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Improving ANFIS Based Model for Long-term Dam Inflow Prediction by Incorporating Monthly Rainfall Forecasts

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  • Jehangir Awan
  • Deg-Hyo Bae

Abstract

The necessity of long-term dam inflow forecast has been recognized for many years. Despite numerous studies, the accurate long-term dam inflow prediction is still a challenging task. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) based model and evaluates the applicability of categorical rainfall forecast for improvement of monthly dam inflow prediction. In order to obtain appropriate ANFIS model configuration for dam inflow prediction, several models were trained and tested using various numbers of input variables i.e. monthly observed rainfall, relative humidity, temperature, dam inflow and categorical monthly rainfall forecast. The ANFIS based models were configured and evaluated for six major dams of South Korea i.e. Andong, Chungju, Daecheong, Guesan, Soyang and Sumjin having high, medium and low reservoir capacity. The results showed significant improvement in dam inflow prediction for all the selected dams using the ANFIS based model with categorical rainfall forecast compared to the ANFIS based model with only preceding month’s dam inflow and weather data. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:5:p:1185-1199
    DOI: 10.1007/s11269-014-0512-7
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    1. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    2. Saman Razavi & Shahab Araghinejad, 2009. "Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(1), pages 39-55, January.
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    1. Xiaoli Zhang & Haixia Wang & Anbang Peng & Wenchuan Wang & Baojian Li & Xudong Huang, 2020. "Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(4), pages 1479-1493, March.
    2. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.
    3. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.
    4. Chuan Li & Yun Bai & Bo Zeng, 2016. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5145-5161, November.
    5. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    6. Adil M. Bagirov & Arshad Mahmood, 2018. "A Comparative Assessment of Models to Predict Monthly Rainfall in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1777-1794, March.
    7. 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.
    8. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    9. 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.
    10. Chih-Chiang Wei & Nien-Sheng Hsu & Chien-Lin Huang, 2016. "Rainfall-Runoff Prediction Using Dynamic Typhoon Information and Surface Weather Characteristic Considering Monsoon Effects," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 877-895, January.

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