So, like that MCDA is very much part of any kinds of knowledge based systems which we can develop for say particular watershed management problem. So, now let us look into a particular say not based systems for watershed management when we consider. So, this flowchart shows a typical type of not based model for watershed management. So, say if you consider watershed or water related issues within the watershed then we have to go for hydrological modeling say for example, we can find out the rainfall runoff using soil conservation current number based model and say once the runoff is determined say this runoff also depends upon the soil moisture balance and then crop water requirement and then irrigation requirement. So, then based upon that once the runoff is predicted then we can identify how much water is available for that particular area and then if sufficient water is not available then we can think over how we can go for water harvesting. So, what is the water harvesting potential for that particular area and then say if you use say fuzzy membership approach say which we will be discussing in the coming slides what is fuzzy logic and all those things we will be discussing detail. So, if you consider fuzzy membership approach then we can use those approach to identify what will be whether the land particular area or land is suitable for that particular crop and then we will get a special temporal multi-criteria decision system or multi-criteria decision model for identifying the most suitable cropping zones for that particular area. So, based upon this approach one of my PhD student Reshmi Dev in 2008 developed a model in department of civil engineering IIT Bombay a specified knowledge based systems for watershed management and these results were published in the journal Reaction Rainage American Society of Civil Engineers. So, this way a typical knowledge based systems consists say various components for the particular problem we have to we may have to use sometimes some specified models say for example, rainfall terrain of model and then we have to consider say the say for example, land is suitability and then the water requirement and all those things we can combine together integrate together within a GIS environment and so, that that becomes a norm based model and that can be effectively utilized say for example, for the land use suitability analysis or the say most suitable cropping zone identification for that particular watershed like that. So, that way as I mentioned this fuzzy logic systems which is say used in many problems for the last 2 decades that can be effectively utilized in watershed management also. So, let us now look into important aspects of fuzzy logic and fuzzy based systems and then related modeling techniques. So, this fuzzy logic systems say it was first presented by Sarek say in the mid 1960s at the University of California at Berkeley and he developed the fuzzy logic as a way of processing data. So, by considering various problems say how to process this data say between various variation within the parameters. So, later on he introduced the idea of partial set membership. So, say if within the one say variation in the variation is say for example, good to bad. So, say if we cannot identify certain class is totally good or certain class is totally bad in between what happens say then that kind of say partial set membership this Dr. Sarek introduced and then he defines the fuzzy systems as say the system which is not clear or distinct or precise. So, a system which is not very clear or we cannot say that this is exactly this is the fashion or it is not so precise. So, that is kind of system we can call it as a fuzzy system and then he defined the fuzzy logic as a multi valued logic that allows intermediate values to be defined between conventionally evaluations like a true or false, yes or no, high or low etc. So, that way Sarek defined the fuzzy logic as an intermediate value or intermediate values between conventionally evaluations like say which is exactly like two fuzz between two and fuzz what is there or between high and low how is the variations. So, like that so the system which is not so clear or distinct or precise that is defined as the within the fuzzy say systems or fuzzy logic. So, actually that way fuzzy we are not actually dealing with probability. So, probability generally deals with uncertainty and likelihood of various parameters say for example, if rain may or may not happen. So, it is uncertainty of that particular parameters, but in fuzzy logic say fuzzy logic generally deals with the ambiguity and vagueness. So, whether say for example, if particular land is there that land if say some particular land we can specifically say it is not at all useful say for some party cultivation, but some land will be very suitable for party cultivation, but in between say if it is there then say how to identify. So, that kind of problems we can easily deal with the fuzzy logic based systems. So, now as I mentioned this fuzzy logic is say a system which we can utilize where vagueness or the we cannot exactly define what is the situation. So, that way the fuzzy logic is based on intuition and judgment. So, we have to see the what is the say for example, for the when we deal with particular problem what kind of intuition we are getting or what kind of judgment we are having. So, that way actually it is not based upon specified mathematical models say, but say we have to see that with our intuition and judgment we have to come up with say methodology in fuzzy logic. And then fuzzy logic provides a smooth transition between members and non-members. So, if the member between member and non-member