IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v84y2006i3p240-248.html
   My bibliography  Save this article

Subsurface drainage performance study using SALTMOD and ANN models

Author

Listed:
  • Sarangi, A.
  • Singh, Man
  • Bhattacharya, A.K.
  • Singh, A.K.

Abstract

No abstract is available for this item.

Suggested Citation

  • Sarangi, A. & Singh, Man & Bhattacharya, A.K. & Singh, A.K., 2006. "Subsurface drainage performance study using SALTMOD and ANN models," Agricultural Water Management, Elsevier, vol. 84(3), pages 240-248, August.
    • Handle: RePEc:eee:agiwat:v:84:y:2006:i:3:p:240-248
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-3774(06)00071-0
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sarangi, A. & Bhattacharya, A.K., 2005. "Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India," Agricultural Water Management, Elsevier, vol. 78(3), pages 195-208, December.
    2. Sharma, V. & Negi, S. C. & Rudra, R. P. & Yang, S., 2003. "Neural networks for predicting nitrate-nitrogen in drainage water," Agricultural Water Management, Elsevier, vol. 63(3), pages 169-183, December.
    3. Tyagi, N. K. & Tyagi, K. C. & Pillai, N. N. & Willardson, L. S., 1993. "Decision support for irrigation system improvement in saline environment," Agricultural Water Management, Elsevier, vol. 23(4), pages 285-301, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Singh, Ajay, 2018. "Assessment of different strategies for managing the water resources problems of irrigated agriculture," Agricultural Water Management, Elsevier, vol. 208(C), pages 187-192.
    2. Lei, Guoqing & Zeng, Wenzhi & Yu, Jin & Huang, Jiesheng, 2023. "A comparison of physical-based and machine learning modeling for soil salt dynamics in crop fields," Agricultural Water Management, Elsevier, vol. 277(C).
    3. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
    4. Mao, Wei & Yang, Jinzhong & Zhu, Yan & Ye, Ming & Wu, Jingwei, 2017. "Loosely coupled SaltMod for simulating groundwater and salt dynamics under well-canal conjunctive irrigation in semi-arid areas," Agricultural Water Management, Elsevier, vol. 192(C), pages 209-220.
    5. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.
    6. Singh, Ajay, 2012. "Validation of SaltMod for a semi-arid part of northwest India and some options for control of waterlogging," Agricultural Water Management, Elsevier, vol. 115(C), pages 194-202.
    7. Yunquan Zhang & Peiling Yang, 2023. "A Simulation-Based Optimization Model for Control of Soil Salinization in the Hetao Irrigation District, Northwest China," Sustainability, MDPI, vol. 15(5), pages 1-20, March.

    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.
    1. Zou, Ping & Yang, Jingsong & Fu, Jianrong & Liu, Guangming & Li, Dongshun, 2010. "Artificial neural network and time series models for predicting soil salt and water content," Agricultural Water Management, Elsevier, vol. 97(12), pages 2009-2019, November.
    2. Liu, Xiaozhi & Kang, Shaozhong & Li, Fusheng, 2009. "Simulation of artificial neural network model for trunk sap flow of Pyrus pyrifolia and its comparison with multiple-linear regression," Agricultural Water Management, Elsevier, vol. 96(6), pages 939-945, June.
    3. Anctil, François & Filion, Mélanie & Tournebize, Julien, 2009. "A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment," Ecological Modelling, Elsevier, vol. 220(6), pages 879-887.
    4. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    5. Paresh Shirsath & Anil Singh, 2010. "A Comparative Study of Daily Pan Evaporation Estimation Using ANN, Regression and Climate Based Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(8), pages 1571-1581, June.
    6. Sarangi, A. & Bhattacharya, A.K., 2005. "Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India," Agricultural Water Management, Elsevier, vol. 78(3), pages 195-208, December.
    7. J. Patil & A. Sarangi & O. Singh & A. Singh & T. Ahmad, 2008. "Development of a GIS Interface for Estimation of Runoff from Watersheds," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(9), pages 1221-1239, September.
    8. Singh, Ajay & Krause, Peter & Panda, Sudhindra N. & Flugel, Wolfgang-Albert, 2010. "Rising water table: A threat to sustainable agriculture in an irrigated semi-arid region of Haryana, India," Agricultural Water Management, Elsevier, vol. 97(10), pages 1443-1451, October.
    9. Agnieszka Petryk & Edyta Kruk & Marek Ryczek & Lenka Lackóová, 2023. "Comparison of Pedotransfer Functions for Determination of Saturated Hydraulic Conductivity for Highly Eroded Loess Soil," Land, MDPI, vol. 12(3), pages 1-13, March.
    10. Zhang, WenJun & Zhang, XiYan, 2008. "Neural network modeling of survival dynamics of holometabolous insects: A case study," Ecological Modelling, Elsevier, vol. 211(3), pages 433-443.
    11. Tyagi, N. K., 2005. "Application of hydraulic and economic optimization for planning conjunctive use of surface and saline ground water: a case study," IWMI Books, Reports H029291, International Water Management Institute.
    12. 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.
    13. Zhang, WenJun & Bai, ChangJun & Liu, GuoDao, 2007. "Neural network modeling of ecosystems: A case study on cabbage growth system," Ecological Modelling, Elsevier, vol. 201(3), pages 317-325.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    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:84:y:2006:i:3:p:240-248. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.