Application of machine learning approaches in supporting irrigation decision making: A review
Author
Abstract
Suggested Citation
DOI: 10.1016/j.agwat.2024.108710
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Yiqi Huang & Ruqi Li & Xiaotong Wei & Zhen Wang & Tianbei Ge & Xi Qiao, 2022. "Evaluating Data Augmentation Effects on the Recognition of Sugarcane Leaf Spot," Agriculture, MDPI, vol. 12(12), pages 1-19, November.
- Chen, Mengting & Cui, Yuanlai & Wang, Xiaonan & Xie, Hengwang & Liu, Fangping & Luo, Tongyuan & Zheng, Shizong & Luo, Yufeng, 2021. "A reinforcement learning approach to irrigation decision-making for rice using weather forecasts," Agricultural Water Management, Elsevier, vol. 250(C).
- N. Maier & J. Dietrich, 2016. "Using SWAT for Strategic Planning of Basin Scale Irrigation Control Policies: a Case Study from a Humid Region in Northern Germany," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3285-3298, July.
- Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
- Justin Sirignano & Konstantinos Spiliopoulos, 2017. "DGM: A deep learning algorithm for solving partial differential equations," Papers 1708.07469, arXiv.org, revised Sep 2018.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Imran Ali Lakhiar & Haofang Yan & Chuan Zhang & Guoqing Wang & Bin He & Beibei Hao & Yujing Han & Biyu Wang & Rongxuan Bao & Tabinda Naz Syed & Junaid Nawaz Chauhdary & Md. Rakibuzzaman, 2024. "A Review of Precision Irrigation Water-Saving Technology under Changing Climate for Enhancing Water Use Efficiency, Crop Yield, and Environmental Footprints," Agriculture, MDPI, vol. 14(7), pages 1-40, July.
- Kristina O. F. Williams & Benjamin F. Akers, 2023. "Numerical Simulation of the Korteweg–de Vries Equation with Machine Learning," Mathematics, MDPI, vol. 11(13), pages 1-14, June.
- Uniyal, Bhumika & Dietrich, Jörg & Vasilakos, Christos & Tzoraki, Ourania, 2017. "Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices," Agricultural Water Management, Elsevier, vol. 193(C), pages 55-70.
- William Lefebvre & Enzo Miller, 2021. "Linear-quadratic stochastic delayed control and deep learning resolution," Working Papers hal-03145949, HAL.
- Zhouzhou Gu & Mathieu Lauri`ere & Sebastian Merkel & Jonathan Payne, 2024. "Global Solutions to Master Equations for Continuous Time Heterogeneous Agent Macroeconomic Models," Papers 2406.13726, arXiv.org.
- Parand, K. & Aghaei, A.A. & Jani, M. & Ghodsi, A., 2021. "A new approach to the numerical solution of Fredholm integral equations using least squares-support vector regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 180(C), pages 114-128.
- William Lefebvre & Enzo Miller, 2021. "Linear-quadratic stochastic delayed control and deep learning resolution," Papers 2102.09851, arXiv.org, revised Feb 2021.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Deep Stochastic Optimization in Finance," Papers 2205.04604, arXiv.org.
- Sebastian Jaimungal, 2022. "Reinforcement learning and stochastic optimisation," Finance and Stochastics, Springer, vol. 26(1), pages 103-129, January.
- Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019.
"Pricing Options and Computing Implied Volatilities using Neural Networks,"
Risks, MDPI, vol. 7(1), pages 1-22, February.
- Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing options and computing implied volatilities using neural networks," Papers 1901.08943, arXiv.org, revised Apr 2019.
- Bastien Baldacci & Joffrey Derchu & Iuliia Manziuk, 2020. "An approximate solution for options market-making in high dimension," Papers 2009.00907, arXiv.org.
- Alexandre Pannier & Cristopher Salvi, 2024. "A path-dependent PDE solver based on signature kernels," Papers 2403.11738, arXiv.org, revised Oct 2024.
- Rong Du & Duy-Minh Dang, 2023. "Fourier Neural Network Approximation of Transition Densities in Finance," Papers 2309.03966, arXiv.org, revised Sep 2024.
- Ali Al-Aradi & Adolfo Correia & Danilo de Frietas Naiff & Gabriel Jardim & Yuri Saporito, 2019. "Extensions of the Deep Galerkin Method," Papers 1912.01455, arXiv.org, revised Apr 2022.
- Yuga Iguchi & Riu Naito & Yusuke Okano & Akihiko Takahashi & Toshihiro Yamada, 2021. "Deep Asymptotic Expansion: Application to Financial Mathematics," CIRJE F-Series CIRJE-F-1178, CIRJE, Faculty of Economics, University of Tokyo.
- Martin Hutzenthaler & Arnulf Jentzen & Thomas Kruse & Tuan Anh Nguyen, 2020. "A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations," Partial Differential Equations and Applications, Springer, vol. 1(2), pages 1-34, April.
- Sebastian Becker & Patrick Cheridito & Arnulf Jentzen, 2020. "Pricing and Hedging American-Style Options with Deep Learning," JRFM, MDPI, vol. 13(7), pages 1-12, July.
- Salah A. Faroughi & Ramin Soltanmohammadi & Pingki Datta & Seyed Kourosh Mahjour & Shirko Faroughi, 2023. "Physics-Informed Neural Networks with Periodic Activation Functions for Solute Transport in Heterogeneous Porous Media," Mathematics, MDPI, vol. 12(1), pages 1-23, December.
- Jiequn Han & Ruimeng Hu & Jihao Long, 2020. "Convergence of Deep Fictitious Play for Stochastic Differential Games," Papers 2008.05519, arXiv.org, revised Mar 2021.
- Chen, Mengting & Linker, Raphael & Wu, Conglin & Xie, Hua & Cui, Yuanlai & Luo, Yufeng & Lv, Xinwei & Zheng, Shizong, 2022. "Multi-objective optimization of rice irrigation modes using ACOP-Rice model and historical meteorological data," Agricultural Water Management, Elsevier, vol. 272(C).
More about this item
Keywords
Machine Learning; Irrigation Water Use Modeling; Water Management; FAIR data;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:294:y:2024:i:c:s0378377424000453. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.