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A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida

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  • Yicheng Gong

    (Beijing University of Technology
    Ohio State University)

  • Yongxiang Zhang

    (Beijing University of Technology)

  • Shuangshuang Lan

    (Beijing University of Technology
    Key Laboratory of Subsurface Hydrology and Ecological Effect in Arid Region of Ministry of Education)

  • Huan Wang

    (China Institute of Water Resource and Hydropower Research)

Abstract

Accurate and reliable prediction of groundwater level is essential for water resource development and management. This study was carried out to test the validity of three nonlinear time-series intelligence models, artificial neural networks (ANN), support vector machines (SVM) and adaptive neuro fuzzy inference system (ANFIS) in the prediction of the groundwater level when taking the interaction between surface water and groundwater into consideration. These three models were developed and applied for two wells near Lake Okeechobee in Florida, United States. 10 years data-sets including hydrological parameters such as precipitation (P), temperature (T), past groundwater level (G) and lake level (L) were used as input data to forecast groundwater level. Five quantitative standard statistical performance evaluation measures, correlation coefficient (R), normalized mean square error (NMSE), root mean squared error (RMSE), Nash-Sutcliffe efficiency coefficient (NS) and Akaike information criteria (AIC), were employed to evaluate the performances of these models. The conclusions achieved from this research would be beneficial to the water resources management, it proved the necessity and effect of considering the surface water-groundwater interaction in the prediction of groundwater level. These three models were proved applicable to the prediction of groundwater level one, two and three months ahead for the area that is close to the surface water, for example, the lake area. The models using P, T, G and L achieved better prediction result than that using P, T and G only. At the same time, results from ANFIS and SVM models were more accurate than that from ANN model.

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  • Yicheng Gong & Yongxiang Zhang & Shuangshuang Lan & Huan Wang, 2016. "A Comparative Study of Artificial Neural Networks, Support Vector Machines and Adaptive Neuro Fuzzy Inference System for Forecasting Groundwater Levels near Lake Okeechobee, Florida," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 375-391, January.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:1:d:10.1007_s11269-015-1167-8
    DOI: 10.1007/s11269-015-1167-8
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    1. Veysel Güldal & Hakan Tongal, 2010. "Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(1), pages 105-128, January.
    2. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Erratum to: Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(11), pages 4113-4113, September.
    3. Bagher Shirmohammadi & Mehdi Vafakhah & Vahid Moosavi & Alireza Moghaddamnia, 2013. "Application of Several Data-Driven Techniques for Predicting Groundwater Level," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(2), pages 419-432, January.
    4. Afiq Hipni & Ahmed El-shafie & Ali Najah & Othman Karim & Aini Hussain & Muhammad Mukhlisin, 2013. "Daily Forecasting of Dam Water Levels: Comparing a Support Vector Machine (SVM) Model With Adaptive Neuro Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(10), pages 3803-3823, August.
    5. Xiaohu Wen & Jianhua Si & Zhibin He & Jun Wu & Hongbo Shao & Haijiao Yu, 2015. "Support-Vector-Machine-Based Models for Modeling Daily Reference Evapotranspiration With Limited Climatic Data in Extreme Arid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(9), pages 3195-3209, July.
    6. Jehangir Awan & Deg-Hyo Bae, 2014. "Improving ANFIS Based Model for Long-term Dam Inflow Prediction by Incorporating Monthly Rainfall Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1185-1199, March.
    7. Samad Emamgholizadeh & Khadije Moslemi & Gholamhosein Karami, 2014. "Prediction the Groundwater Level of Bastam Plain (Iran) by Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(15), pages 5433-5446, December.
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