Download Subtitles for Machine Learning Predicts Floods and Landslides [2024] | AI Project

hello everyone welcome to I expert today we are going to see one of the interesting project which is named as flood prediction using machine learning this is the very Innovative project which could save millions of lives in India Flint prediction not only a technology blood prediction belongs to people it's more about Hope communities and future that's what we are going to we are not only building a technology using machine we are building hope for the peoples so with the help of copes we can save millions of lives in future so that this project will be more important for the current Society stand last are we lost 5 12 people lives due to the flood so this so you can see the importance of this project okay FL prediction is not a easy task you should analyze various amount of datas for example weather report River levels satellite images historical plate these are the datas you should consider but these datas are huge in real time but than to the power of Miss with the help of this machine learning Technique we can analyze these datas very easily how we have to analyze these datas so this project works on machine learning in this project we are using conversional neural network as a trained algorithm you have to give various data to train for example you have to give historical data as a historical rainfall data current rainfall data then Dam levels as well as satellite images these are the datas you have to consider to give as a input while training these datas will called as a data set what CNN will do means it will take relative features from the data set which you are giving it will take while the FL happening in the existing system it will take what type of features what type of serious correlation will happen for example if the dam level was in this level if the rain will happen in this level Maybe the flood will happen this correlation factor that will consider the CNN will consider this correlation Factor this relation factor from the DAT to then what it will do means it will Max the correlation factor with the current real now what's the dam level now what is the satellite imaginary value now what is the rainfall value it will compare this current level values with the previous data set value then it will find the relative propag how much propagation of flood will happen how much propagation of landslide will happen that's what this D will do the main accuracy of main advantage of CNN here was it having 97% accuracy which is huge in real time okay you can predict the flood exactly when the flood will happen where the flood will happen how much amount of flood will happen this thing you will predict very easily the landslide also you can consider from the blood level so this is the project please connect with this video we will explain complete demo of the project with presentation with coding and everything thank you uh let me explain the project base paper as well as presentation this is our project base paper this is it based base paper okay the title as spal temporal flenders are mapping using integration of telemetry data and prediction model this is the project base paper we are considered for this project in the project base paper they have taken lstm algorithm they have taken lstm long shortterm memory this is the algorithm they have used to train as well as testing this project the main drawback of this project was they achieved very low accuracy as you can see they achieved 74% accuracy the main drawback was this one only 74% achieve accuracy only they achieved for this project the architecture of the existing system shown here they have considered water level as well as hourly water level as well as daily water level these datas are trained under temporal prediction model that is called lsdm based on the gque they have estimated the FL reject this is completely existing system very low in accuracy to overcome this drawback only we are going for our propos system so this is our propos system BPT as you can see the title was flood and Landslide prediction using machine learning we have created flood as well as Landslide prediction what the model we have created for this project the abstract we have mentioned that natural disaster such as Landslide as well as blood are posed to significant challenges in human regions okay this stud present uh Innovative machine learning approach to predict this Landslide as well as flood insecurity in suspectable area this is the obstruct part of project in this project we are using historical weather patterns Dam levels land usage what is the weather data socio Eon socio economic indicators to predict the how much loss will happen Okay how much wealth loss will happen we are using this five input in our data set to trade okay in introduction we are given that how frequently how severely this natural disaster uh raised to significant concert okay so we have to make sure proposed algorithm able to predict this flood as well as lands slate very efficiently that is the major Moto of this project okay why because India contains uh India having more number of human lives as well as India having more number of forest when compared to other regions so the flood as well as landfall prediction last Light prediction is very important for India okay the role of machine learning here was the predictive approach in this project we are using predictive approach no for prediction means you have to apply huge accuracy High higher number of accuracy for that purpose only we are using machine learning here okay it don't use any data mining and all it uses pure machine learning approach the main focus of this project was two things flood