Journal of Hydrologic Engineering cover with an image of a water turbine on a blue background. The journal title, ASCE logo, and Environmental and Water Resources Institute logo are displayed as well.
Special Collection on AI/ML for Hydroclimatic Extremes in the Context of Hydraulic Design

Guest Editors:
Prof. (Dr.) Rajib Maity, Ph.D., FRMetS, Department of Civil Engineering, Indian Institute of Technology Kharagpur, West Bengal, India

Hydrological extremes (intense rainfall, floods, droughts) are increasing globally as one of the prominent manifestations of climate change. This Special Collection explores how Artificial Intelligence/Machine Learning (AI/ML) approaches can be used to analyze and model these extremes using the advantage of extensive data/simulation availability and enhanced computational power in the recent times. The focus of this Special Collection is on applying these advancements for better management of extreme hydrological phenomena in the context of hydraulic design.

Papers in this Collection

Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate
ORCID ID iconDebarghya Bhattacharyya; and ORCID ID iconUjjwal Saha, Ph.D.
Published online: September 29, 2023

Time-Varying Evaluation of Compound Drought and Hot Extremes in Machine Learning–Predicted Ensemble CMIP5 Future Climate: A Multivariate Multi-Index Approach
ORCID ID iconSushree Swagatika Swain, Aff.M.ASCE; Ashok Mishra; and ORCID ID iconChandranath Chatterjee
Published online: January 11, 2024

Application of Hybrid AI Models for Accurate Prediction of Scour Depths under Submerged Circular Vertical Jet
Sai Guguloth; ORCID ID iconManish Pandey, M.ASCE; and Manali Pal
Published online: March 19, 2024

Estimating Reservoir Sedimentation Using Machine Learning
ORCID ID iconAmanda L. Cox, M.ASCE; Deanna Meyer; ORCID ID iconAlejandra Botero-Acosta; ORCID ID iconVasit Sagan; ORCID ID iconIbrahim Demir; Marian Muste; Paul Boyd, M.ASCE; and Chandra Pathak, F.ASCE
Published online: April 17, 2024

Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models
ORCID ID iconAbdullah A. Alsumaiei, Ph.D.
Published online: July 8, 2024

Multivariate Analysis and Anomaly Detection of a US Reservoir Sedimentation Data Set
ORCID ID iconAlejandra Botero-Acosta; ORCID ID iconAmanda L. Cox, M.ASCE; ORCID ID iconVasit Sagan; ORCID ID iconIbrahim Demir; Marian Muste; Paul Boyd, M.ASCE; and Chandra Pathak, F.ASCE
Published online: July 18, 2024