Development of an Integrated Natural and Socioeconomic Indicators Monitoring System for Bulacan Using Earth Intelligence Tools

Authors

  • Mark Neil Pascual School of Graduate Studies, AMA Computer University, Villa Arca Ave., Project 8, Quezon City, Philippines
  • Jeffrey Leonen School of Graduate Studies, AMA Computer University, Villa Arca Ave., Project 8, Quezon City, Philippines

DOI:

https://doi.org/10.65141/ject.v2i1.n1

Keywords:

Flood risk assessment, disaster preparedness, Geographic Information Systems (GIS), predictive analytics, earth intelligence tools

Abstract

This study introduces an Integrated Natural and Socioeconomic Indicators Monitoring System for Bulacan, designed to strengthen local flood risk assessment and enhance disaster preparedness. Recognizing gaps in current methodologies, this research integrates Earth intelligence tools, incorporating real-time satellite imagery, geospatial analyses, socioeconomic indicators, and localized datasets. The primary objective is to provide an accurate, dynamic platform for predictive analytics, prescriptive mitigation, and real-time flood risk monitoring. Methodologically, the study employs a Single Page Application (SPA) architecture hosted on cloud infrastructure, utilizing React.js for visualization and a Django REST API for data management. Stakeholder evaluations conducted in Meycauayan City and surrounding barangays revealed significant improvements in flood prediction accuracy, enhanced decision-making speed, and increased overall user satisfaction. Challenges encountered during system implementation included complexities in data integration and maintaining consistent responsiveness during peak usage. The research concludes that the developed system effectively addresses critical limitations of fragmented disaster response mechanisms. Key recommendations include continuous technological enhancement, comprehensive stakeholder training, and broader integration of hazard datasets to further augment disaster resilience capabilities in Bulacan.

References

Abante, A. M., Abante, C. G., Bartolata, J., Cobilla, M., Rosalada, J. P., Octeza, F., & Torres, E. (2023). Land use policy area (LUPA): A stratagem towards advanced preparedness in the ArcGIS platform. International Journal of Computing Sciences Research, 7, 1273–1286. https://doi.org/10.25147/ijcsr.2017.001.1.100

Adu-Gyamfi, B., Ariyaningsih, A., Zuquan, H., Yamazawa, N., Kato, A., & Shaw, R. (2024). Reflections on science, technology and innovation on the aspirations of the Sendai Framework for Disaster Risk Reduction. International Journal of Disaster Resilience in the Built Environment, 15(2), 289–302.

Ahmed, I., Das (Pan), N., Debnath, J., Bhowmik, M., & Bhattacharjee, S. (2024). Flood hazard zonation using GIS-based multi-parametric analytical hierarchy process. Geosystems and Geoenvironment, 3(2). https://doi.org/10.1016/j.geogeo.2023.100250

Albano, H. B. (2025). Empowering local government units with open-source tools: Building a dynamic web-based information system. Isabela State University Linker: Journal of Engineering, Computing, and Technology, 1(1), 1–14. https://doi.org/10.65141/ject.v1i1.n1

Archieval, M. J., Vinluan, A. A., & Villegas, R. A. (2024). Evacuation operation management system using multi-objective artificial bee colony. Isabela State University Linker: Journal of Engineering, Computing, and Technology, 1(2), 26–39. https://doi.org/10.65141/ject.v1i2.n3

Aspiras, K. F. (2022). Building Metropolitan Manila’s institutional resilience in the context of disaster risk reduction and management. In Disaster Risk Reduction for Resilience: Disaster Risk Management Strategies (pp. 317–331). Springer. https://doi.org/10.1007/978-3-030-72196-1_12

Bardiago, G. V., Santa Monica, J. B. D., & Feliciano, C. G. M. (2024). HealthSentry: Design and development of municipal health condition monitoring using spatio-temporal analysis and geo-mapping. Isabela State University Linker: Journal of Education, Social Sciences, and Allied Health, 1(1), 91–106. Retrieved from https://www.isujournals.ph/index.php/jessah/article/view/46

Brown de Colstoun, E. C., Huang, C., Wang, P., Tilton, J. C., Tan, B., Phillips, J., Niemczura, S., Ling, P.-Y., & Wolfe, R. E. (2017). Global man-made impervious surface (GMIS) dataset from Landsat (Version 1.00) [Dataset and images via ArcGIS map service]. Palisades, NY: NASA SEDAC.

