Lake water level modeling using newly developed hybrid data intelligence model

Yaseen Z. M., Naghshara S., Salih S. Q., Kim S., Malik A., Ghorbani M. A.

THEORETICAL AND APPLIED CLIMATOLOGY, vol.141, pp.1285-1300, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 141
  • Publication Date: 2020
  • Doi Number: 10.1007/s00704-020-03263-8
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, IBZ Online, PASCAL, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, CAB Abstracts, Environment Index, Geobase, Index Islamicus, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Page Numbers: pp.1285-1300
  • Istanbul Technical University Affiliated: No


The forecasting of lake water level is one of the complex problems in the hydrology field owing to the incorporating with various hydrological and morphological characteristics. In this research, newly hybrid data intelligence (DI) model based on the integration of the Multilayer Perceptron (MLP) and Whale Optimization Algorithm (WOA) is developed for lake water level forecasting. The potential of the proposed hybrid MLP_WOA model is validated against several well-established DI models over the literature including the Cascade-Correlation Neural Network Model (CCNNM), Self-Organizing Map (SOM), Decision Tree Regression (DTR), Random Forest Regression (RFR), and classical MLP. The applied predictive models are examined to forecast the Van Lake water level fluctuation with monthly scale over seven-decade time period (1943-2016). The input variables are abstracted using statistical correlation analysis procedure. The modeling is diagnosed using multiple statistical metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient (NSE), Willmott's Index (WI), Legate and McCabe's Index (LMI), determination coefficient (R-2)). In addition, graphical distribution data such as the Taylor diagram, violin plot, and point density are investigated. Results indicated that the MLP_WOA model performed superior prediction results over the comparable models based on forecasting performance. Five-month lead times performed the best results for the prediction procedure. In quantitative terms, the RMSE and MAE are reduced by 29.8% and 33.9%, 48.3% and 52%, 57.6% and 59.7%, 53.9% and 58.3%, and 25.3% and 23.9% using the MLP_WOA model over CCNNM, SOM, DTR, RFR, and MLP models, respectively. In comparison with the literature studies, using longer span of historical data elevated the forecasting accuracy. In summary, MLP_WOA model provided an applicable and simple methodology for Van Lake water level forecasting owing to its simple learning procedure.