Name: Flood Susceptibility Mapping: Model Design
Start: 2026-03-11
End: 2026-03-13

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Delivery Mode

Virtual / Online

Level

Moderate

Certificate

Mentor Based

Language

English

Rating

4 Stars

About Workshop

This workshop covers the design and development of flood susceptibility models using GIS and machine learning. Participants will explore terrain analysis, hydrological factors, and climate change integration in flood risk mapping. Hands-on sessions will focus on model building, validation, and sensitivity analysis to enhance flood risk management strategies.

Aim

The aim of this workshop is to provide participants with the skills to design robust flood susceptibility models using GIS and machine learning techniques. It focuses on understanding terrain, hydrological factors, and the integration of climate change projections. Participants will learn to evaluate model accuracy, perform sensitivity analysis, and improve flood risk assessments.

What Participants Will Learn

To understand the fundamentals of flood susceptibility mapping using GIS and remote sensing.
To learn the integration of terrain features, hydrological factors, and climate projections into flood models.
To gain hands-on experience in building statistical and machine learning-based flood susceptibility models.
To evaluate model performance through validation techniques and accuracy metrics.
To conduct sensitivity and uncertainty analysis for enhancing flood risk assessment and decision-making.

Structure

Day 1: Geospatial Intelligence & Multi-Source Data Fusion

Focus: Establishing a robust, high-resolution foundational database.

Advanced Spatial Stratigraphy: Leveraging Multi-Criteria Decision Analysis (MCDA) and high-resolution Digital Elevation Models (DEMs).
Hydro-Climatic Synthesis: Integrating CMIP6 climate projections and satellite altimetry (GRACE/Sentinel) into susceptibility frameworks.
Geomorphological Influencers: Deep dive into Slope, Aspect, TWI (Topographic Wetness Index), and SPI (Stream Power Index) dynamics.

Hands-On Sessions

Hands-On I: Automated Data Pipelines—API-based acquisition of global environmental datasets.
Hands-On II: Topographic Morphometry—Generating secondary hydrological derivative layers using GIS automation.

Day 2: Algorithmic Architecture & Predictive Modeling

Focus: Navigating the transition from statistical inference to AI-driven forecasting.

Comparative Model Paradigms: Evaluating Frequency Ratio (FR) and Weight of Evidence (WoE) against non-linear ML approaches.
Advanced ML Ensembles: Deploying Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (XGBoost) for spatial classification.
Optimization Strategies: Hyperparameter tuning using Bayesian optimization to minimize structural risk.
The Hybrid Frontier: Introduction to Neuro-Fuzzy systems and Deep Learning (CNNs) for complex flood-pattern recognition.

Hands-On Sessions

Hands-On I: Scripting Statistical Benchmarks—Implementing Bivariate and Multivariate regression models.
Hands-On II: Neural Network Implementation—Building and training a supervised ML model for spatial prediction.

Day 3: Validation Rigor, Uncertainty, and Policy Integration

Focus: Ensuring model reliability for publication and industrial application.

Diagnostic Metrics: Beyond accuracy—interpreting the Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC).
Sensitivity Analysis: Identifying dominant predictors using Jackknife tests and Variance-based Sobol indices.
Uncertainty Quantification (UQ): Assessing error propagation in spatial modeling to enhance decision-making confidence.
Future-Proofing: Scaling models for real-time monitoring and integration into Early Warning Systems (EWS).

Hands-On Sessions

Hands-On I: Performance Analytics—Generating Confusion Matrices and F1-Scores for model comparison.
Hands-On II: Robustness Testing—Conducting Monte Carlo simulations for spatial uncertainty assessment.

Important Dates

Registration Ends

4 PM

Workshop Dates

2026-03-11

5: 30PM

What You Will Gain

Outcomes

Ability to design and implement flood susceptibility models using GIS and machine learning techniques.
Proficiency in integrating climate change projections and hydrological data into flood risk assessments.
Skill in evaluating model accuracy using validation techniques like AUC and ROC.
Expertise in performing sensitivity and uncertainty analysis to improve flood model reliability.
Enhanced capacity to contribute to effective flood risk management and decision-making strategies.

Who Should Attend

Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.