An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System

Hayder, Israa M. and Al-Amiedy, Taief Alaa and Ghaban, Wad and Saeed, Faisal and Nasser, Maged and Al-Ali, Ghazwan Abdulnabi and Younis, Hussain A. (2023) An Intelligent Early Flood Forecasting and Prediction Leveraging Machine and Deep Learning Algorithms with Advanced Alert System. Processes, 11 (2). p. 481. ISSN 2227-9717

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Abstract

Flood disasters are a natural occurrence around the world, resulting in numerous casualties. It is vital to develop an accurate flood forecasting and prediction model in order to curb damages and limit the number of victims. Water resource allocation, management, planning, flood warning and forecasting, and flood damage mitigation all benefit from rain forecasting. Prior to recent decades’ worth of research, this domain demonstrated to be promising prospects in time series prediction tasks. Therefore, the main aim of this study is to build a forecasting model based on the exponential smoothing-long-short term memory (ES-LSTM) structure and recurrent neural networks (RNNs) for predicting hourly precipitation seasons; and classify the precipitation using an artificial neural network (ANN) model and decision tree (DT) algorithm. We employ the dataset from the Australian commonwealth office of meteorology named Historical Daily Weather dataset to test the effectiveness of the proposed model. The findings showed that the ES-LSTM and RNN had achieved 3.17 and 6.42 in terms of mean absolute percentage error (MAPE), respectively. Meanwhile, the ANN and DT models obtained a prediction accuracy rate of 96.65% and 84.0%, respectively. Finally, the outcomes revealed that ES-LSTM and ANN had achieved the best results compared to other models.

Item Type:	Article
Identification Number:	10.3390/pr11020481
Dates:	Date Event 3 February 2023 Accepted 5 February 2023 Published Online
Uncontrolled Keywords:	flood forecasting and prediction, multilayer perceptron (MLP), time series analysis; es-lstm, machine learning (ML), deep learning (DL), artificial neural network (ANN), decision tree (DT), recurrent neural network (RNN), exponential smoothing
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Gemma Tonks
Date Deposited:	09 Mar 2023 10:33
Last Modified:	09 Mar 2023 10:33
URI:	https://www.open-access.bcu.ac.uk/id/eprint/14233

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