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Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques

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  • Sharda, V.N.
  • Patel, R.M.
  • Prasher, S.O.
  • Ojasvi, P.R.
  • Prakash, Chandra

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Suggested Citation

  • Sharda, V.N. & Patel, R.M. & Prasher, S.O. & Ojasvi, P.R. & Prakash, Chandra, 2006. "Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques," Agricultural Water Management, Elsevier, vol. 83(3), pages 233-242, June.
    • Handle: RePEc:eee:agiwat:v:83:y:2006:i:3:p:233-242
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    References listed on IDEAS

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    1. Peter Sephton, 2001. "Forecasting recessions: can we do better on MARS?," Review, Federal Reserve Bank of St. Louis, vol. 83(Mar), pages 39-49.
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    1. Zaher Mundher Yaseen & Ozgur Kisi & Vahdettin Demir, 2016. "Enhancing Long-Term Streamflow Forecasting and Predicting using Periodicity Data Component: Application of Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4125-4151, September.
    2. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    3. Kisi, Ozgur, 2016. "Modeling reference evapotranspiration using three different heuristic regression approaches," Agricultural Water Management, Elsevier, vol. 169(C), pages 162-172.
    4. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).

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