Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across ke...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Applied Computing and Geosciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197424000533 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850065565047062528 |
|---|---|
| author | Rajib Maity Aman Srivastava Subharthi Sarkar Mohd Imran Khan |
| author_facet | Rajib Maity Aman Srivastava Subharthi Sarkar Mohd Imran Khan |
| author_sort | Rajib Maity |
| collection | DOAJ |
| description | Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction. |
| format | Article |
| id | doaj-art-a43b8f1ae1714f1eb5b348455c8f5cf4 |
| institution | DOAJ |
| issn | 2590-1974 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-a43b8f1ae1714f1eb5b348455c8f5cf42025-08-20T02:48:58ZengElsevierApplied Computing and Geosciences2590-19742024-12-012410020610.1016/j.acags.2024.100206Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learningRajib Maity0Aman Srivastava1Subharthi Sarkar2Mohd Imran Khan3Corresponding author.; Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, IndiaDepartment of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, IndiaDepartment of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, IndiaDepartment of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, IndiaArtificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing hydrology, driving significant advancements in water resource management, modeling, and prediction. This review synthesizes cutting-edge developments, methodologies, and applications of AI-ML-DL across key hydrological processes. By critically evaluating these techniques against traditional models, we highlight their superior ability to capture complex, nonlinear relationships and adapt to diverse environments. We further explore AI applications in precipitation forecasting, evapotranspiration estimation, groundwater dynamics, and extreme event prediction (floods, droughts, and compound events), showcasing their timely potential in addressing critical water-related challenges. A particular emphasis is placed on Explainable AI (XAI) and transfer learning as essential tools for improving model transparency and applicability, enabling broader stakeholder trust and cross-regional adaptability. The review also addresses persistent challenges, including data limitations, computational demands, and model interpretability, proposing solutions that integrate emerging technologies like quantum computing, the Internet of Things (IoT), and interdisciplinary collaboration. This review charts a strategic course for future research and practice by bridging AI advancements with practical hydrological applications. Our findings highlight the importance of embracing AI-driven approaches for next-generation hydrological modeling and provide actionable understandings for researchers, practitioners, and policymakers. As hydrology faces escalating challenges due to human-induced climate change and growing water demands, the continued evolution of AI-integrated models and innovations in data handling and stakeholder engagement will be imperative. In conclusion, the findings emphasize the critical role of AI-driven hydrological modeling in addressing global water challenges, including climate change adaptation, sustainable water resource management, and disaster risk reduction.http://www.sciencedirect.com/science/article/pii/S2590197424000533Climate changeWater resources managementEngineering hydrologyHydrological modelingReal-time forecastingDisaster risk reduction |
| spellingShingle | Rajib Maity Aman Srivastava Subharthi Sarkar Mohd Imran Khan Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning Applied Computing and Geosciences Climate change Water resources management Engineering hydrology Hydrological modeling Real-time forecasting Disaster risk reduction |
| title | Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning |
| title_full | Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning |
| title_fullStr | Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning |
| title_full_unstemmed | Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning |
| title_short | Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning |
| title_sort | revolutionizing the future of hydrological science impact of machine learning and deep learning amidst emerging explainable ai and transfer learning |
| topic | Climate change Water resources management Engineering hydrology Hydrological modeling Real-time forecasting Disaster risk reduction |
| url | http://www.sciencedirect.com/science/article/pii/S2590197424000533 |
| work_keys_str_mv | AT rajibmaity revolutionizingthefutureofhydrologicalscienceimpactofmachinelearninganddeeplearningamidstemergingexplainableaiandtransferlearning AT amansrivastava revolutionizingthefutureofhydrologicalscienceimpactofmachinelearninganddeeplearningamidstemergingexplainableaiandtransferlearning AT subharthisarkar revolutionizingthefutureofhydrologicalscienceimpactofmachinelearninganddeeplearningamidstemergingexplainableaiandtransferlearning AT mohdimrankhan revolutionizingthefutureofhydrologicalscienceimpactofmachinelearninganddeeplearningamidstemergingexplainableaiandtransferlearning |