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1 Ismail, Z.; Go, Y. I. 2021. Fog-to-water for water scarcity in climate-change hazards hotspots: pilot study in Southeast Asia. Global Challenges, 2000036. (Online first) [doi: https://doi.org/10.1002/gch2.202000036]

Water scarcity ; Water harvesting ; Fog ; Freshwater ; Climate change ; Vulnerability ; Drought stress ; Drinking water ; Sustainability ; Water supply ; Water demand ; Population growth ; Sanitation ; Economic aspects / South East Asia

(Location: IWMI HQ Call no: e-copy only Record No: H050253)

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/gch2.202000036?download=true
https://vlibrary.iwmi.org/pdf/H050253.pdf

(1.50 MB) (1.50 MB)

Water is indispensable for human survival. Freshwater scarcity and unsustainable water are the main growing concerns in the world. It is estimated that about 800 million people worldwide do not have basic access to drinking water and about 2.2 billion people do not have access to safe water supply. Southeast Asia is most likely to experience water scarcity and water demand as a result of climate change. Climate change and the increasing water demand that eventually contribute to water scarcity are focused upon here. For Southeast Asia to adapt to the adverse consequences of global climate change and the growing concern of environmental water demand, fog water harvesting is considered as the most promising method to overcome water scarcity or drought. Fog water collection technique is a passive, low maintenance, and sustainable option that can supply fresh drinking water to communities where fog is a common phenomenon. Fog water harvesting system involves the use of mesh nets to collect water as fog passes through them. Only minimal cost is required for the operation and maintenance. In conclusion, fog water harvesting seems to be a promising method that can be implemented to overcome water scarcity and water demand in Southeast Asia.

2 Nordin, N. F. C.; Mohd, N. S.; Koting, S.; Ismail, Z.; Sherif, M.; El-Shafie, A. 2021. Groundwater quality forecasting modelling using artificial intelligence: a review. Groundwater for Sustainable Development, 14:100643. [doi: https://doi.org/10.1016/j.gsd.2021.100643]

Water quality ; Groundwater table ; Forecasting ; Modelling ; Artificial intelligence ; Neural networks ; Water levels ; Contamination ; Uncertainty

(Location: IWMI HQ Call no: e-copy only Record No: H050642)

https://www.sciencedirect.com/science/article/pii/S2352801X21001004/pdfft?md5=bcb01ac39baa7de9c7e0899dec4c6595&pid=1-s2.0-S2352801X21001004-main.pdf
https://vlibrary.iwmi.org/pdf/H050642.pdf

(2.25 MB) (2.25 MB)

This review paper closely explores the techniques and significances of the most potent artificial intelligence (AI) approaches in a concise and integrated way, specifically in the groundwater quality modelling and forecasting for its suitability in domestic usage. This paper systematically provides an extensive review of the four most used AI methods: artificial neural network (ANN), adaptive network-based fuzzy inference system (ANFIS), evolutionary algorithm (EA) and support vector machine (SVM), to reflect on the features and abilities while defining the greatest challenges throughout the process of providing desired results. Analysis among the four AI methods found that ANN performed better when handling a large number of data sets and accurately made predictions due to its ability to model complex non-linear and complex relationships, despite some weaknesses. The findings of this review demonstrate that the successful adoption of AI models is impacted by the appropriateness of input consideration, types of individual functions, the efficiency of performance metrics, etc. The outcomes from this study will be beneficial for groundwater development plans and contribute to the improvement of the AI applications in groundwater quality. Recommendations are presented in this study to strengthen the knowledge development towards improving the modelling structure in the mentioned area.