Assessment of flood susceptibility and seismic risk in [Your City/Region] using multi-criteria GIS-based weighting and machine learning models

Project Abstract

This study presents a spatially explicit assessment of flood susceptibility and seismic risk in [Your City/Region] by integrating multi-criteria GIS-based weighting with advanced machine learning models. The research combines hydro-meteorological, topographic, geological, land-use, and socio-economic datasets to develop a unified framework capable of capturing spatial heterogeneity and interaction effects between flood and seismic hazards. A comprehensive data collection and preprocessing pipeline was established, including the harmonization of raster and vector layers, handling of missing values, and normalization to enable fair comparison across diverse indicators. To quantify flood susceptibility, key criteria such as rainfall intensity and duration, proximity to rivers and floodplains, soil type, land cover, drainage density, elevation, slope, and drainage network characteristics were weighted using a hybrid approach that blends expert knowledge with data-driven optimization, incorporating techniques such as analytic hierarchy process (AHP) and random forest feature importance. For seismic risk, criteria including peak ground acceleration, proximity to active faults, soil liquefaction potential, basin depth, building stock vulnerability, population density, critical infrastructure exposure, and historical seismicity were integrated. The multi-criteria framework was then fused to produce two complementary hazard maps flood susceptibility index (FSI) and seismic risk index (SRI). Machine learning models, including gradient boosting (XGBoost), random forest, support vector machines, and neural networks, were trained using historical flood events, seismic occurrences, and corresponding exposure data to learn nonlinear relationships and interactions among indicators. Model performance was evaluated via cross-validation, ROC-AUC, precision-recall, F1-score, and region-specific hit rates, with additional assessment of spatial transferability to validate model generalizability in neighboring areas. The outputs include high-resolution risk maps, uncertainty estimates, and scenario analyses under varying climate and urban development trajectories. A coupled flood-seismic risk integration layer was developed to identify zones of compound hazard, where overlapping high FSI and high SRI indicate regions of elevated risk requiring prioritized mitigation. Sensitivity analyses revealed the most influential indicators for each hazard and highlighted potential data gaps, such as real-time flood stage monitoring and up-to-date building vulnerability datasets. The study also explores adaptive zoning and multi-hazard mitigation planning, offering decision-support insights for emergency preparedness, land-use regulation, and infrastructure resilience investments. The methodological framework is designed to be transferable to other regions by substituting region-specific datasets, enabling comparative risk assessments across different geographies. The results demonstrate that integrating GIS-based weighting with robust machine learning models enhances predictive accuracy and provides actionable spatial intelligence for policymakers, urban planners, and disaster management agencies. Recommendations include the development of an operational early-warning dashboard, incorporation of dynamic climate projections, and continuous refinement of vulnerability curves to reflect evolving urban morphology and climate resilience strategies. This work contributes to advancing multi-hazard risk assessment paradigms by illustrating a scalable, data-driven approach that bridges scientific inquiry with practical risk reduction in [Your City/Region].

Project Overview

What This Project Is About

---

A straightforward study that looks at how floods and earthquakes affect a city or region. It uses maps and basic computer tools to combine different factors that influence risk and then uses simple models to identify areas most at risk.

---

---

The Problem It Addresses

---

Many places lack a clear, actionable picture of where flood and seismic risks concentrate. This project aims to fill that gap by bringing together multiple risk clues so decision-makers can focus prevention, planning, and response where it matters most.

---

---

Objectives of the Project

---

1. Identify key factors that influence flood and seismic risk (terrain, rainfall, soil, fault lines, etc.).
2. Create a simple scoring system to rank areas by combined risk.
3. Develop a user-friendly map showing high-risk zones.
4. Explain how different data choices affect the results in plain terms.

---

---

What You Will Do Step by Step

---

1. Collect basic data such as topography, rainfall, land use, and known faults or flood histories.
2. Prepare and explore data to ensure consistency (clean any errors, align formats).
3. Assign weights to factors so important causes of flood and quake risk stand out.
4. Combine factors into a simple risk score for each area and map it.
5. Check the results by comparing with past events or existing studies.
6. Build a short guide on how to use the map for planning and response.

---

---

Expected Outcome

A clear, easy-to-read map and report showing where flood and seismic risks are highest, plus practical steps for mitigation and preparedness that non-specialists can use in planning and policy decisions.