IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network

This project outlines the design of a flood monitoring system to obtain accurate data on river overflows. Additionally, it provides the machine learning technique, to predict the arrival of floods, by considering the rainfall data and water level from previously available data to predict the rainfal...

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Main Author:	Mohammed Raslan, Monzer
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/99540/1/MonzerMohammedRaslanMSKE2022.pdf
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id	my-utm-ep.99540
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spelling	my-utm-ep.995402023-02-28T07:45:26Z IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network 2022 Mohammed Raslan, Monzer TK Electrical engineering. Electronics Nuclear engineering This project outlines the design of a flood monitoring system to obtain accurate data on river overflows. Additionally, it provides the machine learning technique, to predict the arrival of floods, by considering the rainfall data and water level from previously available data to predict the rainfall and water level for the next hours. The problem is the shortage of flood information in areas that are constantly flooded leads to malfunction in analysing the flood reasons. In addition, the fuzzy and unpredicted situation of the flood. Moreover, there is no flood data analysis so action can be taken based. Finally, data is not visualized in a Dashboard, so they can have a deeper look at the situation. The object of this study is to design an IoT flood monitoring system based on two water level sensors and a rain gauge sensor. In addition, to forecast the flood based on Long Short-Term Memory (LSTM) networks for historical data and the data collected from the monitoring system. The monitoring system utilizes a submersible water level sensor that measures the water level. Additionally, the tipping bucket rainfall sensor measures the rain gauge and tests the rainfall in the natural environment. The system is based on IoT to provide real-time data. The recorded data is transmitted to the cloud via a GSM network and displayed on an online platform. The flood forecasting model used Long Short-Term Memory (LSTM) networks to predict future floods. The aim of this case study is to contribute to the reduction of casualties and flood damage in streams, as well as to the development of more accurate flood forecasting in typical urban flood risk locations. The result was experimented with using historical data since the current data is insufficient yet to make an accurate prediction. The main findings of the research are the predicted values of streamflow and rainfall for historical data, also water level and rain gauge for new data. The forecasting method that applied LSTM showed high accuracy of the result reaching more than 90% with evaluation errors for historical data MAE, RMSE and MSE are 0.93, 1.7 and 3.025 respectively. Also, 0.0055, 0.3325 and 0.1175 for new data respectively. The developed monitoring system and flood forecasting can be used efficiently as a non-structural solution to alleviate the damage caused by urban floods. 2022 Thesis http://eprints.utm.my/id/eprint/99540/ http://eprints.utm.my/id/eprint/99540/1/MonzerMohammedRaslanMSKE2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149947 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohammed Raslan, Monzer IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network
description	This project outlines the design of a flood monitoring system to obtain accurate data on river overflows. Additionally, it provides the machine learning technique, to predict the arrival of floods, by considering the rainfall data and water level from previously available data to predict the rainfall and water level for the next hours. The problem is the shortage of flood information in areas that are constantly flooded leads to malfunction in analysing the flood reasons. In addition, the fuzzy and unpredicted situation of the flood. Moreover, there is no flood data analysis so action can be taken based. Finally, data is not visualized in a Dashboard, so they can have a deeper look at the situation. The object of this study is to design an IoT flood monitoring system based on two water level sensors and a rain gauge sensor. In addition, to forecast the flood based on Long Short-Term Memory (LSTM) networks for historical data and the data collected from the monitoring system. The monitoring system utilizes a submersible water level sensor that measures the water level. Additionally, the tipping bucket rainfall sensor measures the rain gauge and tests the rainfall in the natural environment. The system is based on IoT to provide real-time data. The recorded data is transmitted to the cloud via a GSM network and displayed on an online platform. The flood forecasting model used Long Short-Term Memory (LSTM) networks to predict future floods. The aim of this case study is to contribute to the reduction of casualties and flood damage in streams, as well as to the development of more accurate flood forecasting in typical urban flood risk locations. The result was experimented with using historical data since the current data is insufficient yet to make an accurate prediction. The main findings of the research are the predicted values of streamflow and rainfall for historical data, also water level and rain gauge for new data. The forecasting method that applied LSTM showed high accuracy of the result reaching more than 90% with evaluation errors for historical data MAE, RMSE and MSE are 0.93, 1.7 and 3.025 respectively. Also, 0.0055, 0.3325 and 0.1175 for new data respectively. The developed monitoring system and flood forecasting can be used efficiently as a non-structural solution to alleviate the damage caused by urban floods.
format	Thesis
qualification_level	Master's degree
author	Mohammed Raslan, Monzer
author_facet	Mohammed Raslan, Monzer
author_sort	Mohammed Raslan, Monzer
title	IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network
title_short	IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network
title_full	IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network
title_fullStr	IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network
title_full_unstemmed	IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network
title_sort	iot based real-time monitoring system of rainfall and water level for flood prediction using lstm network
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/99540/1/MonzerMohammedRaslanMSKE2022.pdf
_version_	1776100615121797120

IoT based real-time monitoring system of rainfall and water level for flood prediction using LSTM network

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