期刊论文

Ullah, I;Shah, MH

英文

Water

2073-4441

2024

16

11

1492-

10.3390/w16111492

Conceptualization, W.S. and J.C.; methodology, W.S.; software, W.S.; validation, I.U. (Irfan Ullah 1), M.H.S. and W.S.; formal analysis, I.U. (Irfan Ullah 1) and M.H.S.; investigation, I.U. (Irfan Ullah 1); resources, J.C.; data curation, W.S.; writing—original draft preparation, W.S.; writing—review and editing, I.U. (Irfan Ullah 2), M.H.S. and I.U. (Irfan Ullah 1); visualization, I.U. ((Irfan Ullah 1); supervision, J.C.; project administration, I.U. (Irfan Ullah 1); funding acquisition, I.U. (Irfan Ullah 1). All authors have read and agreed to the published version of the manuscript. This research is financially supported by the National Natural Science Foundation of China Fund for International Scientists under Grant No: 42350410438 and the China Postdoctoral Science Foundation Grant No: 2023M730928.

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Water is a fundamental and crucial natural resource for human survival. However, the global demand for water is increasing, leading to a subsequent decrease in water availability. This study addresses the critical need for improved water resource forecasting models amidst global water scarcity concerns exacerbated by climate change. This study uses the best weather and water resource forecasting model for sustainable development. Employing a Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) approach, the research enhances drought prediction capabilities by integrating secondary data o...