Development of Management Information System using Geospatial Modeling Analysis and Predictive Algorithms (Geo-MAPA): A Smart-Monitored Alert and Response Technology for Forest Fire Readiness and Early-warning System (SMARTFIRES) For Leyte Sab-a Basin Peatland

Abstract

This research presents the development and implementation of MIS-GeoMAPA, a comprehensive Management Information System utilizing geospatial modeling and predictive algorithms for forest fire readiness and early warning in Leyte's Sab-a Basin Peatland (LSBP). Employing an iterative-waterfall development approach, we integrated real-time environmental sensor data, including temperature, humidity, wind speed, and soil moisture, with advanced geospatial analysis. Predictive models, particularly linear and logistic regression, were developed to forecast fire risks, achieving high accuracy validated through statistical tests like the Wilcoxon signed-rank test. The system’s performance, reflected in a Mean Squared Error (MSE) of 0.25 and an 88% accuracy rate, underscores its potential in enhancing fire preparedness and response. This study highlights the critical role of environmental monitoring and predictive analytics in mitigating forest fire impacts, offering a scalable solution for environmental management and disaster readiness.