Machine Learning Approaches for Prediction of Daily River Flow

River flow is an important parameter in hydrology, irrigation scheduling, groundwater pollution studies, and hydropower analysis. It depends on various climate and hydrologic factors, e.g. precipitation, temperature, river basin physiography, geological characteristics of the basin, etc. Although several factors may affect river flow quantity and quality during a certain period, it is difficult to account for all those variables in simulating/predicting river flow values due to the complex relations governing the hydrologic cycle in nature. Therefore, using simpler methods that can be used with fewer required input data would be necessary. A prediction task was implemented in the present study to obtain river flow values based on the previously recorded river flows using three machine learning approaches, namely, multi-variate adaptive regression spline (MARS), boosted regression tree (BT), and random forest (RF). Data from three stations in Iowa stat (U.S.A) covering daily records of five years were utilized for developing the ML models. Based on the results, all three applied models could simulate the river flow values well, when the time lags of two successive days were introduced to feed the model. An analysis was also made for detecting the variations of the applied statistical indicators per test stage of k-fold testing data assignment. This analysis showed obvious variations of indicators among the test stages, revealing the necessity of adopting k-fold testing in the studied region.