A comparative study of extensive machine learning models for predicting long‐term monthly rainfall with an ensemble of climatic and meteorological predictors. Issue 11 (18th November 2021)

• Record Type: Journal Article
• Authors: Zhou, Zhengzheng; Ren, Jie; He, Xiaogang; Liu, Shuguang
• Is Part Of: Hydrological processes. Volume 35:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-18
• Subjects: climatic and meteorological predictors -- machine learning -- monthly rainfall prediction; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.14424
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: Rainfall prediction is of vital importance in water resources management. Accurate long‐term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning‐based methods for monthly rainfall, and to improve the understanding of the role of large‐scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state‐of‐the‐art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual‐stage attention‐based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4‐month lagged Western North Pacific Monsoon had higher importance than other large‐scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions. Abstract : This study examined the performances of extensive machine learning methods for predicting monthly rainfall and explored the role of large‐scale climatic variables and local meteorological variables in rainfall prediction over 25 stations in the East China region. The results demonstrate that the ML models outperform ARIMA model, and RF model relatively outperforms other models. The overall output is scalable and can be readily generalized to other regions.