A comprehensive method based on machine learning schemes in predicting river flow, case study: Po River

Abstract

River flow forecasting has been the focus of many researchers for many years.  The methods evolved from simple statistical methods to highly sophisticated mathematical models.  In recent years, due to the advancement of computers and artificial algorithms, new methods have become increasingly reliable and easier to use.  One of the promising artificial intelligence methods is the Extreme Gradient Boosting (XGBoost) model.  XGBoost is a scalable, distributed gradient-boosting decision tree machine learning library.  It provides parallel tree boosting and is the leading machine learning library for regression, classification, and ranking problems.  Three different algorithms of XGBoost were used in this research and the results were compared.  These algorithms were Random Search, Grid Search, and CatBoost. The proposed models were conducted in a station located Pò River basin which is the longest river in Italy, and it flows from the Cottian Alps and ends at a delta projecting into the Adriatic Sea new Venice.  The data were divided into training and validation sets.  The statistical indicators included mean square error, Nash-Sutcliffe efficiency, and mean absolute error were calculated for each set to compare the efficiency of each algorithm.  These indicators showed that XGBoost using random search algorithm had better performance, although the other algorithms were also acceptable predictions.  In general, the XGBoost model could be used as a reliable tool to forecast the river flow at locations with enough historical data.