means if the what is the decision is yes or no. So, what is there in between? So, that is we say that the transition between members and non-members or the between say high and low. So, what is there? So, that kind of transition. So, that way it is relatively simple and this fuzzy logic is relatively simple, fast and adaptive and then it is less sensitive to systems fluctuations and then say according to the problem we can define or design certain rules. So, that when it is a rule based operation we can define and then it can be implemented same for design objectives or like what is difficulty express mathematically in terms of linguistic or descriptive rules. So, mathematically if we cannot have precise type of rules or type of say definition for that particular problem then, but it may be able we can put it in terms of linguistic or descriptive rules. So, there fuzzy logic we can directly utilize. So, that way say if you consider for example conventional of this sets are binary, but fuzzy logic is say the variation in between is considered. So, now an element either belongs to the set or does not. So, generally crisp form the conventional set is say particular thing is concerned whether it belongs or it does not belong, but say like true and false. So, that way if we assign true is 0 then for false we can assign 1. So, like that, but in fuzzy logic for the type of problems where it is not possible to specifically say true or false, but something say in between. So, that means it may can vary from 0 to 1, but it may not be say exactly as 0 or 1 depending upon the problem. So, like that say if we consider the problem. So, say now let us see say here you can see that in this figure say this A, B, C these are all subsets of this particular problem. So, then if it is specified specifically the things are in this, then it is A or in this subset it is B or in this C, but in between if then what happens. So, that way fuzzy logic can be considered. So, now let us look what is fuzzy sets. So, fuzzy sets we have to consider a set of details within the for that particular problem. So, this allows elements to be partially in a set as I mentioned here this particular set which we consider. So, allows elements to be partially in a sets each element is given a degree of membership in a set. Then a membership function is the relationship between the values of an element and its degree of membership in a set. Say for example, the variation is say this particular function mu. So, then this is negative and this side is positive. So, then negative positive then or large, medium, small. So, in between that how the variations are taking place. So, that way we can consider for the particular problem which we consider. So, that way we can consider the fuzzy sets. So, now in this kinds of problems we have to consider the membership say whether it is in which subset or whether between those sets. So, like that we have to consider the membership functions. So, here the details of the membership functions are given in this slides. So, the memberships generally can be crisp membership functions. So, crisp membership functions say for example, this mu which we consider either 1 or 0. So, exactly 1 or 0. So, say for example, particular number greater than 10. So, that way we can define like this. But as far as fuzzy membership functions are concerned the membership value here is not exactly 1 or 0, but it is varying. So, the degree of truth of a statement can range between 0 and 1. And the linguistic variables are used for to describe this fuzzy measures what is happening. So, examples of fuzzy measures include say the particular say problem is closed say like a water body is close to the agriculture land or it is a medium heavy say it is a heavy light, big, small, smart, fast, slow, hot, cold, cold or tall short like that. So, on linguistic terms, linguistic variables we can use say between these parameters and then we can specify. So, that way in the fuzzy membership functions these values say it is not crisp like 0 or 1, but it will be varying between these parameters. So, now say we can see that to that way we need to define the fuzzy logic operations. So, say if we consider two sets say whether how that sets are behaving. So, accordingly we can say design the problem. So, now in the fuzzy logic operations say for example, we say that to the union say for example, if this is subset A sub set B then the union is maximum mu A x mu B x as shown in this figure. And then if you consider only the intersection. So, for example, minimum of mu A x mu B x. So, this is the intersection which we consider and then a compliment the negation of the specified membership function. So, we do not consider this area, but on the other two sides area if we consider then the compliment. So, like that fuzzy logic we can define particular fuzzy logic operations and then we can consider the particular problems which we are dealing. And now say this fuzzy logic as I mentioned say in most of the natural problems which we consider like say if we consider the watershed management. So, then the particular area is concerned crop suitability or irrigation management. So, like that various problems. So, we cannot specifically exactly say that is this is the way, but it can vary between say yes or no or false or right or say like that between the these parameters varies. So, that when fuzzy logic has got that number of applications say for example, in watershed management. Of course, this fuzzy logic was developed for various other types of problems. So, some of the applications I have listed here like a right smoothness control then in all other kinds of engineering like electrical