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Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters

Flood catastrophes are among the natural disasters that have occurred regularly around the world. Malaysia is one of the countries that experience flood disasters on a yearly basis, most notably during the monsoon season, which runs from November to January. This study developed a novel flood foreca...

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Bibliographic Details
Main Author: Maspo, Nur-Adib
Format: Thesis
Language:English
Published: 2022
Subjects:
Q Science (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/100374/1/NurAdibMaspoPMJIIT2022.pdf
http://eprints.utm.my/id/eprint/100374/
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spelling my.utm.1003742023-04-13T02:31:56Z http://eprints.utm.my/id/eprint/100374/ Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters Maspo, Nur-Adib Q Science (General) TA Engineering (General). Civil engineering (General) Flood catastrophes are among the natural disasters that have occurred regularly around the world. Malaysia is one of the countries that experience flood disasters on a yearly basis, most notably during the monsoon season, which runs from November to January. This study developed a novel flood forecasting model through the application of advanced machine learning (ML), deep learning (DL), and natural language processing (NLP) for sentiment analysis and text classification before a flood event, during a flood event and after a flood event. The morphometric ranking approach (MRA) was used to identify flood-susceptibility areas. Various data sources were collected including natural dimension such as rainfall intensity (mm), streamflow (cm/s), and water level (m) from Department of Irrigation and Drainage, and social dimension like text data extracted from Twitter platform. A digital elevation model (DEM) was used to process parameters for MRA with the application of geographic information system (GIS) for identifying flood-prone areas. General ML pipelines were used before building the model such as data pre-processing, data exploration to detect outliers, and filling missing values. The flood forecasting model used advanced machine learning and deep learning specifically long-short term memory (LSTM) which is suitable for time series data of rainfall and streamflow forecasting. Additionally, the model was developed using these three models: LSTM, ARIMA, and FB Prophet. The forecasting results indicated that the LSTM model has a root mean square error (RMSE) of 10.76, which is more accurate in comparison to the other models ARIMA and FB Prophet, which have RMSE values of 14.15, and 14.23, respectively. The accuracy of the model of text classification algorithm for predicting flood events is 0.87. Flood susceptibility mapping using MRA revealed that sub-catchments 5, 24, and 25 were highly susceptible to flooding. These sub-catchments were located in Jeli, Kuala Krai sub-district, and Gua Musang sub-district, respectively. In sum, this flood forecasting model is vital to provide flood information for early warning system to enable flood managers or decision-makers to make more informed plans during the flood preparation and mitigation phases, thereby minimizing the impact of floods on people, property, and the environment. 2022 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/100374/1/NurAdibMaspoPMJIIT2022.pdf Maspo, Nur-Adib (2022) Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters. PhD thesis, Universiti Teknologi Malaysia, Malaysia-Japan International Institute of Technology. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150904
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
TA Engineering (General). Civil engineering (General)
spellingShingle Q Science (General)
TA Engineering (General). Civil engineering (General)
Maspo, Nur-Adib
Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
description Flood catastrophes are among the natural disasters that have occurred regularly around the world. Malaysia is one of the countries that experience flood disasters on a yearly basis, most notably during the monsoon season, which runs from November to January. This study developed a novel flood forecasting model through the application of advanced machine learning (ML), deep learning (DL), and natural language processing (NLP) for sentiment analysis and text classification before a flood event, during a flood event and after a flood event. The morphometric ranking approach (MRA) was used to identify flood-susceptibility areas. Various data sources were collected including natural dimension such as rainfall intensity (mm), streamflow (cm/s), and water level (m) from Department of Irrigation and Drainage, and social dimension like text data extracted from Twitter platform. A digital elevation model (DEM) was used to process parameters for MRA with the application of geographic information system (GIS) for identifying flood-prone areas. General ML pipelines were used before building the model such as data pre-processing, data exploration to detect outliers, and filling missing values. The flood forecasting model used advanced machine learning and deep learning specifically long-short term memory (LSTM) which is suitable for time series data of rainfall and streamflow forecasting. Additionally, the model was developed using these three models: LSTM, ARIMA, and FB Prophet. The forecasting results indicated that the LSTM model has a root mean square error (RMSE) of 10.76, which is more accurate in comparison to the other models ARIMA and FB Prophet, which have RMSE values of 14.15, and 14.23, respectively. The accuracy of the model of text classification algorithm for predicting flood events is 0.87. Flood susceptibility mapping using MRA revealed that sub-catchments 5, 24, and 25 were highly susceptible to flooding. These sub-catchments were located in Jeli, Kuala Krai sub-district, and Gua Musang sub-district, respectively. In sum, this flood forecasting model is vital to provide flood information for early warning system to enable flood managers or decision-makers to make more informed plans during the flood preparation and mitigation phases, thereby minimizing the impact of floods on people, property, and the environment.
format Thesis
author Maspo, Nur-Adib
author_facet Maspo, Nur-Adib
author_sort Maspo, Nur-Adib
title Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
title_short Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
title_full Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
title_fullStr Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
title_full_unstemmed Flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
title_sort flood forecasting using advanced machine learning model and flood susceptibility analysis and mapping using morphometric parameters
publishDate 2022
url http://eprints.utm.my/id/eprint/100374/1/NurAdibMaspoPMJIIT2022.pdf
http://eprints.utm.my/id/eprint/100374/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150904
_version_ 1764222557620273152
score 13.211869

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