References
Hussain, M.I., A. Muscolo, M. Farooq, and W. Ahmad, Sustainable use and management of non-conventional water resources for rehabilitation of marginal lands in arid and semiarid environments. Agricultural water management, 2019. 221: p. 462- Falkenmark, M., J. Lundqvist, and C. Widstrand. Macro?scale water scarcity requires micro?scale approaches: Aspects of vulnerability in semi?arid development. In the Natural Resources forum. 1989. Wiley Online Library. Bortolini, L., C. Maucieri, and M. Borin, A tool for the evaluation of irrigation water quality in the arid and semi-arid regions. Agronomy, 2018. 8(2): p. 23. Giri, S., Water quality prospective in Twenty-First Century: Status of water quality in major river basins, contemporary strategies and impediments: A review. Environmental Pollution, 2021. 271: p. 116332. Lowe, M., R. Qin, and X. Mao, A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water, 2022. 14(9): p. 1384. Li, L., S. Rong, R. Wang, and S. Yu, Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review. Chemical Engineering Journal, 2021. 405: p. 126673. Priyan, K., Issues and challenges of groundwater and surface water management in semi-arid regions. Groundwater resources development and planning in the semi-arid region, 2021: p. 1-17. Seth, S., Human impacts and management issues in arid and semi-arid regions. International Contributions to Hydrogeology, 2003. 23: p. 289-341. Shevah, Y., Water resources, water scarcity challenges, and perspectives. Water challenges and solutions on a global scale, 2015: p. 185-219. Hanjra, M.A. and M.E. Qureshi, Global water crisis and future food security in an era of climate change. Food policy, 2010. 35(5): p. 365-377. Ewuzie, U., O.P. Bolade, and A.O. Egbedina, Application of deep learning and machine learning methods in water quality modelling and prediction: a review. Current trends and advances in computer-aided intelligent environmental data engineering, 2022: p. 185-218. Drogkoula, M., K. Kokkinos, and N. Samaras, A comprehensive survey of machine learning methodologies with emphasis in water resources management. Applied Sciences, 2023. 13(22): p. 12147. Ighalo, J.O., A.G. Adeniyi, and G. Marques, Internet of things for water quality monitoring and assessment: a comprehensive review. Artificial intelligence for sustainable development: theory, practice and future applications, 2021: p. 245-259. Prapti, D.R., A.R. Mohamed Shariff, H. Che Man, N.M. Ramli, T. Perumal, and M. Shariff, Internet of Things (IoT)?based aquaculture: An overview of IoT application on water quality monitoring. Reviews in Aquaculture, 2022. 14(2): p. 979-992. Tung, T.M. and Z.M. Yaseen, A survey on river water quality modelling using artificial intelligence models: 2000–2020. Journal of Hydrology, 2020. 585: p. 124670. Schiff, D., B. Rakova, A. Ayesh, A. Fanti, and M. Lennon, Principles to practices for responsible AI: closing the gap. arXiv preprint arXiv:2006.04707, 2020. Peksa, J. and D. Mamchur, A Review on the State of the Art in Copter Drones and Flight Control Systems. Sensors, 2024. 24(11): p. 3349. Ijlil, S., A. Essahlaoui, M. Mohajane, N. Essahlaoui, E.M. Mili, and A. Van Rompaey, Machine learning algorithms for modelling and mapping of groundwater pollution risk: A study to reach water security and sustainable development (Sdg) goals in a Mediterranean aquifer system. Remote Sensing, 2022. 14(10): p. 2379. Fernández-Cirelli, A., J.L. Arumí, D. Rivera, and P.W. Boochs, Environmental effects of irrigation in arid and semi-arid regions. Chilean Journal of Agricultural Research 69 (2009), Nr. SUPPL. 1, 2009. 69(SUPPL. 1): p. 27-40. Gutiérrez, M., R.N. Biagioni, M.T. Alarcón-Herrera, and B.A. Rivas-Lucero, An overview of nitrate sources and operating processes in arid and semiarid aquifer systems. Science of the total environment, 2018. 