engineering, electrons engineering, mechanical engineering like that. Then braking systems, high performance drives, air conditioning systems, digital image processing, washing machines, pattern recognition, remote sensing, video game, artificial intelligence, graphics controllers for automated policy, police sketches like that. So, large number of applications we can see now in literature related to fuzzy logic. Now, since our main industries here related to watershed management problems. So, watershed related application also large number of applications we can see in literature in modeling rainfall runoff processes, then erosive soil measurements and hydroecological modeling or watershed and flood forecasting and water quality problems, cropping and the reaction management. So, like that watershed related or watershed management related number of problems also we can utilize this fuzzy logic. Since many of the this natural problems related to watershed problems are very much say fuzzy in nature. So, that way we can utilize the this fuzzy logic or fuzzy sets or fuzzy based model for watershed management related problems. So, now let us look say this fuzzy logic the concepts we have now discussed. So, now let us look what are the advantages and limitations of fuzzy logic and say with respect to application what we have seen some there are certain advantages and some limitations. So, here some of the advantages are like allow it the fuzzy logic allows the use of vague linguistic terms in the rules. So, that based upon that we can say come up with certain decision for that particular problem and that way we do not need any specific the exact mathematical models for that particular problem. So, based upon the linguistic variations we can make decisions or make we can make modeling and these are rule based and a descriptive type. So, most of the fuzzy logic systems are rule based and then some of the limitations include this is difficult to estimate membership functions. So, most of the problems say what kind of membership is there accordingly it is exactly to define or to estimate the membership is difficult. There are many ways of interpreting fuzzy rules. So, say the term itself is fuzzy. So, say we can interpret also in different ways. So, we have to the correctness of the decision or the we will have better decisions or better say interpretation if we use the best possible kind of system and combining the outputs of several fuzzy rules and defusifying the outputs. So, we have to first see the facification of the systems and then based upon the rules or the say the in terms of linguistic systems and then we have to again come back and defusify the output. So, that way we had to go through a certain procedure. So, these are some of the limitations as far as fuzzy logic type systems are concerned. So, now let us look what are the important components as far as fuzzy logic is concerned. So, this slide shows the basic components of fuzzy logic systems. Say here first of all of course, data input is required based upon the available input only we consider the particular problems and then come up with a certain decision. So, first is the inputs. So, data inputs and then based upon the problem say we consider say certain types of models to facify the system. So, that is called the facification and then we consider the fuzzy rules the base rules applicable for that particular problems. So, based upon that we will be getting the fuzzy outputs and then since to the to normalize and to for the understanding of the problem again we have to do a defusification and then we will be getting the outputs. So, this way in a fuzzy system the basic there are five basic components and that way a systematic modeling or systematic operation operations are possible in a fuzzy based modeling. So, now let us look into that the three components for the facification, fuzzy base rule and defusification some of the important aspects let us look into. So, facification means it is a for a facifier, facifier converts a crispy input into fuzzy variables. Say for example, say if we consider the land use say for particular crop say it is not the possible crisp inputs are not suitable then suitable. So, in between we can have say less suitable moderate suitable less suitable. So, like that. So, that way this facifier converts each piece of input data to various degrees of membership. So, the membership function is a graphical representation as shown in this figure representation of the magnitude of participation. So, like the land suitability it is suitable or not suitable then in between we can have less suitable moderate suitable like that. Then the definition of the membership functions must reflects the designers knowledge and then provides smooth transition between member and a non-members of a fuzzy set. So, that way we have to do this facification. So, it should provide smooth transition between a member and non-members of the fuzzy set. Then typical shapes of the membership functions we can have a Gaussian variation say Gaussian variation like this or we can have a trapezoidal variation as shown here or we can have a simple triangular variation also. So, that way this kinds of variations we can use in the the facification processes. Then the second one is the fuzzy based rule. So, this fuzzy based rule is actually the important component in any of these kinds of modeling. So, this fuzzy based rule includes all possible fuzzy relationships between inputs and outputs. So, we have seen that inputs are there and then corresponding outputs will be there. So, this fuzzy relations we can generate relations based upon this inputs and outputs. So, these include all possible fuzzy