Improving irrigation management by modelling the irrigation schedule
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Cited by:
- Liangfeng Zou & Yuanyuan Zha & Yuqing Diao & Chi Tang & Wenquan Gu & Dongguo Shao, 2023. "Coupling the Causal Inference and Informer Networks for Short-term Forecasting in Irrigation Water Usage," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 427-449, January.
- Mouatadid, Soukayna & Adamowski, Jan F. & Tiwari, Mukesh K. & Quilty, John M., 2019. "Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting," Agricultural Water Management, Elsevier, vol. 219(C), pages 72-85.
- Forouhar, Leila & Wu, Wenyan & Wang, Q.J. & Hakala, Kirsti, 2022. "A hybrid framework for short-term irrigation demand forecasting," Agricultural Water Management, Elsevier, vol. 273(C).
- R. Perea & E. Poyato & P. Montesinos & J. Díaz, 2015. "Irrigation Demand Forecasting Using Artificial Neuro-Genetic Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5551-5567, December.
- Elmaloglou, St. & Malamos, N., 2000. "Simulation of soil moisture content of a prairie field with SWAP93," Agricultural Water Management, Elsevier, vol. 43(2), pages 139-149, March.
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