prediction as well as last Light prediction these are the main two focus of this project the existing system existing system uses three models hydrological models hydrological models means the base paper say water level they are solely used water level to predict the flood but we are using some Advanced consideration okay the hydrological model remote sensing some person using satellite maps to predict the water flow at all okay and rainfall prediction these are the existing methodology used in our project but the main drag B was Data scarcity data scarcity means you can't get exact data for all the region in existing system but we are using proposed API here the proposed API capable capable of getting all the data for all the regions okay we are using AP here let me discuss everything on the project demo section computational complexity the lstm pr to complexity okay it can't handle all the datas the main drawback of the LST was computational complexity then model interoperability model interoperability means the Deep learning things was difficult to interpret making challenge to understand how the prediction of uh predictions are made okay you can't understand the data clearly that is the main drawback of the exiting system these drawbacks are overcome by our propose system in our propose system we are using we are going to predict flood as well as last SL both the things we have to predict okay this system integrate satellite imaginary rainfall pattern then topographic patterns as well as soil moisture level R flow levels these factors are considered to to predict the flood as well as landfall Direction so that this accuracy of the project was huge when compared to the existing system the main algorithm used in this project was conversion neural network this only achieved the 97% of accuracy okay as well as uh the proposed conversion neural network model can be continuously trained as well as upgraded okay over the time to improve the accuracy so this system also include the realtime monitoring as well as propos to prediction model what the things included in this project this is the overall architecture of this project in the architecture as I mentioned earlier I'm using historical data sensor data means what's the current temperature what's the current rainfall rate as well as current water level of the particular dams these are the sensor data then weather forecast weather forecast means predictable forecast what the maybe a rain will come tomorrow or not what's the expected uh range of temperature for tomorrow these are the data called as weather forecast then hydrological data means as I mentioned here what's the dam level how much amount of water the L Dam can survey these are the data called as hydrological data these datas are collected and these data are pre-processed then we made final data set from the data set we made feature engineering selected features can be pred from the data set that model that features can be trained under the particular CNN model okay that CNN will train particular feature it store on the particular model okay then we are using flask model to validate this result we are building particular website in the website we can give you can give the input particular area what type of what are the area you have to consider you can give the area name then what our project will do means it will fetch the realtime data from the for the particular place this real time datas are always compared to the already pre-ra model based on the pre-ra model probability it will give whether you are having flood alert or not okay these things are accessed by the user interface as I mentioned earlier by using flask we made uh beautiful website for this project uh these are the four models involved in the project data collection model pre-processing model then model selection then predicting model these are the four stems involved in this project okay models uh this is the sequence diagrams of the project class diagram activity diagram use case diagram all the diagrams given for this project if you're purchasing means you can access all the things for free okay uh you can see the result okay the proposed model having accuracy of 97% as I mentioned ear existing model having accuracy of 71% the pr rate was PR rate of propos system was 97 as well as exitting system having press rate of 70 only the proposed system having uh F1 score of 99 exit having F score of 81 so you can see the accuracy of our project which is improved by huge margin this is a hardware requirement of the project you need minimum I3 process to run this project okay in this project can be run on Windows as well as Mac OS we need python to run this project in conclusion we have given that this is the interesting project with the help of CNN we have improved to improv the prediction model of flood as well as Landslide it poses significant advancement when compared to the base paper it we are accessing vast amount of data set to make the accuracy as well as the data set contains historical weather pattern topography as well as oil condition we are using machine learning model that's called CNN to apply the predictive foress reference for this project okay uh please connect to this video for demon sessions let's move on to the project demo so this is the pro uh this is the project code folder you can find the complete coding here this is already pre-trained model this is our python code which you are using this is the training code which are used to predict the project then this is the template folder the template folder we have used frontend HTML pages okay to run this project you have to copy the project code location then open Ana Navigator just click python terminal once you open the python