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Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

Author

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  • Yu, Haijiao
  • Wen, Xiaohu
  • Wu, Min
  • Sheng, Danrui
  • Wu, Jun
  • Zhao, Ying

Abstract

Accurate groundwater quality estimation is of great significance for effective irrigation management in agricultural areas. However, due to lack of data, the parameterization of the physical models is restricted for large areas with diverse underlying surface conditions. To obtain effective and convenient estimations of groundwater quality with the most accessible data is urgently needed. In this study, the validity of the data-based models in irrigation water quality index estimation was investigated by only using physical groundwater parameters as inputs. 15 combination scenarios of the physical parameters including temperature, pH, electrical conductivity, and dissolved oxygen were examined by support vector machine (SVM), random forests (RF), artificial neural networks (ANN) and extreme learning machine (ELM) models for total dissolved solids (TDS), potential salinity (PS), and sodium adsorption ratio (SAR) estimation for the Zhangye Basin, northwest China. Performance of the artificial intelligence (AI) models was evaluated according to the coefficient of correlation (R), root mean squared error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NS). The Monte Carlo (MC) approach was performed to assess the uncertainty of the physical groundwater parameters and the sensitivity of the AI models. The results revealed an input pattern, which emphasized the important role of EC and the improving function of pH, in revealing the nature of data-based irrigation water quality index estimation. Results of the uncertainty analysis also confirmed the prominent superiority and robustness of the SVM, RF and ELM models in producing excellent estimations with only physical parameters as inputs. The developed AI models were valuable in estimating irrigation water quality indexes, thus could help decision makers manage irrigation strategies. By using physical groundwater parameters as input variables, the AI models showed prospects in convenient and cost-effective irrigation water quality index estimating.

Suggested Citation

  • Yu, Haijiao & Wen, Xiaohu & Wu, Min & Sheng, Danrui & Wu, Jun & Zhao, Ying, 2022. "Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture," Agricultural Water Management, Elsevier, vol. 262(C).
    • Handle: RePEc:eee:agiwat:v:262:y:2022:i:c:s0378377421007009
    DOI: 10.1016/j.agwat.2021.107423
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    1. Nosetto, M.D. & Acosta, A.M. & Jayawickreme, D.H. & Ballesteros, S.I. & Jackson, R.B. & Jobbágy, E.G., 2013. "Land-use and topography shape soil and groundwater salinity in central Argentina," Agricultural Water Management, Elsevier, vol. 129(C), pages 120-129.
    2. Zhu, Guofeng & Yong, Leilei & Zhang, Zhuanxia & Sun, Zhigang & Sang, Liyuan & Liu, Yuwei & Wang, Lei & Guo, Huiwen, 2021. "Infiltration process of irrigation water in oasis farmland and its enlightenment to optimization of irrigation mode: Based on stable isotope data," Agricultural Water Management, Elsevier, vol. 258(C).
    3. Qi Feng & Linshan Yang & Ravinesh C. Deo & Amir AghaKouchak & Jan F. Adamowski & Roger Stone & Zhenliang Yin & Wei Liu & Jianhua Si & Xiaohu Wen & Meng Zhu & Shixiong Cao, 2019. "Domino effect of climate change over two millennia in ancient China’s Hexi Corridor," Nature Sustainability, Nature, vol. 2(10), pages 957-961, October.
    4. Jiang, Song & Meng, Jijun & Zhu, Likai, 2020. "Spatial and temporal analyses of potential land use conflict under the constraints of water resources in the middle reaches of the Heihe River," Land Use Policy, Elsevier, vol. 97(C).
    5. Li, Xiaolin & Tong, Ling & Niu, Jun & Kang, Shaozhong & Du, Taisheng & Li, Sien & Ding, Risheng, 2017. "Spatio-temporal distribution of irrigation water productivity and its driving factors for cereal crops in Hexi Corridor, Northwest China," Agricultural Water Management, Elsevier, vol. 179(C), pages 55-63.
    6. Liu, Minghuan & Jiang, Yao & Xu, Xu & Huang, Quanzhong & Huo, Zailin & Huang, Guanhua, 2018. "Long-term groundwater dynamics affected by intense agricultural activities in oasis areas of arid inland river basins, Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 37-52.
    7. Wang, Tianyu & Wang, Zhenhua & Guo, Li & Zhang, Jinzhu & Li, Wenhao & He, Huaijie & Zong, Rui & Wang, Dongwang & Jia, Zhecheng & Wen, Yue, 2021. "Experiences and challenges of agricultural development in an artificial oasis: A review," Agricultural Systems, Elsevier, vol. 193(C).
    8. El Bilali, Ali & Taleb, Abdeslam & Brouziyne, Youssef, 2021. "Groundwater quality forecasting using machine learning algorithms for irrigation purposes," Agricultural Water Management, Elsevier, vol. 245(C).
    9. Ali, Aram & Bennett, John McL & Biggs, Andrew A.J. & Marchuk, Alla & Ghahramani, Afshin, 2021. "Assessing the hydraulic reduction performance of HYDRUS-1D for application of alkaline irrigation in variably-saturated soils: Validation of pH driven hydraulic reduction scaling factors," Agricultural Water Management, Elsevier, vol. 256(C).
    Full references (including those not matched with items on IDEAS)

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