Daily Reservoir Evaporation Estimation Using MLP and ANFIS: A Comparative Study for Sustainable Water Management

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DÖKMEN F., COŞKUN DİLCAN Ç., AHİ Y.

WATER, cilt.17, sa.17, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 17
  • Basım Tarihi: 2025
  • Doi Numarası: 10.3390/w17172623
  • Dergi Adı: WATER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Compendex, Environment Index, Food Science & Technology Abstracts, Geobase, INSPEC, Pollution Abstracts, Veterinary Science Database, Directory of Open Access Journals
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Reservoir evaporation is a vital component of the hydrological cycle and presents considerable challenges for sustainable water management, especially in arid and semi-arid regions. This study assesses the effectiveness of two Artificial Intelligence (AI) methods: Multilayer Perceptron (MLP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), a combination ANN with fuzzy logic, in estimating daily evaporation from a large reservoir in a semi-arid region. Using eight years of hydrometeorological data from a nearby station, the study employed the ReliefF algorithm as a feature selection method for relevant input variables. The dataset was divided into training, validation, and testing subsets with 5% and 10% validation ratios, using four train-test splits of 70:30, 75:25, 80:20, and 85:15. Various training algorithms (e.g., Levenberg-Marquardt) and membership functions (e.g., generalized bell-shaped functions) were tested for both models. MLP consistently outperformed ANFIS on the test sets, showing higher R2 and lower RMSE values. In the best-performing 70:30 split, MLP achieved an R2 of 0.8069 and RMSE of 0.0923, compared to ANFIS with an R2 of 0.3192 and RMSE of 0.2254. The findings highlight the AI-based approaches' potential to support improved evaporation forecasting and integration into decision support tools for water resource planning amid changing climatic conditions.