Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

Precipitation forecasting is essential for the assessment of several hydrological processes. This study shows that based on a machine learning approach, reliable models for precipitation prediction can be developed. The tropical monsoon-climate northern region of Bangladesh, including the Rangpur and Sylhet division, was chosen as the case study. Two machine learning algorithms were used: M5P and support vector regression. Moreover, a novel hybrid model based on the two algorithms was developed. The performance of prediction models was assessed by means of evaluation metrics and graphical representations. A sensitivity analysis was also carried out to assess the prediction accuracy as the number of exogenous inputs reduces and lag times increases. Overall, the hybrid model M5P-SVR led to the best predictions among used models in this study, with R-2 values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively.

Automated summary

This study shows that reliable models for precipitation prediction can be developed using a machine learning approach. A hybrid model based on M5P and support vector regression algorithms achieved the best predictions in this study, with high R-2 values for the stations of Rangpur and Sylhet.