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All Journal Interdisciplinary International Journal of Conservation and Culture
Ali Nurdin
Universitas Galuh
Agriculture, Biological Sciences & Forestry Arts Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Social Sciences

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The integration of artificial intelligence in water conservation: A systematic literature review and thematic analysis Ali Nurdin; Rian Dwicahya Supriatman; Maulana Sidiq; Nana Yudi Permana
Interdisciplinary International Journal of Conservation and Culture Vol 4 No 1 (2026): April 2026
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/iijcc.v4i1.6033

Abstract

Water scarcity and mismanagement represent critical global challenges exacerbated by climate change and rapid urbanization. In response, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal technologies optimizing equitable water distribution, real-time monitoring, and sustainable conservation strategies. This study conducts a Systematic Literature Review (SLR) based on the PRISMA guidelines to synthesize the current landscape of computational AI applications targeting water conservation. From an initial identification of 1,449 records gathered from the Scopus database, stringent inclusion criteria filtered specifically for English publications within the Computer Science structural domain spanning the years 2015-2025. This uncompromising filtration yielded exactly 19 empirical, high-impact peer-reviewed articles for qualitative synthesis. A robust thematic analysis reveals three primary domains mapping this niche research architecture: (1) Precision Agriculture and Smart Irrigation Systems leveraging IoT, (2) Predictive Modeling and Real-time Grid Monitoring for urban leakages, and (3) Water Quality and Biodiversity Modeling. Finally, this review highlights critical future research directions, including the demand for explainable AI (XAI) models in hydrology, resolving data privacy and sparse data scenarios using federated learning, and integrating extreme-event climate indices into dynamic resource planning algorithms.
Co-Authors Maulana Sidiq Nana Yudi Permana Rian Dwicahya Supriatman