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DOI	10.1007/s13201-022-01798-x
	Utilizing deep learning machine for inflow forecasting in two different environment regions: a case study of a tropical and semi-arid region
	Saab, Saad Mawlood; Othman, Faridah; Tan, Chee Ghuan; Allawi, Mohammed Falah; Sherif, Mohsen; El-Shafie, Ahmed
通讯作者	El-Shafie, A
来源期刊	APPLIED WATER SCIENCE
ISSN	2190-5487
EISSN	2190-5495
出版年	2022
卷号	12 期号:12
英文摘要	Reservoir inflow (Q(flow)) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Q(flow) at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm genetic algorithm (GA), for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm.
英文关键词	Reservoir inflow Deep learning Lead time Tropical region Semi-arid region
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000877732100003
WOS关键词	NEURAL-NETWORKS ; MODEL ; PREDICTION ; HYDROLOGY ; PERFORMANCE ; PARAMETERS
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391884
推荐引用方式 GB/T 7714	Saab, Saad Mawlood,Othman, Faridah,Tan, Chee Ghuan,et al. Utilizing deep learning machine for inflow forecasting in two different environment regions: a case study of a tropical and semi-arid region[J],2022,12(12).
APA	Saab, Saad Mawlood,Othman, Faridah,Tan, Chee Ghuan,Allawi, Mohammed Falah,Sherif, Mohsen,&El-Shafie, Ahmed.(2022).Utilizing deep learning machine for inflow forecasting in two different environment regions: a case study of a tropical and semi-arid region.APPLIED WATER SCIENCE,12(12).
MLA	Saab, Saad Mawlood,et al."Utilizing deep learning machine for inflow forecasting in two different environment regions: a case study of a tropical and semi-arid region".APPLIED WATER SCIENCE 12.12(2022).