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Modern Hydrology and Water Resources Modelling with AI
New
24 students

Modern Hydrology and Water Resources Modelling with AI

Master hydrology with AI and Python: statistics, IDF, bias correction, machine learning, GIS, forecasting & dashboards.
Created byAsghar Azizian
Last updated 8/2026
English
English [Auto],

What you'll learn

  • Analyze hydrologic and meteorological data using Python, statistical methods, visualization, and AI-assisted workflows
  • Develop rainfall-runoff, flood frequency, IDF, bias correction, and hydrologic forecasting models using Python
  • Build and evaluate machine learning models, including regression and XGBoost, for hydrology and water resources applications
  • Create GIS-based hydrologic analyses, river mapping workflows, and interactive AI-powered dashboards for decision support

Course content

4 sections • 11 lectures • 6h 9m total length

  • Bias Correction using Quantile Mapping (QM)34:35

    Learn how Quantile Mapping is used to correct systematic biases in climate and hydrologic datasets. This lecture demonstrates the main concept, workflow, and practical implementation of QM for improving modeled or forecast data.

  • Regression analyses using XGBoost Machine Learning36:08

    Explore how XGBoost can be applied to regression problems in hydrology and water resources. You will learn how to prepare data, train the model, evaluate predictions, and interpret model performance.

  • Developing a Python code for Flood depth calculation in different channel types20:27

    Develop a practical Python workflow to estimate flood depth for different channel geometries. The lecture shows how hydraulic relationships can be translated into reusable Python code for engineering applications.

  • Regression Analyses using AI and Python52:28

    Learn how AI-assisted Python coding can simplify regression analysis. This lecture covers model development, data fitting, prediction, visualization, and interpretation of regression results using a practical workflow.

Requirements

  • Basic knowledge of hydrology or water resources is helpful but not required. No advanced programming experience is needed; Python and AI workflows are explained step by step

Description

Modern Hydrology and Water Resources with AI is a practical, project-based course designed to show how modern hydrologists and water resources engineers can combine traditional hydrologic methods with Python, Artificial Intelligence, Machine Learning, GIS, and data-driven modelling.

Rather than focusing on only one hydrologic model, this course covers a wide range of real-world applications and demonstrates how AI can dramatically accelerate hydrologic analysis, programming, visualization, and model development.

You will learn how to use Python and AI tools to analyse hydro-meteorological data, perform statistical and frequency analyses, develop IDF curves, apply bias correction techniques, create rainfall-runoff models, perform flood routing, and develop hydrologic forecasting models.

The course also introduces Machine Learning approaches for hydrology, including regression and XGBoost-based modelling, and demonstrates how these methods can be applied to real hydrologic datasets.

Spatial analysis is another important component of the course. You will explore GIS and geospatial workflows for hydrologic applications, including watershed and river analysis, extraction of river characteristics such as river width, and integration of spatial data with hydrologic models.

A key focus of the course is the practical use of Artificial Intelligence. You will see how AI can assist with Python programming, model development, troubleshooting, data processing, visualization, and even the creation of professional interactive hydrology dashboards.

Topics covered in this course include:

Hydrologic data analysis with Python and AI

Statistical hydrology and regression analysis

Flood Frequency Analysis

IDF curve development

Rainfall-runoff modeling

Muskingum flood routing

Hydro-meteorological data processing

Bias correction of climate and forecast data

Hydrologic and flood forecasting

Machine Learning and XGBoost for hydrology

River width and river geometry extraction

Professional plots, maps, and visualizations

AI-assisted interactive dashboard development

The course emphasizes practical implementation rather than theory alone. Step-by-step examples and real-world workflows are used throughout the course so that you can adapt the methods to your own hydrology and water resources projects.

This course is suitable for hydrologists, civil and water resources engineers, environmental engineers, researchers, graduate students, GIS specialists, and professionals interested in applying modern computational and AI techniques to hydrology.

By the end of the course, you will have a practical toolkit for combining hydrology, Python, AI, Machine Learning, GIS, and forecasting to solve modern water resources engineering problems.

Who this course is for:

  • This course is designed for hydrologists, civil and water resources engineers, environmental engineers, researchers, graduate students, GIS professionals, and anyone interested in applying Python, AI, machine learning, forecasting, and geospatial analysis to practical hydrologic problems.

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