Rashid et al. (2026) The transformative role of artificial intelligence in water resources engineering: A comprehensive review

Identification

• Journal: Environmental Modelling & Software
• Year: 2026
• Date: 2026-01-06
• Authors: Muhammad Rashid, Adan Saeed, Mohiq Khalid, Aniqa Murtaza, Muhammad Waqar Saleem
• DOI: 10.1016/j.envsoft.2026.106857

Research Groups

• Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO, USA
• School of Civil and Environmental Engineering (SCEE), NUST Institute of Civil Engineering (NICE), National University of Sciences and Technology (NUST), Islamabad, Pakistan
• Department of Civil Engineering, University of Engineering & Technology, Lahore, Pakistan

Short Summary

This comprehensive review synthesizes the transformative role of artificial intelligence (AI) in water resources engineering, detailing its applications across the hydrologic cycle and highlighting both significant gains in predictive skill and operational efficiency, as well as persistent challenges like data scarcity and interpretability.

Objective

• To comprehensively review and synthesize how various artificial intelligence (AI) families, including machine learning, deep learning, fuzzy logic, and hybrid schemes, are being applied to address complex forecasting, control, and decision problems in water resources engineering.

Study Configuration

• Spatial Scale: Global review of applications across diverse water resources engineering domains.
• Temporal Scale: Synthesis of existing literature and applications over time, discussing current trends and future directions.

Methodology and Data

• Models used: This is a review paper discussing the application of various AI models, including: Machine Learning (supervised, unsupervised, reinforcement), Deep Learning (Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transformers), Fuzzy Logic, and Hybrid schemes.
• Data sources: Scientific literature (review paper). The paper also discusses the types of data used in AI applications (e.g., hydrological, water quality, climate data) and associated challenges (sparse, noisy data).

Main Results

• AI is extensively applied across water resources engineering for: streamflow and rainfall–runoff prediction, water-quality assessment, flood and drought risk management, precision irrigation, urban water operations (demand, leakage, treatment), groundwater management, climate-impact analysis, and reservoir scheduling.
• Reported benefits of AI integration include higher predictive skill, enhanced operational efficiency, and significant cost savings, contributing to more sustainable water management.
• Key constraints and challenges identified are: sparse and noisy data, limited interpretability of AI models, high deployment costs, skill barriers for implementation, and ethical concerns regarding bias, accountability, and privacy.
• Future priorities for AI in water resources engineering include: developing Explainable AI (XAI), creating resilient hybrid physics-ML models, tighter integration with Internet of Things (IoT) and remote sensing technologies, and establishing principled strategies to address data scarcity.

Contributions

• Provides a comprehensive and structured synthesis of the current landscape of AI applications in water resources engineering, categorizing diverse AI families and their specific uses.
• Identifies and articulates the significant benefits (e.g., predictive skill, efficiency, cost savings) derived from AI adoption in the water sector.
• Critically surfaces persistent constraints and challenges (e.g., data quality, interpretability, ethics) that hinder the full realization of AI's potential.
• Outlines crucial future research directions and priorities, advocating for rigorous, transparent, and interdisciplinary practices to ensure AI benefits water security and equity.

Funding

Not specified in the provided text.

Citation

@article{Rashid2026transformative,
  author = {Rashid, Muhammad and Saeed, Adan and Khalid, Mohiq and Murtaza, Aniqa and Saleem, Muhammad Waqar},
  title = {The transformative role of artificial intelligence in water resources engineering: A comprehensive review},
  journal = {Environmental Modelling & Software},
  year = {2026},
  doi = {10.1016/j.envsoft.2026.106857},
  url = {https://doi.org/10.1016/j.envsoft.2026.106857}
}