Journal of Water and Wastewater Science and Engineering

Journal of Water and Wastewater Science and Engineering

Short-Term Prediction of Hydrological Drought Using Deep Learning-Based Method

Document Type : Research Paper

Authors
Abbas Noorinejad 1
Farhad Hooshyaripor 2
Majid Dehghani 3
1 M.Sc., Faculty of Civil Engineering, Architecture and Art, Sciences and Research Branch, Islamic Azad University, Tehran, Iran.
2 Asisstant professor, Faculty of Civil Engineering, Architecture and Art, Sciences and Research Branch, Islamic Azad University, Tehran, Iran.
3 Associate Professor, Technical and Engineering Department, Faculty of Civil Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.
10.22112/jwwse.2023.381217.1345
Abstract
Hydrological drought affects water allocation and hydropower generation. Therefore, hydrological drought prediction can help for optimal water resources management. In this paper, short-term hydrological drought was predicted using deep learning. Doing so, Standardized Hydrological Drought Index (SHDI) was calculated in one-, three- and six-month periods. Then, 36 combinations and three different modes of input variables were considered to predict SHDI. In the next step, various optimization algorithms, including grasshopper optimization algorithm, salt swarm algorithm, biogeography-based optimization, and particle swarm optimization combined with ANN were used to predict SHDI. Here, 70% of the data were considered for training and the remaining 30% for test. Finally, the performance of deep learning (DNN) compared to the combined ANN-SSA, -BBO, -PSO and -GOA were discussed. The results of ANN compared to the combined models showed that the combined models performed better, but they were less campatible to DNN. The DNN on one hand, does not need to define the input combination, and on the other hand could reduce the value of RMSE to 0.32.
Keywords
Hydrologic Drought
Deep Learning
Prediction
Standardized Hydrologic Drought Index
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Journal of Water and Wastewater Science and Engineering
Volume 9, Issue 2
Summer 2024
Pages 3-16

  • Receive Date 21 January 2023
  • Revise Date 31 August 2023
  • Accept Date 13 September 2023