International Journal on Science and Technology

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Utilizing Forecasting Algorithms For Integrated Monitoring Of Meteorological Patterns And Fisherfolks Locations

Author(s) Loyd Jeson Tinaytinay Roa
Country Philippines
Abstract Small-scale fisherfolk in the Philippines remain highly vulnerable to maritime hazards due to fragmented registration systems, limited vessel monitoring mechanisms, and the absence of localized predictive weather intelligence. This study proposes and evaluates an integrated monitoring framework implemented through the Fisherfolk Integrated System for Holistic Networking, Engagement, and Tracking (FISHNET). The system integrates automated fisherfolk registration, real-time Global Positioning System (GPS) vessel tracking, Short Message Service (SMS)-based alert dissemination, and a hybrid supervised machine learning and rule-based weather risk prediction model designed for low-resource coastal environments.

The forecasting component employs supervised linear regression trained on historical meteorological time-series data to generate hourly and weekly environmental trend estimates. These predictions are translated into operational safety categories (Safe, Caution, and Danger) and a quantitative hourly fishing suitability score ranging from 0 to 100 using a deterministic penalty-based scoring framework. The system further extracts optimal and high-risk sailing windows and generates automated, time-specific advisories to support proactive decision-making. Predictive outputs are synchronized with real-time spatial vessel data to enhance situational awareness and localized safety coordination.
The system was pilot-tested in Barangay Barra, Dipolog City, involving 30 purposively selected participants, including small-scale fisherfolk, local government personnel, and information technology experts. Evaluation results demonstrated high functional acceptance across usability, reliability, efficiency, and overall satisfaction dimensions (means ranging from 4.59 to 4.89 on a five-point scale). Spearman’s rank correlation analysis revealed a strong positive relationship between system usability and user satisfaction (ρ = 0.7913, p < 0.01), indicating that intuitive system design significantly influences adoption.

Findings suggest that integrating supervised forecasting, structured risk classification, hourly suitability scoring, and automated advisory generation within a unified governance framework enhances maritime situational awareness and community-level disaster preparedness. The study contributes an explainable, resource-efficient predictive monitoring architecture tailored to vulnerable small-scale fisheries contexts.
Keywords Small Scale Fisheries, Small Scale Fisherfolk, Maritime Safety, Forecasting Algorithms, Machine Learning, Machine Learning Forecasting, GPS Vessel Tracking, Early Warning Systems, Digital Governance, Digital Fisheries Management, Coastal Resilience, Coastal Disaster Risk Reduction, Weather Risk Classification, Integrated Monitoring System
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-02-24
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