 Gwydodd wedi bod a'i gwneud yr hyffordd. D ranche, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd, ddodd. Felly ddodd, ddodd, ddodd, ddodd, ddodd. Dyma, hynny'n mynd i'n cael ei gwirio'r cyfle i fynd i ysgol, mayfwrdd, ond pan oedd ei ddiwylliant ym hyfforddau o'r cyfle i fynd i gwynt llunas. Ychydig i chi bach mewn ar y botwng hyfforddiadol yma. Mae'n gweineb yn bach, fel i'ch ddweud. Felly, we'n gwein iawn i gyhoeddwch o'r gwahodd yna fel ei oed. Dwi'n gwneud hynny'n gweld yma? Dweud 150 miliwn o anhygotu y Llywodraeth ac yma ond a'r holl o ffloeddiadol. Yna'n ffordd o'r holl o'r holl o'r holl o fenngwydol, o'r fath o'r pethau-draen, oherwydd yna mynd i ddweud yma'r rywbeth ac yn ghaen thanksaeth y fathau-draen. Mae cymoedd yma'r pethau o'r pethau-draen. Mae'n cyhoeddiad yma, lle mae'n cyhoeddiad eraill pethau. Mae'r cyhoeddiad ar ôl yn gyfreithio'n gefnogiad, ond mewn gweld na llwyddo Panel o ran o bobl yn ysgrifennu'n gwybod, ond nefyd oedd ar gyfer ei fathau i gael. Rydych chi ddewid, rydych chi i bwysig, roedd ymateb o'r senzio mewn i gyd yn ddechrau gyda'r awr. Mae'n ddiddordeb bod yma yma yn y typach hefyd, ac yn yr un o'r tyn nhw, mae'n gyn ni'n dechrau'r senzi yn mennyn, ac oeddwch chi'n ddiddordeb o'r nifer o'r senzi yn diogel. Yn ymdegwyd yma, mae'n wneud y gwneud yw'r cyfnodol gyda'r busg mae'n gwneud yn gyfnodol. Felly byddwn yn ei wneud i'w sengharu'n gympredigol i'w hwnnw i'w meddwl i'r rhan o'r ddechrau, ac mae'n ei ddigonu'n gwneud ychydigol i'w ymddangodd arlaed. Felly mae'r ddigonu'n gwneud i ddweud ar gyfer gyfer gyfer gyfer gyfnodol, ac mae'n gweithio'n gweithio. Felly yw'r gweithio'n gwneud. Mae unigwyd ddigonwch yn ysgololol yn ysgolol fod yw'r unrhyw ddymaru sydd wedi'i rhan o'r bydd y nôl yn gyfnod. Mae'r ysgololol yn gyfnod yn ysgolol. Mae'n mynd i gael y byddwn yn gwych yn ddysgwch. Mae'n byddio'n meddwl o'r ffwrdd. Mae'n rhaid i'r bobl yn brifioedd ysgolol. Mae'n meddwl i'r bobl yn meddwl o'r bobl yn meddwl o'r bwysigol. Mae'n gwirionedd, yn bucynno, yn 4,000 perth. Mae'n gŵr yn ystafell hwn o bachoddau i'r cyfle sy'n dod arlaethau arno gwypwyr o'r ffordd. Mae'n hoff ychaw feld ychwanegau ac mae'n gwirionedd yma, ac y gallwn i'n gwirionedd. Dwi'n cael ei ddweud ffasien i'r prif edrygu'r ffordd, ac mae'n ffasien i ddwy fyddiol, ond that enables us to actually measure the speed of ways as well. We also use some two wire systems and some one wire systems just for portability and then they just measure it, they measure it, they measure it through the city of Dax. Of what they think it's talking about. Next slide please. So we did two demonstrations during the pre-teach projects, one in the PDPENs acts, and one in the doorless. Now, the slight endoas is really significant is after 2014, this is where the semen collapsed, the railway line collapsed due to storm vents, due to crystal erosion and so on. So, our concurrent sensors that we have actually been endoas, we had almost as Sentinel ddiogelıygaeth arfer ac sydd yn gynll pride i collu ofiwys jam yn seym nirydd y iawn. Ymarferfyd, dyna hi ddim yn Neptune. Rwydy rhai yr un hay royalraedd yn iam y c border, a yna'r berthynas hon. Mae These were deployed alongside a beach profile laser sensor as well, which was able to take the profile of the beach, particularly at low tide. Beach level has significant impact on what we would have done. We have now that mortar, and then in addition to that, we were also taking lots of information from established sensor monitoring networks, i ni i sut yw o'i hyffordd iddyn nhw gyda fiy ysgwrdd o'u numerol i gyn�chiadol, i'r gyferwyr cyngor o'r發ol sy'n gweithio cyllid yn gwneud a'i gweithio benwyd, byddwn i'n mor oedd i'r cyllid i gyd, a'r cyngor, i gael, a'r cyngor, a'r cyfan neu yn gyffordd. Diolch i ni gael! Mae'r ffordd deoedd, mae gennym i'n ddarparu i'r gyfyrdd yng Nghymir. A hefyd, y ffordd yng Nghymru yn ymwylo'r gwaith yn meddyl iawn, yn ymwneud am ffwrdd yn ymwneud. Felly, hefyd yn fwy o'r unrhyw yng Nghymru yn ysgolion L-Model, sydd yma'r ddwyf yn cael ei bod yn cael ei werthfawr ar y ddechrau. Nid yw'r unrhyw ymwneud. Felly, yn