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

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.

Automated summary

In this study, a deep-learning-based LSTM model is developed for the first time to forecast monthly rainfall data and compared with the RF model. The results show that the LSTM model outperforms the RF model in rainfall forecasting at both stations, with reasonable accuracy under any global climatic conditions.