-
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.
Mentor Based
Flood Susceptibility Mapping: Model Design
“Designing Accurate Models for Effective Flood Susceptibility Mapping”
About This Course
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.
Workshop Objectives
Workshop 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.
Who Should Enrol?
- 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.
Important Dates
Registration Ends
02/25/2026
IST 4 PM
Workshop Dates
02/25/2026 – 02/27/2026
IST 5: 30PM
Workshop 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.
Fee Structure
Student
₹2999 | $70
Ph.D. Scholar / Researcher
₹3999 | $80
Academician / Faculty
₹4999 | $90
Industry Professional
₹6999 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Publication Opportunity
Get published in a prestigious open-access journal.
Centre of Excellence
Become part of an elite research community.
Networking & Learning
Connect with global researchers and mentors.
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
★★★★★
Cancer Drug Discovery: Creating Cancer Therapies
Mario Rigo • ★★★★★
Power BI and Advanced SQL Mastery Integration Workshop, CRISPR-Cas Genome Editing: Workflow, Tools and Techniques
Silvia Santopolo • ★★★★★
Artificial Intelligence for Cancer Drug Delivery
G Jyothi • ★★★★★
Artificial Intelligence for Cancer Drug Delivery
RAVIKANT SHEKHAR • View All Feedbacks →