Determining the water level fluctuations of Lake Van through the integrated machine learning methods - SERENCAM, UĞUR (My University, 2022)
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.
Keywords: Tarımsal Bilimler; Çevre Mühendisliği; Mühendislik ve Teknoloji; Agricultural Sciences; Environmental Engineering; Engineering and Technology; ÇEVRE BİLİMLERİ; Çevre / Ekoloji; Tarım ve Çevre Bilimleri (AGE); ENVIRONMENTAL SCIENCES; ENVIRONMENT/ECOLOGY; Agriculture & Environment Sciences (AGE); Su Bilimi; Doğa ve Peyzaj Koruma; Çevre Bilimi (çeşitli); Fizik Bilimleri; Yaşam Bilimleri; Aquatic Science; Nature and Landscape Conservation; Environmental Science (miscellaneous); Physical Sciences; Life Sciences; tree-based ensemble machine learning; water level forecast; signal processing; Lake Van; Mann-Whitney U test; hyperparameter optimisation; XGBoost; EMPIRICAL MODE DECOMPOSITION