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

Serencam U., Ekmekcioğlu Ö., Başakın E. E., Özger M.

INTERNATIONAL JOURNAL OF GLOBAL WARMING, cilt.27, sa.2, ss.123-142, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 27 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1504/ijgw.2022.123278
  • Dergi Adı: INTERNATIONAL JOURNAL OF GLOBAL WARMING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.123-142
  • Anahtar Kelimeler: tree-based ensemble machine learning, water level forecast, signal processing, Lake Van, Mann-Whitney U test, hyperparameter optimisation, XGBoost, EMPIRICAL MODE DECOMPOSITION
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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.