Design and evaluation of an open-source landslide early-warning system

Thesis Abstract

In many mountainous and hilly regions, landslides pose significant threats to life, infrastructure, and livelihoods, yet early-warning capabilities remain limited due to fragmented data sources, software heterogeneity, and insufficient community integration. This study addresses the gap by designing and evaluating an open-source landslide early-warning system (L-EWS) that integrates geophysical, meteorological, and remote-sensing data streams within a modular, reproducible platform to enhance decision-support for local authorities and communities. The aim is to develop a low-cost, extensible, and auditable L-EWS that can be deployed in resource-constrained settings while maintaining scientific rigor. Specific objectives are (i) to synthesize existing theories of landslide triggers and warning communication into a cohesive conceptual framework; (ii) to implement a modular software architecture that ingests multi-source data, performs real-time preprocessing, and executes risk assessment models; (iii) to calibrate and validate predictive models using historical event catalogs and sensor data from a representative study area covering approximately 180 square kilometers and spanning 15 years; (iv) to evaluate system performance against operational requirements through a mixed-methods field trial involving 12 local forecast teams and 24 community stakeholders; and (v) to develop governance, user manuals, and open datasets to promote reproducibility and adoption. The methodology adopts a design science research paradigm complemented by a theoretical lens anchored in the concepts of risk society and diffusion of innovations. A population comprising landslide-prone communities within the study area will be considered, with a sample that includes 40 historically documented landslide events, 24 near-real-time meteorological stations, and 30 high-resolution satellite-derived soil moisture rasters. Data collection instruments include sensor loggers for precipitation, soil moisture probes, a portable GNSS-based displacement kit, survey instruments for community risk perception, and a structured interview protocol for emergency managers. Instrument validity and reliability will be established through content validation with a panel of five geomorphology and hydrology experts, test-retest reliability analyses for survey items (Cronbach’s alpha ? 0.80), and pilot testing over a three-month period. Data analysis will employ a multi-model approach (i) statistical regression analyses (logistic and Poisson) to identify significant predictors of landslide occurrence; (ii) machine-learning techniques (random forest, gradient boosting, and support-vector machines) for failure probability estimation; (iii) time-series analysis (ARBIMA) for precursor signal trends; (iv) model comparison using ROC-AUC, precision-recall, and Brier scores; and (v) qualitative analysis of stakeholder feedback using thematic analysis to assess usability and perceived reliability. Model specification will integrate a Bayesian updating mechanism to refine posterior probabilities as new data arrive, thereby supporting adaptive alert thresholds. The system architecture will be implemented in an open-source stack including Python-based data processing, PostgreSQL/PostGIS for spatial data management, and a web-based dashboard built with Leaflet and D3.js for visualization. Ethical considerations will include informed consent for stakeholder interviews, data privacy protections for crowd-sourced inputs, and transparent disclosure of model limitations. Expected findings include (i) identification of robust predictor sets (e.g., antecedent rainfall intensity, soil moisture anomalies, shallow groundwater fluctuations, and terrain susceptibility indices) with demonstrable predictive performance (ROC-AUC ? 0. eighty) on held-out data; (ii) demonstration of improved alert lead times and reduced false alarm rates compared with baseline, non-open-source approaches; (iii) evidence that participatory design enhances user acceptance and system use in operational contexts; and (iv) a replicable deployment blueprint, including data schemas, model parameters, and governance procedures. The study contributes to knowledge by advancing open-source, reproducible EWS design in geology and hazards research, integrating design science with practical implementation in a low-resource setting, and providing empirical evaluation of usability and decision-support effectiveness grounded in social-technical theory. Conclusions are expected to advocate for broader adoption of modular, transparent, and participatory EWS architectures, with recommendations for policy integration, capacity-building, and continued model validation across diverse climatic and geological contexts. Suggestions for further work include extending the framework to incorporate crowd-sourced alarm reporting, integrating higher-resolution UAV-derived terrain data, and scaling the architecture to regional or national hazard monitoring programs.