Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area
Author
Abstract
Suggested Citation
DOI: 10.1016/j.agwat.2020.106121
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
- Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
- Horne, D.J. & Scotter, D.R., 2016. "The available water holding capacity of soils under pasture," Agricultural Water Management, Elsevier, vol. 177(C), pages 165-171.
- de Fraiture, Charlotte & Wichelns, Dennis, 2010. "Satisfying future water demands for agriculture," Agricultural Water Management, Elsevier, vol. 97(4), pages 502-511, April.
- Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
- Givi, J. & Prasher, S.O. & Patel, R.M., 2004. "Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point," Agricultural Water Management, Elsevier, vol. 70(2), pages 83-96, November.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Hamza Negiş, 2023. "Using Models and Artificial Neural Networks to Predict Soil Compaction Based on Textural Properties of Soils under Agriculture," Agriculture, MDPI, vol. 14(1), pages 1-14, December.
- Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.
- Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
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.- Mattar, M.A. & Alazba, A.A. & Zin El-Abedin, T.K., 2015. "Forecasting furrow irrigation infiltration using artificial neural networks," Agricultural Water Management, Elsevier, vol. 148(C), pages 63-71.
- Al Zayed, Islam Sabry & Elagib, Nadir Ahmed & Ribbe, Lars & Heinrich, Jürgen, 2016. "Satellite-based evapotranspiration over Gezira Irrigation Scheme, Sudan: A comparative study," Agricultural Water Management, Elsevier, vol. 177(C), pages 66-76.
- Tuba Tanyıldızı Ağır, 2024. "Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
- Lankford, B. & Makin, Ian & Matthews, N. & McCornick, Peter G. & Noble, A. & Shah, Tushaar, "undated". "A compact to revitalise large-scale irrigation systems using a leadership-partnership-ownership 'Theory of Change'," Papers published in Journals (Open Access) H047459, International Water Management Institute.
- Zhaohong Wu & Wenyuan Hua & Liangguo Luo & Katsuya Tanaka, 2022. "Technical Efficiency of Maize Production and Its Influencing Factors in the World’s Largest Groundwater Drop Funnel Area, China," Agriculture, MDPI, vol. 12(5), pages 1-14, April.
- Hanjra, Munir A. & Qureshi, M. Ejaz, 2010. "Global water crisis and future food security in an era of climate change," Food Policy, Elsevier, vol. 35(5), pages 365-377, October.
- Patricio Vladimir Méndez-Zambrano & Luis Patricio Tierra Pérez & Rogelio Estalin Ureta Valdez & Ángel Patricio Flores Orozco, 2023. "Technological Innovations for Agricultural Production from an Environmental Perspective: A Review," Sustainability, MDPI, vol. 15(22), pages 1-15, November.
- Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
- Rinaudo, Jean-Daniel & Maton, Laure & Terrason, Isabelle & Chazot, Sébastien & Richard-Ferroudji, Audrey & Caballero, Yvan, 2013. "Combining scenario workshops with modeling to assess future irrigation water demands," Agricultural Water Management, Elsevier, vol. 130(C), pages 103-112.
- Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).
- Łukasz Borek & Andrzej Bogdał & Tomasz Kowalik, 2021. "Use of Pedotransfer Functions in the Rosetta Model to Determine Saturated Hydraulic Conductivity (Ks) of Arable Soils: A Case Study," Land, MDPI, vol. 10(9), pages 1-22, September.
- Lan Anh Tong & Mehmet Ali Ulubaşoğlu & Cahit Guven, 2022.
"Growing more Rice with less water: the System of Rice Intensification and water productivity in Vietnam,"
Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(3), pages 581-611, July.
- Tong, Lan Anh & Ulubasoglu, Mehmet Ali & Guven, Cahit, 2022. "Growing more Rice with less water: the System of Rice Intensification and water productivity in Vietnam," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(03), January.
- Piñeiro-Chousa, Juan & López-Cabarcos, M.Ángeles & Ribeiro-Soriano, Domingo, 2020. "Does investor attention influence water companies’ stock returns?," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
- Eric Njuki & Boris E. Bravo-Ureta, 2019. "Examining irrigation productivity in U.S. agriculture using a single-factor approach," Journal of Productivity Analysis, Springer, vol. 51(2), pages 125-136, June.
- Feng, Yu & Jia, Yue & Cui, Ningbo & Zhao, Lu & Li, Chen & Gong, Daozhi, 2017. "Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China," Agricultural Water Management, Elsevier, vol. 181(C), pages 1-9.
- Tiffany L. Fess & James B. Kotcon & Vagner A. Benedito, 2011. "Crop Breeding for Low Input Agriculture: A Sustainable Response to Feed a Growing World Population," Sustainability, MDPI, vol. 3(10), pages 1-31, October.
- María Blanco & Benjamin Van Doorslaer & Wolfgang Britz & Heinz-Peter Witzke, 2012. "Exploring the feasibility of integrating water issues into the CAPRI model," JRC Research Reports JRC77058, Joint Research Centre.
- Houshang Ghamarnia & Vahid Rezvani & Erfan Khodaei & Hossein Mirzaei, 2012. "Time and Place Calibration of the Hargreaves Equation for Estimating Monthly Reference Evapotranspiration under Different Climatic Conditions," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 4(3), pages 111-111, January.
- Mondol, Md Anarul Haque & Zhu, Xuan & Dunkerley, David & Henley, Benjamin J., 2022. "Changing occurrence of crop water surplus or deficit and the impact of irrigation: An analysis highlighting consequences for rice production in Bangladesh," Agricultural Water Management, Elsevier, vol. 269(C).
- Seydou Traore & Yufeng Luo & Guy Fipps, 2017. "Gene-Expression Programming for Short-Term Forecasting of Daily Reference Evapotranspiration Using Public Weather Forecast Information," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4891-4908, December.
More about this item
Keywords
Field capacity; Permanent wilting point; Deep learning; Artificial neural network; k-nearest neighbour;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:234:y:2020:i:c:s037837741932356x. 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.