AI & Environment Resource HubHasib Khan; Wafa F. Alfwzan; Rabia Latif; Jehad Alzabut; Rajermani Thinakaran · 2025-05-30
This study presents a hybrid AI and mathematical modeling approach using deep learning and fractional-order equations to forecast water cycle dynamics and inform sustainable water and climate policy decisions.
Hasib Khan; Wafa F. Alfwzan; Rabia Latif; Jehad Alzabut; Rajermani Thinakaran. 2025-05-30. AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change. https://www.mdpi.com/2504-3110/9/6/361 (AI & Environment Resource Hub; record paper-298; collection snapshot 2026-09-15).
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Source sheet: Scientific Paper · Row 302 · Original ID: paper-298.
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Shared topics: Cross-cutting sustainability; Governance and society; Water; Oceans and coasts
We believe that innovation and technological advancement are key to building 21st-century economies in the region. We partner with diverse organizations to fostering economic growth, social progress, and environmental sustainability.
Shared topics: Cross-cutting sustainability; Governance and society; Water; Oceans and coasts
Artificial Intelligence (AI) has the potential to transform how water is managed. Yet, these emerging opportunities raise critical considerations around issues such as data governance, energy use, and the environmental footprint of digital infrastructure. To explore these challenges and opportunities, IWRA is launching a new webinar series: "The Promises and Challenges of AI, Data Centres & Freshwater Futures". Bringing together experts from around the world, the series examines the intersection of emerging technologies and water sustainability.
Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Oceans and coasts
AI and ML optimize optoelectronic systems for sustainability by enhancing energy efficiency, renewable energy harvesting, environmental monitoring, and smart cities while also advancing ocean optics and photonics for marine ecosystem monitoring and climate change mitigation.
Shared topics: Cross-cutting sustainability; Governance and society; Climate and greenhouse gases; Water
This paper argues that AI's environmental impacts from energy consumption and water use to mineral extraction constitute a global climate justice concern that demands moving beyond efficiency metrics to center the unequal distribution of costs and benefits, particularly in the Global South.