imbryd y model LL i gael gweithio'r meddylion o'r profi-sgwyl. Yn hyn ymlaen i'r Sefais, mae hynny yn y prifrogrwm. Dwi'n rhoi'n ddifurio'r profi-sgwyl. Mae'r profi-sgwyl wedi'u meddylion o'r profi-sgwyl wedi'u meddylion o'r profi-sgwyl wedi'u meddylion o'r profi-sgwyl wedi'u meddylion. The more you look at the B scan data between, you can see with the beach grid that the outcome is like. The last slide, so just to illustrate this point and the impact that might actually have on an issue of the universe has a Ysgrifwyr yw yw'r data ar maes 2022. Y model yw'r model yw'r model yw'r model yw'r model yw'r model yw'r model yw'r newydd. Next slide please. So one of the things we wanted to do is to see if we could actually start issuing hazard alerts now casting this wave of topping data since we now have an institute centre that can do this. So we actually turned this rather large centre into an internet things centre. So we essentially connected it to the internet. We got it to send its data in a similar. We dropped it directly into an API. Then from there you can access the data in a multitude of formats. This is all publicly available. Do you want to click on? I think there might be something else that I'm doing. Yes, what about that? In addition to this, we wanted to actually bring in lots of of the environment conditions that are happening at the same time so that we can have all this information in one place. So we've got data from the Environment Agency, Metacross and Chang Coastal Observatory. Next slide please. So now we've got all this data into an API. We essentially decided to have a way of demonstrating with a public interface where we could actually issue those hazards with alerts. So we built a little dashboard for showing the way that we're topping data. We used a traffic light system to help the public understand what might be hazardous about this new type of data and we combined that with all the environmental data that I was collecting in the API. What's interesting, if we compare the way they're topping sensors, the sea wall edge now and at the railway wall, you can see that quite a significant amount of way of topping actually not only reaches the front of the sea wall but it actually reaches the railway line as well. Next slide please. So we started looking at what kind of conditions, environmental conditions might actually affect what they're topping. In this particular illustration, we've got the different environmental conditioners who work and we've plotted their distribution against the number of way they're topping way so that we have measures during our deployment. The top graphs at sea wall edge and the bottom graph is the railway wall and throughout Hultoplawn we found that there was about 35% of the way the topping way has actually made it to the railway level. Next slide please. So just some initial analysis. Initially here we're looking at the water level at the top we've got the sea wall edge and then we've got the railway wall below. We found that the maximum mean water level in the Gordon Jr deployment were 2.61 can 0.26 meters on this data but actually the most frequency of they've been topping is 1 meter so this was actually above the mean but below the mass. We also found that some swell events, a bit of bi-modal wave action that happens in the George area and we actually found that some swell wave events actually also forms way more topping at much much lower water levels. Next slide please. Here we've got wave height. The channel coastal observatory officially known as the storm wave threshold for Dondlish is 2.77 meters but all our waves of over-topping before the beginning of this time were actually below this shown. They are at a maximum of 2.16 meters and again we've got swell wave events that were causing over-topping waves even at low water levels. Next slide please. Read on the gift. Read on to. I think it's just one should just be one. Yep, there we go. So how's our sense of getting on against the prediction models in wave over-topping? So we did this comparison on the 7th and 8th march 2022 and we can see the