relationships between these inputs and outputs. So, rules are expressed in the if then format that means, if this then say if this particular check dam is constructed then how much will be the flooding problem or how much is the area can be related. So, like that if this is done then what will happen? So, if then format then we can the rules reflect experts decisions. So, say this the whatever the rules which we generate based upon that the final decisions made. So, that way they should reflect the experts decisions and then rules are tabulated as fuzzy words. So, like say for example, if a particular person is there we can by looking to that particular person his actions or his say condition we can say that whether he is healthy then we can say that he is whether somewhat healthy, less healthy or unhealthy. So, this can be based upon say various conditions like whether he is how much tall or whether he is fat or he is thin or say his overall health conditions. So, say for example, we can generate a particular fuzzy based rules say for example, related to healthier unhealthy say healthy is concerned if height is tall and weight is medium then we say healthy. So, like that if then relationships we can form and say for example, if height is small and then weight is more than unhealthy. So, like that we can have say we can generate various fuzzy based rules. So, this is related to the health of a person, but say for example, related to the crop suitability particular area or land suitability we can consider various aspects like if what is available then a reaction can be done. So, that this particular crop say like a party is possible. So, like that we can generate the fuzzy based rules. So, that will be very useful in this fuzzy based modeling. So, that way as shown here we can have various say conditions say like a fuzzy based decision as shown in the slides. So, the fuzzy based decisions say we can give various weightages here and then function is shown here. So, this is related to unhealthy, less healthy, somewhat healthy or healthy. So, like that. So, most of this fuzzy based rules are based upon if this is the condition then what is the situation. So, like that. So, then theoretical component is the defusification. So, once the input is there and then classification is done and then fuzzy based upon the fuzzy rules say we are now going getting a output which is in the not understandable for a normal person. So, we have to defusify the system. So, defusification as shown in this here converts the resulting fuzzy outputs from the fuzzy inference engine to a number. So, generally in compute terms it will be represent terms of a number and this number will be showing how the variation is taking place whether it can be between 0 to 1 or whatever it is. Then converting the output fuzzy variable into a unique number. So, that unique number represents say whether that person is healthy, less healthy, somewhat healthy or that particular area is suitable less suitable or more suitable. So, like that. So, number of defusification methods are available in literature like a weighted average conditions, maximum membership, average maximum membership or center of gravity or that particular say in the area triangular or trapezoidal like that variation. So, these different methods are there anyway. We are not going to the details of this, but the defusification is required when we use the fuzzy logic and that gives the particular output the system which we are looking for that particular problem. So, that way defusification is also very important. So, now what we are discussing is the fuzzy logic and then say the structure of a fuzzy logic systems and then how the fuzzy logic is working and then its applications that is what we are discussing so far. So, now today is our main topic is knowledge based model. So, related to say watershed management, how we can develop a model based knowledge based knowledge based model say related to watershed management. So, that way now further in the coming few slides we will discuss a particular knowledge based model for related to watershed management. So, let us look into this particular model developed by my PhD student Deshmi Devi and presented in her thesis not based model for supplementary regression assessment in agricultural watershed. So, here say she developed a fuzzy rule based inference system for land suitability evaluation. So, if a particular land is a particular watershed say for particular crops say how effectively that is say suitable less suitable or more suitable like that and then she developed a spatio-temporal multi criteria decision analysis model for SMCDA model for identifying the scope for supplementary regression. So, based upon this fuzzy rule based system she developed a model to identify whether if we consider the particular watershed and then its cropping bat and its irrigation availability and so, whether we have to go for supplementary regression and then how effectively we can do within the contest of a knowledge based model. So, that way model has been developed and then also she developed a graphically viscer interface for this particular model. So, in this particular model for this knowledge based model 5 steps there in the model development first one is the classification of the attributes, then estimation of the intermediate land suitability index, then generation of the fuzzy rule based, then aggregation of the rules like a fuzzy output in terms of high suitability classes like less suitable suitable more suitable like that and then defusification. So, the basic steps, specification estimation intermediate land suitability index, then generation of fuzzy rules and