Determining the water level fluctuations of Lake Van through the integrated machine learning methods

Abstract

Determining the lake levels is of paramount importance considering the environmental challenges encountered due to the global warming. The purpose of this study is to predict water level fluctuation of Lake Van using extreme gradient boosting (XGBoost). In addition, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was adopted to the proposed model. The gravitational search algorithm (GSA) was utilised to tune the hyperparameters of XGBoost and the genetic algorithm (GA) and particle swarm optimisation (PSO) were used for benchmarking. The results showed that GSA-CEEMDAN-XGBoost model outperformed its counterparts, i.e., GA-CEEMDAN-XGBoost and PSO-CEEMDAN-XGBoost, according to the performance metrics.