terminal means use CD space paste the project location then enter to run the main project you have to type pythons space app.py this is the command used to run the main project so once you type the python space app.py means it start run the project so it's executed now so this is your local host address just copy the local host address paste it on the browser so this is our project homepage this website running from our terminal okay this terminal on generated this website with the help of these codes okay so you can find the complete project on the Local Host to start this project this is our homepage we named our project as a flood guard flood guard powered by AA this is our project code folder sorry this is our project homepage we have created some homepage about our project about us everything you have mentioned then what are the services we are providing means plot heat Maps satellite images predict flood these are the services included in this project so then why this project important means this project will help on the first date rescue operation also this project we can estimate to predict how much food supply uh person will demand or while flood happening so this is our contactor form then some frequently Asked question also we have displayed this website completely running with the help of python okay this website completely running with the help of python the first step which are going was plots in the plots you can see the complete flood areas okay flood prediction what are the places what are the places will be flood prawn in India you can see the complete results okay can see the complete results in the red colored things referred to flood occurring PE and flood occurring places okay the number belongs to latitude and longitude position this website this graph automatically generated with help of our prediction you can zoom also if you are zooming here means you can get the clear idea for example Mumbai PR to flood then also you can get Chennai Chennai also PR to flood then Ki then tanur these are the places PR flood these are the places okay then the second graph belongs to how much flood will happen in the each and every place okay how much flood will happen on the each place this graph completely Dynamic graph will be generated based on our prediction process okay you can see the flood intensity this is flood intensity graph you can see this is these are the places flood may happen in the huge data okay this is the fled intensity graph okay and this is the flood intensity graph then the third graph will be how much rain will happen on the each and every places how much rain will happen on the each and every place you can see the rain plot here okay these are this also will be generated based on our plot okay prediction plot then go for the heat Maps heat Maps directly related to our satellite so this is the heat map heat map belongs to how much loss will happen in terms of US dollars so if fled happened means how much loss will happen in terms of you uh US Dollars this also very much important why because if the flood happening in Forest places mean there is no huge loss for few months but if the flood happening on the Metro City means the loss ratio will be higher the heat map will uh uh help us to find out how much loss how much money loss will happen in terms of flood as well as rainfall okay you can see if the flood happening on the New Delhi means this is the New Delhi surrounding if the flood happening on the New Delhi surrounding means the loss ratio will be huge also the flood will happening on the Mumbai side means the loss will be huge why because these areas are surrounded by various number of peoples okay huge population that's why it it will make huge loss okay that's Al important also in hit M you can get Chennai PR to less loss when compared to Delhi why because Chennai population count is different as well as Chennai architecture count is Chennai architecture style is different okay as well as Chennai CV policy also different so we compared to Chennai Delhi will meet huge loss for the same amount of uh rain okay then partiti participation also refer to the PO population ratio and loss ratio here also you can find out Deni Mumbai then this place West Bengal these places are PR to flood as well as huge loss if some rain will happening means okay so this also key map then directly I can go for the satellite these graphs are completely generated by our prediction process these graphs are completely Dynamic okay it will change automatically so for example if you're going for D in July Monthan June month means you can see the rain data here in rain data these are the data rainfall data from Deli if the same thing I'm turning into July means you all know in July three three or 4% died on uh Delhi you can see the complete graph compared to June you got huge rainfall in Delhi surrounding also you can change the city here for example I taken Mumbai in May I have taken we can see see the result May Mumbai got low rainfall so I can turn it in July for Mumbai so compared to May in July month Mumbai got huge rainfall this is the cloud ratio from where clouds are gathered near to Mumbai okay I can change into Chennai also these are the Metro City I can change so Chennai got less amount of rainfall compared to other Metro City this is the important page in our project prediction page in prediction page you can type any city in the world you can type any city in the world what this project will do means it will fetch the lude and longitude portion of the uh particular City it will get the weather data from this AP visual Crossing AP this project Real Time Project it you can check this project any date anything okay it will work perfectly this is