Dr. Roshan Karan Srivastav is an Associate Professor in the Department of Civil and Environmental Engineering at IIT Tirupati, serving since June 2018. He holds a Ph.D. in Water Resources Management from IIT Madras, a M.Tech in Water Resources Management from Motilal Nehru National Institute of Technology, and a B.E in Civil Engineering from University College of Engineering, Osmania University. His extensive research encompasses hydro-climatology, flood forecasting, reservoir operation, and stochastic hydrology. Before his tenure at IIT Tirupati, he was a Post-Doctoral Fellow at the University of Western Ontario, focusing on weather generators, system dynamics, and integrated water resources management. Dr. Srivastav has made significant contributions to the field through numerous publications in top-tier journals and international conferences. He also serves as the Project Director for the Technology Innovation Hub at IIT Tirupati Navavishkar I-Hub Foundation (IITTNiF), where he leads initiatives in positioning and precision technologies.Dr P. C. Nayak is Scientist F in Surface Water Hydrology Division at National Institute of Hydrology, Roorkee. Dr. Nayak hold a B. Tech in Civil Engineering from College of Engineering and Technology, Bhubaneswar and M. Tech in Water Resources Engineering from Indian Institute of Technology, Kharagpur, and Ph.D in Water Resources Engineering from Indian Institute of Technology Madras, Chennai. Dr. Nayak has more than 25 years of experience in the field of water resources, surface water hydrological modelling, flood management and application of machine learning techniques and his major research contributions include development of models and software tools for hydrologic modeling using conceptual, distributed and data driven algorithm. Soft computing has to date been the main topic of investigation, in particular exploring the potential benefits and pitfalls. He has published more than 150 research papers in journals as well as book chapters and technical reports. He was a core committee member in development of Decision Support System (DSS) funded by World Bank. He was serving as Associate Editor in Journal of Hydrology, Elsevier Sciences.

• Applications of Physics-guided Machine Learning Architectures in Hydrology.- A Review of Approaches and Applications for Streamflow Forecasting Using AI-based Models.- Estimation Of Groundwater Levels Using Machine Learning Techniques.- River Discharge Forecasting in Mahanadi River Basin Based On Deep Learning Techniques.- Machine Learning models for Groundwater Level Prediction.- Genetic Algorithm-Aided Neural Network for Sediment Critical Shear Stress Modeling.- An Integrative Approach for Oxygen Demand-based Stream Water Quality Modelling using QUAL2K-ANN Interactions.- Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA.- The High Resolution Statistical Downscaling of Seasonal Rainfall Forecasts Models for Comprehensive Evaluation of Hybrid Gamma Distribution for Districts of West Bengal, India.- Prediction of Rainfall in One of the Wettest Regions in India using Machine Learning Methods.

Dr. Roshan Karan Srivastav is an Associate Professor in the Department of Civil and Environmental Engineering at IIT Tirupati, serving since June 2018. He holds a Ph.D. in Water Resources Management from IIT Madras, a M.Tech in Water Resources Management from Motilal Nehru National Institute of Technology, and a B.E in Civil Engineering from University College of Engineering, Osmania University. His extensive research encompasses hydro-climatology, flood forecasting, reservoir operation, and stochastic hydrology. Before his tenure at IIT Tirupati, he was a Post-Doctoral Fellow at the University of Western Ontario, focusing on weather generators, system dynamics, and integrated water resources management. Dr. Srivastav has made significant contributions to the field through numerous publications in top-tier journals and international conferences. He also serves as the Project Director for the Technology Innovation Hub at IIT Tirupati Navavishkar I-Hub Foundation (IITTNiF), where he leads initiatives in positioning and precision technologies.Dr P. C. Nayak is Scientist F in Surface Water Hydrology Division at National Institute of Hydrology, Roorkee. Dr. Nayak hold a B. Tech in Civil Engineering from College of Engineering and Technology, Bhubaneswar and M. Tech in Water Resources Engineering from Indian Institute of Technology, Kharagpur, and Ph.D in Water Resources Engineering from Indian Institute of Technology Madras, Chennai. Dr. Nayak has more than 25 years of experience in the field of water resources, surface water hydrological modelling, flood management and application of machine learning techniques and his major research contributions include development of models and software tools for hydrologic modeling using conceptual, distributed and data driven algorithm. Soft computing has to date been the main topic of investigation, in particular exploring the potential benefits and pitfalls. He has published more than 150 research papers in journals as well as book chapters and technical reports. He was a core committee member in development of Decision Support System (DSS) funded by World Bank. He was serving as Associate Editor in Journal of Hydrology, Elsevier Sciences.

