Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation

Among several components of the hydrology cycle, streamflow is one of the essential process necessarily needed to be studied. The establishment of an accurate and reliable forecasting soft computing model for this process is highly vital for water resource planning and management. The influence of the climatological environment on streamflow is central and studying its influence is very significant from the hydrology perspective. It has been noticed that the application of machine learning models considerably become predominant in solving and capturing the complexity of hydrological applications. This research presents the implementation of a novel hybrid model called Multivariate Adaptive Regression Spline integrated with Differential Evolution (MARS-DE) to forecast streamflow pattern in semi-arid region. To achieve this, monthly time series streamflow data at Baghdad station, coordinated at Tigris River, Iraq, is inspected. For the model validation, Least Square Support Vector Regression (LSSVR) and standalone MARS models are conducted. To demonstrate the analysis of the undertaken models, several statistical indicators are computed to verify the modeling accuracies. Based on the achieved results, the MARS-DE model exhibited an excellent hybrid predictive modeling capability for monthly time scale streamflow in semi-arid region. Quantitatively; MARS-DE, LSSVR and MARS models achieved the minimum root mean square error (RMSE) and mean absolute error (MAE) values of 46.64–35.25 m ³ /s, 57.50–49.20 m ³ /s and 78.01–62.65 m ³ /s, respectively. In conclusion, several perspectives are suggested for further studies to enhance the forecasting capability of the model.