Syllabus

Module I (10 Hours)
Introduction to AI in Water Resources Management:
Role of Artificial Intelligence and Machine Learning in water resources management; Importance of data-driven approaches in hydrology; Basic statistical concepts and data preprocessing; Overview of AI/ML algorithms (supervised, unsupervised, deep learning); Tools and platforms: Python, MATLAB, TensorFlow, Scikit-learn, WEKA.

Module II (8 Hours)
Time Series Analysis and Machine Learning Applications:
Hydrologic time series modeling using AI: trend analysis, seasonality, oscillation, jumps, and stochastic components; Streamflow and rainfall prediction using ML models (e.g., ANN, SVM, LSTM); AI-based frequency analysis and correlation techniques; Development and calibration of stochastic hydrologic models with machine learning.

Module III (8 Hours)
Reservoir Capacity: Reservoir Capacity Fixation, Ripple’s Mass Curve and sequent peak algorithm, Determination of Spillway Size Reservoir Operation and Management: Strategies, Working Table and Allocation

Module IV (6 Hours)
Reservoir Sedimentation: Sediment yield and erosion measurement, Sediment dynamics in Reservoirs

Module V (4 Hours)
Reservoir Economics and Optimization: Economical Analysis of Reservoirs, Reservoirs Optimization Techniques.

To apply statistical and probabilistic methods in hydrology, including probability distributions, frequency analysis, and regression techniques.

To analyze time series and stochastic hydrologic processes by identifying trends, seasonality, and stochastic components for predictive modeling.

To evaluate reservoir capacity, sedimentation dynamics, and spillway design for effective water storage and sediment management.

To optimize reservoir operation through economic analysis and advanced optimization techniques for sustainable water resource management.

By the end of the course, students will be able to:
CO1: Apply statistical concepts, probability distributions, frequency analysis, and regression techniques to analyze hydrological data and assess uncertainty in hydrologic processes.

CO2: Perform time series analysis in hydrology, including trend detection, seasonality assessment, and stochastic modeling, to characterize hydrologic variability and develop predictive models.

CO3: Evaluate reservoir capacity using mass curve and sequent peak algorithms, determine spillway sizing, and implement reservoir operation strategies for efficient water resource management.

CO4: Analyze sediment yield, erosion processes, and sediment transport dynamics in reservoirs to develop strategies for sedimentation management and reservoir sustainability.

CO5: Conduct economic analysis of reservoirs and apply optimization techniques for efficient reservoir planning, operation, and management.

Haan, C. T, Statistical Methods in Hydrology, 1st East-West Press Edition

Morris, G. L., and Fan, J., Reservoir Sedimentation Handbook: Design and Management of Dams, Reservoirs, and Watersheds for Sustainable Use, Mc Graw Hill

L. W. Meyer, Water Resources Hand Book, Mc Graw Hill

C.C. Warnic, Hydro power Engineering, Prentice Hall Inc., New Jersey, 1984