624: p. 1513-1522. Sonone, S.S., S. Jadhav, M.S. Sankhla, and R. Kumar, Water contamination by heavy metals and their toxic effect on aquaculture and human health through food Chain. Lett. Appl. NanoBioScience, 2020. 10(2): p. 2148-2166. Dong, S., B. Liu, Y. Chen, M. Ma, X. Liu, and C. Wang, Hydro-geochemical control of high arsenic and fluoride groundwater in arid and semi-arid areas: a case study of Tumochuan Plain, China. Chemosphere, 2022. 301: p. 134657. Mandal, R., A. Das, A. Sudheer, S. Kumar, S. Verma, M. Gaddam, and R. Deshpande, Sources, controls, and probabilistic health risk assessment of fluoride contamination in groundwater from a semi-arid region in Gujarat, Western India: An isotope– hydrogeochemical perspective. Environmental Geochemistry and Health, 2021: p. 1- Ludwig, F. and M. Moench, The impacts of climate change on water. Climate change adaptation in the water sector, 2012: p. 35-50. Godebo, T.R., M.A. Jeuland, C.J. Paul, D.L. Belachew, and P.G. McCormick, Water quality threats, perceptions of climate change and behavioural responses among farmers in the Ethiopian Rift Valley. Climate, 2021. 9(6): p. 92. Iyiola, A.O., A.J. Akinrinade, and F.O. Ajayi, Effects of Water pollution on biodiversity along the coastal regions, in Biodiversity in Africa: Potentials, Threats and Conservation. 2022, Springer. p. 345-367. Chakraborty, S.K. and S.K. Chakraborty, River pollution and perturbation: Perspectives and processes. Riverine Ecology Volume 2: Biodiversity Conservation, Conflicts and Resolution, 2021: p. 443-530. Nabinejad, S. and H. Schüttrumpf, Flood risk management in arid and semi-arid areas: a comprehensive review of challenges, needs, and opportunities. Water, 2023. 15(17): p. 3113. Ribot, J.C., A. Najam, and G. Watson, Climate variation, vulnerability and sustainable development in the semi-arid tropics. Climate Variability, Climate Change and Social Vulnerability in the Semi-Arid Tropics, Cambridge University Press, Cambridge, UK, Zhang, Y., A. Tariq, A.C. Hughes, D. Hong, F. Wei, H. Sun, J. Sardans, J. Peñuelas, G. Perry, and J. Qiao, Challenges and solutions to biodiversity conservation in arid lands. Science of the Total Environment, 2023. 857: p. 159695. Sakadevan, K. and M.-L. Nguyen, Extent, impact, and response to soil and water salinity in arid and semiarid regions. Advances in agronomy, 2010. 109: p. 55-74. Zahoor, I. and A. Mushtaq, Water pollution from agricultural activities: A critical global review. Int. J. Chem. Biochem. Sci, 2023. 23(1): p. 164-176. Akhtar, N., M.I. Syakir Ishak, S.A. Bhawani, and K. Umar, Various Natural and Anthropogenic Factors Responsible for Water Quality Degradation: A Review. Water, 13(19): p. 2660. Rahman, Z. and V.P. Singh, The relative impact of toxic heavy metals (THMs)(arsenic (As), cadmium (Cd), chromium (Cr)(VI), mercury (Hg), and lead (Pb)) on the total environment: an overview. Environmental monitoring and assessment, 2019. 191: p. 1- Obasi, P.N. and B.B. Akudinobi, Potential health risk and levels of heavy metals in water resources of lead–zinc mining communities of Abakaliki, southeast Nigeria. Applied Water Science, 2020. 10(7): p. 1-23. Valipour, M. and V.P. Singh, Global experiences on wastewater irrigation: challenges and prospects. Balanced urban development: options and strategies for liveable cities, 2016: p. 289-327. Elgallal, M., L. Fletcher, and B. Evans, Assessment of potential risks associated with chemicals in wastewater used for irrigation in arid and semiarid zones: A review. Agricultural Water Management, 2016. 177: p. 419-431. Oladiran, G., S. Raheem, A. Okeniyi, and A. Busari, Effect of solid waste landfill leachates on groundwater quality. Global Journal of Engineering and Technology Advances, 2023. 14(3): p. 205-216. Singh, S., N.J. Raju, W. Gossel, and P. Wy
