Design and evaluate a GIS-based landslide early warning system in seismic zones

Thesis Abstract

Landslides in seismic zones present a significant natural hazard with profound impacts on infrastructure, livelihoods, and safety, necessitating the development of proactive early warning systems that leverage geospatial technologies for timely interventions. This study aims to design and evaluate a Geographic Information System (GIS)-based landslide early warning system (LEWS) tailored for seismic-prone regions, with the overarching goal of enhancing hazard preparedness and risk mitigation through integrated spatial analysis and real-time data monitoring. To achieve this, the research delineates specific objectives identifying key environmental and seismic precursors to landslides, integrating geological, hydrological, and seismic datasets within a GIS framework, developing an operational LEWS prototype, and assessing its predictive accuracy and usability in real-world scenarios. The research adopts a mixed-methods approach, combining quantitative spatial analysis with qualitative usability evaluation. The quantitative component involves collecting primary data from a sample of 120 landslide-prone catchment areas within the targeted seismic zone, selected through stratified random sampling based on historical landslide frequency, land cover, and seismic activity levels. Data sources include remote sensing imagery, topographical maps, geological surveys, and seismic records acquired from national geological agencies, complemented by ground-truth field observations conducted across twenty selected sites. Instruments include GPS devices, soil moisture sensors, accelerometers, and GIS software tools. The qualitative component involves semi-structured interviews and focus group discussions with local residents, emergency responders, and disaster management officials to explore system usability and contextual appropriateness. Analytically, the study employs logistic regression analysis to determine the significance of identified environmental and seismic variables in landslide occurrence, and applies machine learning algorithms, including random forest and logistic regression models, for hazard prediction validation. Spatial analysis techniques such as landslide susceptibility mapping, overlay analysis, and network analysis within ArcGIS environment facilitate the integration of multi-layer datasets. The system’s predictive performance is evaluated through receiver operating characteristic (ROC) curves and confusion matrix analyses, measuring sensitivity, specificity, and overall accuracy. Additionally, thematic analysis is utilized to interpret qualitative data, identifying barriers and facilitators to adopting the LEWS in local communities. Expected findings include the identification of key land surface and seismic parameters—such as slope gradient, soil type, rainfall threshold, and ground acceleration—that significantly influence landslide susceptibility, and demonstration of the GIS-based system’s capacity to provide early warnings with at least 85% predictive accuracy. The system is anticipated to significantly improve the timeliness of hazard alerts, as evidenced by reduced false-positive and false-negative rates in validation tests. The research further emphasizes the importance of community engagement and system usability, indicating high acceptance among local stakeholders following iterative refinement based on feedback. This study contributes novel insights into the integration of seismic data with traditional landslide risk factors within a GIS framework, addressing existing gaps related to real-time predictive capacity in seismic zones. It advances theoretical understanding through the application of the Protection Motivation Theory to user acceptance of early warning systems, and demonstrates a practical model for hazard mitigation applicable in similar vulnerable regions globally. In conclusion, the developed LEWS promises to enhance early warning capabilities, supporting disaster preparedness and reducing landslide-related losses. Recommendations include scaling the system for broader regional deployment, establishing continuous monitoring protocols, and fostering community participation through targeted awareness campaigns. The study underscores the need for institutionalizing GIS-based hazard management and encourages further research into integrating emerging technologies such as remote sensing and IoT sensors for continuous system enhancement.