Forecast of rainfall distribution based on fixed sliding window long short-term memory

Engineering Applications of Computational Fluid Mechanics

Chengcheng Chen¹  Sayed M. Bateni²  Changhyun Jun³  Kwok-Wing Chau⁴  Mahsa H. Kashani⁵  Shahab S. Band⁶  Sonam Sandeep Dash⁷  Qian Zhang⁸ 

[1] College of Computer Science and Technology, Jilin University;Department of Civil and Environmental Engineering and Water Resources Research Center, University of Hawaii at Manoa;Department of Civil and Environmental Engineering, College of Engineering, Chung-Ang University;Department of Civil and Environmental Engineering, Hong Kong Polytechnic University;Department of Water Engineering, Faculty of Agriculture and Natural Resources, Water Management Research Center, University of Mohaghegh Ardabili;Future Technology Research Center, College of Future, National Yunlin University of Science and Technology;School of Water Resources, Indian Institute of Technology Kharagpur;Wenzhou University of Technology;

关键词: deep learning;    forecasting;    long short-term memory;    rainfall;    random forest;    turkey;   

DOI  :  10.1080/19942060.2021.2009374

来源: DOAJ

【 摘 要 】

Applying data mining techniques for rainfall modeling because of a lack of sufficient memory components may increase uncertainty in rainfall forecasting. To solve this issue, in this research, a deep-learning-based long short-term memory (LSTM) model is developed for the first time for forecasting monthly rainfall data, and its capability is compared with a random forest (RF) data-driven model. To this end, monthly rainfall data for a period of 41 years (1980–2020) from two meteorological stations in Turkey, namely Rize and Konya, with different climatic conditions, are used. The analysis is carried out using optimum window sizes for determining the optimum lag times of rainfall time series. The performance of the models is evaluated using five statistical measures, namely root mean square error (RMSE), RMSE-observations standard deviation ratio (RSR), Legate and McCabe’s index (LMI), correlation coefficient (R) and Nash–Sutcliffe efficiency (NSE), and also using two visual means, namely Taylor and violin diagrams. The results reveal that the LSTM model, as a more efficient tool, outperforms the RF model in forecasting rainfall at both stations, with improved RMSE of 12.2–14.9%, RSR of 12.3–14.8%, R of 9.4–13.5% and NSE of 32.9–33.2%. The LSTM-based approach proposed herein could be adopted over any global climatic conditions to forecast the monthly rainfall with reasonable accuracy.

【 授权许可】

Unknown