Investigating groundwater viability for sustainable agriculture: Introducing the Irrigation Groundwater Viability Index (IGVI)

被引:0
作者
Makhlouf, Ahmed [1 ,2 ,3 ]
Kanae, Shinjiro [3 ]
Ibrahim, Mona G. [1 ,4 ]
El-Rawy, Mustafa [2 ,5 ]
Nada, Ali [1 ,3 ,6 ]
Sharaan, Mahmoud [1 ,7 ]
机构
[1] Egypt Japan Univ Sci & Technol, Dept Environm Engn, Alexandria 21934, Egypt
[2] Minia Univ, Fac Engn, Dept Civil Engn, Al Minya 61111, Egypt
[3] Tokyo Inst Technol, Dept Civil & Environm Engn, Tokyo, Japan
[4] Alexandria Univ, High Inst Publ Hlth, Environm Hlth Dept, Alexandria, Egypt
[5] Shaqra Univ, Coll Engn, Civil Engn Dept, Dawadmi 11911, Saudi Arabia
[6] Tanta Univ, Fac Engn, Dept Irrigat & Hydraul Engn, Tanta 31512, Egypt
[7] Suez Canal Univ, Fac Engn, Civil Engn Dept, Ismailia 41522, Egypt
来源
JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING | 2025年 / 13卷 / 03期
关键词
Irrigation Groundwater Viability Index (IGVI); Water Quality; Agricultural Productivity; And Gaussian Process Regression; NILE VALLEY; RECLAMATION; GOVERNORATE; AQUIFER; IMPACTS; NETWORK;
D O I
10.1016/j.jece.2025.117136
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Groundwater irrigation is essential for enhancing agricultural productivity, with its viability influenced by water quality and depth. Previous studies have often assessed these factors in isolation. This research introduces the Irrigation Groundwater Viability Index (IGVI) as a novel framework for evaluating the financial viability of groundwater resources, integrating both water quality and depth. Two hundred ninety-six groundwater samples were collected and analyzed from an agricultural reclamation project in Minia Governorate, Egypt. The Irrigation Water Quality Index (IWQI) was calculated, and samples were classified into five categories with corresponding weights. Additionally, samples were categorized by water table depth, resulting in five more categories. The IGVI was developed by integrating IWQI weights and depth classifications. Gaussian Process Regression (GPR) models, including Rational Quadratic, Mate<acute accent>rn 5/2, Squared Exponential, and Exponential Rational Quadratic, Mate<acute accent>rn 5/ 2, Squared Exponential, and Exponential, were employed to predict IGVI using measurable parameters, including water depth, distance to the river, electrical conductivity (EC), and total dissolved solids (TDS). Findings reveal that 53 % of agricultural reclamation areas exhibit moderate economic viability, while 30 % are classified as having poor viability and 17 % as good viability. The exponential GPR model showed the best predictive accuracy, with a mean absolute error (MAE) of 0.79 and a root mean square error (RMSE) of 1.14. Practically, the IGVI assists water resource stakeholders in making informed decisions, integrating groundwater chemistry with financial implications. This research supports sustainable agricultural practices, enhancing long-term water resource sustainability and food security by prioritizing groundwater quality preservation and optimizing extraction costs.
引用
收藏
页数:11
相关论文
共 61 条
[1]   Sandstone groundwater salinization modelling using physicochemical variables in Southern Saudi Arabia: Application of novel data intelligent algorithms [J].
Abba, S. I. ;
Benaafi, Mohammed ;
Usman, A. G. ;
Aljundi, Isam H. .
AIN SHAMS ENGINEERING JOURNAL, 2023, 14 (03)
[2]   Response of the interaction between surface water and groundwater to climate change and proposed megastructure [J].
Abdelhalim, Ahmed ;
Sefelnasr, Ahmed ;
Ismail, Esam .
JOURNAL OF AFRICAN EARTH SCIENCES, 2020, 162
[3]   Numerical modeling technique for groundwater management in Samalut city, Minia Governorate, Egypt [J].
Abdelhalim, Ahmed ;
Sefelnasr, Ahmed ;
Ismail, Esam .
ARABIAN JOURNAL OF GEOSCIENCES, 2019, 12 (04)
[4]   Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen [J].
Al-Mashreki, Mohammed Hezam ;
Eid, Mohamed Hamdy ;
Saeed, Omar ;
Szekacs, Andras ;
Szucs, Peter ;
Gad, Mohamed ;
Abukhadra, Mostafa R. ;
AlHammadi, Ali A. ;
Alrakhami, Mohammed Saleh ;
Alshabibi, Mubarak Ali ;
Elsayed, Salah ;
Khadr, Mosaad ;
Farouk, Mohamed ;
Ramadan, Hatem Saad .
WATER, 2023, 15 (08)
[5]  
Alam M.F., 2016, Evaluating the benefit-cost ratio of groundwater abstraction for additional irrigation water on global scale
[6]   Irrigation risk assessment of groundwater in a non-perennial river basin of South India: implication from irrigation water quality index (IWQI) and geographical information system (GIS) approaches [J].
Aravinthasamy, P. ;
Karunanidhi, D. ;
Rao, N. Subba ;
Subramani, T. ;
Srinivasamoorthy, K. .
ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (21)
[7]   Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe [J].
Asadollah, Seyed Babak Haji Seyed ;
Jodar-Abellan, Antonio ;
Pardo, Miguel Angel .
AGRICULTURAL SYSTEMS, 2024, 218
[8]   Groundwater vulnerability to climate change: A review of the assessment methodology [J].
Aslam, Rana Ammar ;
Shrestha, Sangam ;
Pandey, Vishnu Prasad .
SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 612 :853-875
[9]  
Aziz N.M.A., 2021, Groundwater management of eocene limestone Aquifer in the area between Beni Mazar and Mallawi West El Minia
[10]   Enhancement of Tunisian groundwater treatment through the simultaneous removal of sulfate, nitrate and nitrogen by-products using a hybrid electrochemical process [J].
Ben Arfa, Maha ;
Attour, Anis ;
Ursu, Alina-Violeta ;
Audonnet, Fabrice ;
Elfil, Hamza ;
Vial, Christophe .
JOURNAL OF WATER PROCESS ENGINEERING, 2024, 68