black line or the black dots are actually our prediction model and the red dots are actually from our wave over-topping sensor. You can see we started to get some discrepancies and we're starting to get some discrepancies that also change the hazard level that could essentially be issued to the public. And I guess the thing is, is this true? Well luckily we had a camera there so we were actually able to validate that our sensor was actually recording the correct type of wave over-topping. So for example, in middle of this graph here we start to see some wave over-topping that means the density of the hazard level and we can see from the camera that yes, we're getting lots of wave over-topping that's impressed. Moving on, this is the day we see that we're actually getting this not a white out essentially. It's just going straight over onto the lower line as well and we're actually getting hazard thresholds that are considered dangerous for transport. Next slide please. So just initial thought about what might actually be causing this discrepancy between the model and the in-situ measures. What we did notice is that due to these two high periods of over-topping that the wave had significantly increased, but we also found that the waves turned directly on shore. We know that a lot of particularly modernity states don't actually include measurements for wind. Next slide. So just a summary of the initial analysis, this is all still very new data. So in one year of telemetrics observations, over 12,000 waves were measured at the Dornish Seymour. It's interesting to point out here that the actual sensor that we're using was only measuring three hours either side, but highly tight. So there may be over-topping events that actually did happen at other times, but unfortunately we didn't measure those. 35% of the waves reached the railway line wall and were considered hazardous. And then finally, Jenny's actually trying to put us out here. Three different bands of over-topping intensities, but low, medium and high. She's tried to plot those against the water level and the speed and weight and height. See if there's anything that's really standing out and what it is. I think it just highlights how complex this process is. And so you may need to see when we're going to do this game, maybe better approaches to try and tease out those processes that were causing the water level to top. Next slide, please. And I'd like to finish there. If you'd like to need any more information. Thank you. I think just before we go to the break, I think it's one question that sort of covers a lot here, Steve. This is clearly quite an intensive system to set up, and there's like a critical need here in training possible. Is it something where there are lots of places around the country where there's a need for this kind of system? Is this something that can be scaled up, or is this like so expensive that it's only going to be where there's trains, but why not? That's a good question. It's very science specific, but I think what we need to do is actually develop digital infrastructure so that we can go out, plug and play those senses, even though it's particularly in the science, to be able to help those communities understand the hazards of where you're talking. In my mind, I had this idea that you would have an app, and it would be like, don't go out now, and then I'd say, you know what is probably obvious to the people in English don't go out right now. That's true, actually. I think one of the things that we can do is try and channel all of this information into larger digital infrastructure and data infrastructure slightly. We can take advantage of the new sort of developments like data commons, for example. We can actually start mixing that data together in places, but also UK-wide. We can actually start mixing that data together with more social information, for example, all natural capital. From there, we can then actually build those apps to say, don't go out. Your train is delayed for a good reason. I've been along that line. It's quite scary. Okay, so we have quite a few questions.