aggregation of the rules and defusification. So, these were the essential steps for this particular model then fuzzy rule based inference system. So, here the problem with a large number of attributes say for example, if a particular watershed or particular area whether we have we want to identify whether that particular area is suitable for particular cropping. So, we have to consider various aspects like the for the particular average number of drive days, proximity of water body. So, this is related to water related issues then elevation with respect to the nearest water body then land related issues like land use, soil texture, terrain slope, soil depth, drainage density, denium density, then soil related issues like pH, then electrical conductivity, salinity, etcetera, then climate related like rainfall, temperature, proximity to roads. So, then based upon this a fuzzy operations related to water related issues which gives the water potential and then from this the land use related issues then the weighted aggregation related to soil, then later terrain and weighted aggregation related to the soil gives the fertility then various other attributes also can be considered. So, now based upon this fuzzy rule based the inference system has been developed and so, this is the overall structure of the model and then defacification and then from that we can get the particular the decision of particular suitability. So, the here she consider hierarchy classification and then she consider both land potential and water potential for that particular problem. So, the main issues are related to land potential water potential so, that a suitable crop in crop index or crop suitability or land suitability for that particular crop can be identified. So, this system was built upon the fuzzy rule based inference system. So, as far as specifications concerned as we discussed earlier the attribute values are mapped between 0 and 1 and then two types of attributes like a thematic attributes for land potential, unique membership value for each class then continuously expressed attributes for land potential, semantic import membership function, then asymmetric left or asymmetric right or optimal range. So, more details of this you can see in the general irrigation drainage paper published so, the reference will be given later by us. Then the next step is the intermediate land suitability index. So, here the weighted aggregation of the attribute membership values are used and attributes like here she used Sati's relative importance scales to identify the intermediate land suitability index and then based upon that relative importance is assumed based on literature field observation and heuristic information and then this gives intermediate land suitability index in three suitability classes like good, moderate and not suitable based on the land and the water potential. So, these are the details as far as the intermediate land suitability index in this particular north based model. Then the fussy rule base and aggregation of the rules are generated. So, suitability criteria is based upon in the form of if then rules in terms of intermediate suitability indexes like if land use is good and water potential is good and terrain is good and physical chemical characters is good and then other parameters are good then the area is excellent. So, like that the system is made and then another scenario if land use is good and water potential is moderate and terrain is moderate then and physical chemical characters is moderate and other parameters are moderate then the area is moderate. And the third one if land use is not suitable and water potential is not suitable and terrain is suitable and physical chemical characters is not suitable and the parameters are not suitable then the area is not suitable. So, like that if then rules fussy rule base were generated. So, this we generated the fussy output in terms of 5 suitability classes for this model like excellence whether the area is excellent, good, moderate, less suitable and not suitable. And then next step is defacification as I discussed earlier. So, this convert the fussy output into a single value land suitability index. So, here maximum centroid method is considered. So, as shown in this figure. So, now based upon this say using this model we can generate the best suitable crop map. So, relative importance and land suitability index is given. So, three cases case one is land suitability index of existing crop. So, that is less than another crop of higher priority. So, then higher priority crop is selected. So, this is one case. Then case two is a land suitability index. So, the existing crop is less than another crop of lesser priority then change in the cropping pattern if less suitability or not suitable for the existing crop. Then third case is land suitability index is same for more than one potential crop if less suitable or not suitable for the existing crop. And if relative importance of the existing crop then the and the other crop then a change in the cropping pattern is proposed. And then it replaces the existing crop with a higher priority one. So, like that various systems were made in this particular models. So, this model the details are given in a flowchart. This is spatial temporal multicreative decision analysis model for irrigation feasibility analysis. So, here first we have to assess the irrigation requirement and runoff availability. Then runoff versus irrigation requirement. So, this gives the water deficit periods. Then land suitability it is coming from the foresee logic. So, runoff versus priority based irrigation requirement. Then you regularly we can identify and then outsource water