• Applications of Physics-guided Machine Learning Architectures in Hydrology.- A Review of Approaches and Applications for Streamflow Forecasting Using AI-based Models.- Estimation Of Groundwater Levels Using Machine Learning Techniques.- River Discharge Forecasting in Mahanadi River Basin Based On Deep Learning Techniques.- Machine Learning models for Groundwater Level Prediction.- Genetic Algorithm-Aided Neural Network for Sediment Critical Shear Stress Modeling.- An Integrative Approach for Oxygen Demand-based Stream Water Quality Modelling using QUAL2K-ANN Interactions.- Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA.- The High Resolution Statistical Downscaling of Seasonal Rainfall Forecasts Models for Comprehensive Evaluation of Hybrid Gamma Distribution for Districts of West Bengal, India.- Prediction of Rainfall in One of the Wettest Regions in India using Machine Learning Methods.

Dr. Roshan Karan Srivastav is an Associate Professor in the Department of Civil and Environmental Engineering at IIT Tirupati, serving since June 2018. He holds a Ph.D. in Water Resources Management from IIT Madras, a M.Tech in Water Resources Management from Motilal Nehru National Institute of Technology, and a B.E in Civil Engineering from University College of Engineering, Osmania University. His extensive research encompasses hydro-climatology, flood forecasting, reservoir operation, and stochastic hydrology. Before his tenure at IIT Tirupati, he was a Post-Doctoral Fellow at the University of Western Ontario, focusing on weather generators, system dynamics, and integrated water resources management. Dr. Srivastav has made significant contributions to the field through numerous publications in top-tier journals and international conferences. He also serves as the Project Director for the Technology Innovation Hub at IIT Tirupati Navavishkar I-Hub Foundation (IITTNiF), where he leads initiatives in positioning and precision technologies.Dr P. C. Nayak is Scientist F in Surface Water Hydrology Division at National Institute of Hydrology, Roorkee. Dr. Nayak hold a B. Tech in Civil Engineering from College of Engineering and Technology, Bhubaneswar and M. Tech in Water Resources Engineering from Indian Institute of Technology, Kharagpur, and Ph.D in Water Resources Engineering from Indian Institute of Technology Madras, Chennai. Dr. Nayak has more than 25 years of experience in the field of water resources, surface water hydrological modelling, flood management and application of machine learning techniques and his major research contributions include development of models and software tools for hydrologic modeling using conceptual, distributed and data driven algorithm. Soft computing has to date been the main topic of investigation, in particular exploring the potential benefits and pitfalls. He has published more than 150 research papers in journals as well as book chapters and technical reports. He was a core committee member in development of Decision Support System (DSS) funded by World Bank. He was serving as Associate Editor in Journal of Hydrology, Elsevier Sciences.

• Applications of Physics-guided Machine Learning Architectures in Hydrology.- A Review of Approaches and Applications for Streamflow Forecasting Using AI-based Models.- Estimation Of Groundwater Levels Using Machine Learning Techniques.- River Discharge Forecasting in Mahanadi River Basin Based On Deep Learning Techniques.- Machine Learning models for Groundwater Level Prediction.- Genetic Algorithm-Aided Neural Network for Sediment Critical Shear Stress Modeling.- An Integrative Approach for Oxygen Demand-based Stream Water Quality Modelling using QUAL2K-ANN Interactions.- Predictive Deep Learning Models for Daily Suspended Sediment Load in the Missouri River, USA.- The High Resolution Statistical Downscaling of Seasonal Rainfall Forecasts Models for Comprehensive Evaluation of Hybrid Gamma Distribution for Districts of West Bengal, India.- Prediction of Rainfall in One of the Wettest Regions in India using Machine Learning Methods.