气候变化领域集成服务门户(CCPortal): Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia

CCPortal

DOI	10.1007/s11069-021-04839-x
	Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia
	Latif S.D.; Ahmed A.N.; Sathiamurthy E.; Huang Y.F.; El-Shafie A.
发表日期	2021
ISSN	0921030X
英文摘要	Forecasting of reservoir inflow is one of the most vital concerns when it comes to managing water resources at reservoirs to mitigate natural hazards such as flooding. Machine learning (ML) models have become widely prevalent in capturing the complexity of reservoir inflow time-series data. However, the model structure's selection required several trails-and-error processes to identify the optimal architecture to capture the necessary information of various patterns of input–output mapping. In this study, the effectiveness of a deep learning (DL) approach in capturing various input–output patterns is examined and applied to reservoir inflow forecasting. The proposed DL approach has a distinct benefit over classical ML models as all the hidden layers are stacked afterward to train on a diverging set of topologies derived from the previous layer's output. Given the nonlinearity of day-to-day data about reservoir inflow, a deep learning algorithm centered on the long short-term memory (LSTM) and two standard machine learning algorithms, namely support vector machine (SVM) and artificial neural network (ANN), were deployed in this study for forecasting reservoir inflow on a daily basis. The gathered data pertained to historical daily inflow from 01/01/2018 to 31/12/2019. The area of study was Durian Tunggal Reservoir, Melaka, Peninsular Malaysia. The choice of the input set was made on the basis of the autocorrelation function. The formulated model was assessed on the basis of statistical indices, such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R2). The outcomes indicate that the LSTM model performed much better than SVM and ANN. Based on the comparison, LSTM outperformed other models with MAE = 0.088, RMSE = 0.27, and R2 = 0.91. This research demonstrates that the deep learning technique is an appropriate method for estimating the daily inflow of the Durian Tunggal Reservoir, unlike the standard machine learning models. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Artificial neural network (ANN)Inflow prediction model Long short-term memory (LSTM)Malaysia Support vector machine (SVM)Water resources management
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206350
作者单位	Civil Engineering Department, College of Engineering, Komar University of Science and Technology, Sulaimany, Kurdistan Region 46001, Iraq; Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Selangor, 43000, Malaysia; Faculty of Science and Marine Environment, Universiti Malaysia Terengganu, Terengganu, Malaysia; Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia; Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, 50603, Malaysia; National Water and Energy Center, United Arab Emirates University, P.O. Box. 15551, Al Ain, United Arab Emirates
推荐引用方式 GB/T 7714	Latif S.D.,Ahmed A.N.,Sathiamurthy E.,et al. Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia[J],2021.
APA	Latif S.D.,Ahmed A.N.,Sathiamurthy E.,Huang Y.F.,&El-Shafie A..(2021).Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia.Natural Hazards.
MLA	Latif S.D.,et al."Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia".Natural Hazards (2021).