Abstract
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.
Highlights
Floods and droughts are natural phenomena that have impacted regions within Peninsular Malaysia throughout recorded history
The best overall performance in predicting SF for the Sungai Johor, Johor data set was produced by model ANN3, which is based on the artificial neural network (ANN) algorithm and input parameter scenario 3
The best overall performance in predicting SF for the Sungai Pahang, Pahang data set was produced by model ANN3, which is based on the ANN algorithm and input parameter scenario 3
Summary
Floods and droughts are natural phenomena that have impacted regions within Peninsular Malaysia throughout recorded history. ML algorithms are able to identify trends and patterns in a large database and continually improve in predictive ability with time, while not requiring much human intervention as they self-learn For these reasons, ML is a valuable tool for modelling and predicting SF as different rivers have different SF magnitudes and behaviours, depending on the spatial and temporal variability as well as the water balance component heterogeneity of a particular river[1,12]. Hybridization of SVM, ANN, and LSTM is not investigated in the present study, as the present study intends to identify the standalone ML model that is most accurate and suitable as a universal model for the case study of 11 different river streamflow data sets in Peninsular Malaysia, which has not been performed before in existing studies. The findings of the present study may open up a topic or focus for a future study on the hybridization of the standalone universal model proposed at the end of the present study
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