A new method for monthly streamflow prediction using multi-source data: Range-dependent multivariate adaptive regression splines-genetic algorithm

The present study involves the development of a new method, a range-dependent multivariate adaptive regression splines-genetic algorithm (RD-MARS-GA), for monthly streamflow prediction. The results of the RD-MARS-GA model are compared with standard MARS, Gaussian process regression, principal component regression, M5 model tree and random forest. The input datasets utilized in the study combine satellite and ground-based datasets. Monthly data from the Treene, Schaale, and Wandse rivers in Northern Germany were used as case studies. The satellite datasets used were sourced from the MERRA-2, IMERG-GPM and MODIS. A comparison of the results reveals that the RD-MARS-GA can improve the forecasting accuracy of the MARS model across all watersheds at different temporal scales. The RD-MARS-GA model surpassed the other regression models and improved the root mean square accuracy of the MARS model by 12.8%, 26.7% and 18.5 in predicting one-month ahead streamflow of the rivers Treene, Schaale, and Wandse, respectively.