Artificial Intelligence in Hydrology

About this course

Artificial Intelligence in Hydrology: From Basics to Advanced Models

This course provides a comprehensive understanding of hydrology and its integration with modern Artificial Intelligence techniques. It begins with fundamental concepts such as the hydrologic cycle, precipitation, infiltration, runoff, hydrographs, and groundwater, building a strong base for learners from civil and environmental engineering backgrounds.

As the course progresses, learners will explore key areas of water resources engineering, including irrigation systems, reservoir planning, and watershed management. Practical engineering methods such as rainfall–runoff modeling, flood estimation, and hydrograph analysis are explained in a clear and applied manner.

The course then transitions into advanced topics, focusing on the application of machine learning and deep learning in hydrology. Learners will gain hands-on exposure to models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), Histogram Gradient Boosting (HGBoost), Long Short-Term Memory (LSTM), and CNN-RNN models.

By the end of the course, learners will be able to apply AI techniques to real-world problems such as streamflow prediction, flood forecasting, groundwater analysis, and water resource management. This course is ideal for students, researchers, and professionals looking to bridge the gap between traditional hydrology and cutting-edge data-driven approaches. Includes real-world case studies, practical datasets, Python implementation, model evaluation techniques, and industry-relevant project experience.

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