Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms

Essam, Yusuf and Huang, Yuk Feng and Ng, Jing Lin and Birima, Ahmed H. and Ahmed, Ali Najah and El-Shafie, Ahmed (2022) Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Scientific Reports, 12 (1). ISSN 2045-2322, DOI

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Floods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation. In the present study, machine learning (ML) models based on the support vector machine (SVM), artificial neural network (ANN), and long short-term memory (LSTM), are tested and developed to predict SF for 11 different rivers throughout Peninsular Malaysia. SF data sets for the rivers were collected from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a universal model that is most capable of predicting SFs for rivers within Peninsular Malaysia. Based on the findings, the ANN3 model which was developed using the ANN algorithm and input scenario 3 (inputs consisting of previous 3 days SF) is deduced as the best overall ML model for SF prediction as it outperformed all the other models in 4 out of 11 of the tested data sets; and obtained among the highest average RMs with a score of 3.27, hence indicating that the model is very adaptable and reliable in accurately predicting SF based on different data sets and river case studies. Therefore, the ANN3 model is proposed as a universal model for SF prediction within Peninsular Malaysia.

Item Type: Article
Funders: Universiti Tunku Abdul Rahman (UTAR), Malaysia, via Project Research Assistantship UTARRPS 6251/H03
Uncontrolled Keywords: Neural-network; Time-Series; Forecast; River; SVM
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GB Physical geography
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 14 Oct 2023 06:17
Last Modified: 14 Oct 2023 06:17

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