Modeling rainfall-runoff process using soft computing techniques

Kisi O., Shiri J., TOMBUL M.

COMPUTERS & GEOSCIENCES, cilt.51, ss.108-117, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 51
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.cageo.2012.07.001
  • Dergi Adı: COMPUTERS & GEOSCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.108-117
  • Anahtar Kelimeler: Rainfall-runoff process, Neural networks, Neuro-fuzzy system, Gene expression programming, GENETIC PROGRAMMING APPROACH, DAILY PAN EVAPORATION, SUSPENDED SEDIMENT ESTIMATION, FUZZY INFERENCE SYSTEM, NEURO-FUZZY, ARTIFICIAL-INTELLIGENCE, CLIMATIC DATA, SHORT-TERM, PREDICTION, NETWORKS
  • Anadolu Üniversitesi Adresli: Evet

Özet

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R-2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods. (c) 2012 Elsevier Ltd. All rights reserved.