the AP we have used for used to collect the data real time data for example I'm using chenai here means it will give the chenai results okay this is the chenai result okay chenai temperature uh rain days what's the wind speed and all you can get forecast for 15 days uh 40 days everything you get yesterday today so what our project will do means in our project we to get complete graphs from this website using this graph it will perform the machine learning operation it will perform the machine learning operation for the current data live data it will collect live data from this website based on the live data it will predict whether the rainfall or the last may happen it will predict that I'm just typing chenai here so before that you can check the command box if I'm typing chenai here means what are my what my project will do means it will get the latitude longitude portion of CH you can get see the things latitude longitude portion of Chennai directly this L to longitude connect with this website it will get the real time data based on the realtime data it will perform the prediction operation see the thing so chenai belongs to Safe category according to our ml model we did not detect any sign of potential flood so in chai you don't find any flood data okay so you can check the results also in chenai the average temperature will be 85 par maximum temperature will be 93 par what's the wind be in J we are getting 30 this is today results okay uh you wind speed 13. 78 mph as well as what's the cloud coverage 88% of cloud coverage is there okay what's the humility ratio 72 these results are belong to chinai chinai did not find any flood things okay any potential flood related things what I'm doing means check the today date I'm just going for the Delhi you can type Delhi here so for Delhi also you can get the latitude longitude so after getting the latitude longitude it will collect all the real time data with the help of API it will get collect all the real time data satellite maps and all then it will predict how much maybe FL happening or not you can get the result for Del unsafe according to our ml model this area has potential risk of flooding within next 15 days potential risk of landslide Also may happen please take emergency measures this result for Del okay depend upon the city the results may change belongs to our machine learning model you can get temperature results per daily maximum temperature minimum temperature as well as wind speed cloud coverage then how much M uh uh participants means the people R for people ratio then how much humidity happening okay sorry I mispronounced that precipitation precipitation how much percipitation will happen all the ratios will get real time okay with the help of this API after getting this API after getting this data it will perform the machine learning operation then only it will give the result what I'm going to do means I'm going to do for the uh Mumbai Mumbai I'm giving results so if I'm typing Mumbai means so after typing Mumbai you can see the results go for the things so this is our Mumbai latitude longitude of Mumbai it will get the all the datas from the API then it will perform the machine learning operation Mumbai FL prediction safe okay according to our ml model we did not find any sign of potential flare what's the Mumbai temperature maximum Wing speed precipitation value everything you can see here then what I'm going to use means I'm going to enter Hyderabad also here so I'm going to use hydrabad I'm going to take kraat so you can get the result for Hyderabad also in our API so we typed hyat this is the geological position of Hyderabad then you can see the result so safe usually in Hyderabad flight may may not happen so we got the results also so we did not find out any potential flood okay this is Hyderabad result this project not only belongs to Indian city or Indian State you can get results for any city in the world so I'm using London here so London New York you can type any City it will work okay I'm just typing London so after typing London so let me check it got London or not okay you can see response London latitude longitude so it got result for London also safe according to our M model we did not sign any potential flood so if we have checked I Delhi only having potential risk of flood for today date for any date this project will work perfectly okay so London maximum temperature average temperature wind speed cloud coverage then precipitation value humidity value so these are the the results for London okay this is completely end to end project with the help of machine learning there is this very interesting project as well as very important for project for current days okay this will help us to save millions of lives if we applied perfectly to know the accuracy of this project just go to the accuracy tab you can get the complete accuracy of the project so this is accuracy score of our project you can see the accuracy around 97% so this is our accuracy we got around 97% accuracy for our propos to system also what are the features we are considered for training means so drainage value Dam quality River Management political factors then population score these are the important features we have considered to uh execute training as well as testing of the Project based on that these features only it will DCT whether the flood may happen or not we have consider drainage also here see the things this is confusion Matrix of our project then overall architecture of our project so this is overall architecture okay to get this project please visit I expert we have displayed various projects for best price okay you can get all the projects from this 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