requirement for different suitability classes whether we have to go for the further the supplementary irrigation like that we can decide using this model. So, these details of this models are available by in this in the paper published in 2010 titled knowledge based model for supplementary irrigation assessment in agriculture watersheds general of irrigation drainage ASC 2010 volume 136 pages 376 to 382. So, let us have a brief look into one case study related to this model which is done by Dashmidev in her thesis. So, location is the harshal water shed and the area is 10.9 square kilometer and the principal crops in this area are padi and finger millets. So, this is the water shed area. So, here based upon the various input data various database were generated like a heuristic information and field observation related attributes, attributes suitability for different crops, crop priority and agriculture practices, land suitability criteria. So, this is done in the heuristic informations and then map layers related to drainage map, contour map, then a soil map, pH map, map showing spatial variation, electrical conductive salinity etcetera were generated and then also land use map, drainage density map, then proximity to water body, proximity to settlement. So, all these details were generated in the database. So, that to this fuzzy based system modeling can be done and then hydrometeorological data related rain, fire, stream, flow, temperature, relative humidity, sunshine, duration, wind speed, all these were collected and then using the earlier described north based model the modeling has been done and so the for the say land suitability related. So, for this water shed for example, the SCSCN soil moisture simulation model has been used for the runoff generation for the given conditions. So, this shows the outputs and then say for example, year 2002 which is a dry year we can identify how much is the water available say and then say for paddy field or finger mill how much is the water requirements and then this is the rainfall hydrograph and then we can identify how much is the irrigation requirement and then accumulation like that. So, that is this all generated using the particular model and then say irrigation requirement the household water shed and say for example, 4 years 2001, 2002, 2003, 2004 were generated. So, irrigation requirement non-agricultural area then finger mill at irrigation then paddy field how say with 50 mm requirement 100 mm requirement or 150 mm requirement and then based upon that a best suitable cropping zones in household water shed has been generated. So, this yellow shows the area suitable for paddy field and then this red shows non-agricultural area and this violet this shows the finger millet suitable and non-suitable for paddy or finger millet. So, this way we can generate the best suitable cropping zones. So, this shows the land suitability for paddy land suitability for finger millet. So, the suitable classes and range of land suitable index is given here say percentage area wise related to paddy and finger millet. So, that way we can generate using this not based model we can generate identify the land suitability for particular crop and then say which of the area we are most suitable or more less suitable so like that we can identify. So, now to finally to conclude say many decision making problem solving tasks are too easy to solve. So, this facility can be used for this purpose and not the best model shows the irrigation requirement for the predicted rainfall and predicted rainfall helps to choose adopt appropriate crops and the irrigation management plans for the given area. So, these are some of the important references used in for today's lecture. So, then before closing some tutorial questions critically study the applications of north based systems for various water resource management problems study various case studies available literature. So, these details you can obtain from the internet study the role of north based modeling in integrated water resource management. So, how we can effectively utilize a north based model then as some self evaluation questions describe the features of typical north based models illustrate the requirements of north based systems, describe typical north based systems for watershed management, illustrate the fuzzy logic operators used in typical fuzzy logic what are the important components of fuzzy logic systems. So, these all these questions you can answer by going through today's lecture. So, if we assignment questions describe the structure of a north based systems what are the important features of multi criteria decision analysis illustrate the features of fuzzy logic based systems describe applications advantages and limitations of fuzzy logic illustrate a typical north based model for watershed management. So, all these questions you can answer by going through today's lecture. And one unsolved problem critically study the typical north based model for a for the water and land management in the watershed. So, for your watershed area study the scope of development of north based model considering rainfall, various crops, land use, land suitability, water requirements etc. So, today we considered the north based systems for watershed management we discussed the fuzzy logic systems and then connecting to that how we can generate north based models. So, that way we can see that this north based models are very useful in watershed management. So, with this lecture then say the particular module on the modern systems for watershed management say module number 6 is over. So, now we will discuss various other aspects of watershed management issues. Thank you.