Bulacan Provincial Disaster Risk Reduction and Management Office. (n.d.). Bulacan flood event reports. Retrieved from Google Drive (publicly accessible reports folder)

Canlas, I. P. (2023). Three decades of disaster risk reduction education: A bibliometric study. Natural Hazards Research, 3(2), 326–335. https://doi.org/10.1016/j.nhres.2023.02.007

Carrasco, S., & Egbelakin, T. (2023). Adaptive mixed methods research for evaluating community resilience and the built environment. In Mixed Methods Research Design for the Built Environment (pp. 233–250). CRC Press. https://doi.org/10.1201/9781003204046-17

Center for International Earth Science Information Network-CIESIN-Columbia University. (2021). Low Elevation Coastal Zone (LECZ) urban-rural population and land area estimates, version 3 (Version 3.00) [Dataset and images via ArcGIS map service]. Palisades, NY: NASA SEDAC. https://doi.org/10.7927/D1X1-D702

Center for International Earth Science Information Network-CIESIN-Columbia University. (2022). Global Gridded Relative Deprivation Index (GRDI), version 1 (Version 1.00) [Dataset and images via ArcGIS map service]. Palisades, NY: NASA SEDAC. https://doi.org/10.7927/3XXE-AP97

Cepero, T., Montané-Jiménez, L. G., & Maestre-Góngora, G. P. (2025). A framework for designing user-centered data visualizations in smart city technologies. Technological Forecasting and Social Change, 210. https://doi.org/10.1016/j.techfore.2024.123855

Department of Science and Technology – Project NOAH. (2015a). Flood hazard map via open-hazards-ph Mapbox map service and dataset (ESRI format) from Project NOAH public Google Drive.

Department of Science and Technology – Project NOAH. (2015b). Storm surge hazard maptiles via open-hazards-ph Mapbox map service. National Institute of Geological Sciences, University of the Philippines.

European Commission. (2024). INFORM: Shared evidence for managing crises and disasters (pp. 1–22). https://doi.org/10.2760/817042

Goh, M. L., Manahan, V. A. M., Mangalus, C. J., Carreon, R. J., Ong, C. C., & Vicente, H. (2023). iAlerto: A web and mobile alert system for Pasig City Disaster Risk Reduction Management Office (PCDRRMO) with mobile GPS service integration. International Journal of Computing Sciences Research, 7, 1092–1108. https://doi.org/10.25147/ijcsr.2017.001.1.93

Group on Earth Observations. (2017). Human Planet Initiative (GEO). Retrieved from https://ghsl.jrc.ec.europa.eu/HPI.php

Group on Earth Observations. (2025). Earth Intelligence for All: GEO POST 2025 Strategy.

Hadi, F. A. A., Sidek, L. M., Salih, G. H. A., Basri, H., Sammen, S. S., Dom, N. M., Ali, Z. M., & Ahmed, A. N. (2024). Machine learning techniques for flood forecasting. Journal of Hydroinformatics, 26(4), 779–799. https://doi.org/10.2166/hydro.2024.208

Joyice, M. H., Vidya, K. V. S., Lakshmi, L. V., Juilath, M., Prajwala, K., & Vasarao, P. S. (2024). Identifying flood prediction using machine learning techniques. International Journal of Innovative Science and Research Technology, 9(3), 144–147. https://doi.org/10.38124/ijisrt/ijisrt24mar112

Joint Research Centre. (2020). INFORM Risk. Retrieved from https://drmkc.jrc.ec.europa.eu/inform-index/INFORM-Risk/Methodology

Lagmay, A. M., & Kerle, N. (2015). Storm-surge models helped for Hagupit. Nature, 519(7544), 414. https://doi.org/10.1038/519414b

Lapidez, J. P., Tablazon, J., Dasallas, L., Gonzalo, L. A., Cabacaba, K. M., Ramos, M. M. A., Suarez, J. K., Santiago, J., Lagmay, A. M. F., & Malano, V. (2015). Identification of storm surge vulnerable areas in the Philippines through the simulation of Typhoon Haiyan-induced storm surge levels over historical storm tracks. Natural Hazards and Earth System Sciences, 15(7), 1473–1481. https://doi.org/10.5194/nhess-15-1473-2015

Murata, H., Saitoh, K., & Sumida, Y. (2018). True color imagery rendering for Himawari-8 with a color reproduction approach based on the CIE XYZ color system. True Col, 96B, 211–238. https://doi.org/10.2151/jmsj.2018-049

Ogbuene, E. B., Eze, C. A., Aloh, O. G., Oroke, A. M., Udegbunam, D. O., Ogbuka, J. C., Achoru, F. E., Ozorme, V. A., Anwara, O., Chukwunonyelum, I., Nebo, A. N., & Okolo, O. J. (2024). Application of machine learning for flood prediction and evaluation in southern Nigeria. Atmospheric and Climate Sciences, 14(3), 299–316. https://doi.org/10.4236/acs.2024.143019

Patel, R., & Patel, A. (2024). Evaluating the impact of climate change on drought risk in semi-arid region using GIS technique. Results in Engineering, 21. https://doi.org/10.1016/j.rineng.2024.101957

Safaeian, M., Moses, R., Ozguven, E. E., & Dulebenets, M. A. (2024). An optimization-based risk management framework with risk interdependence for effective disaster risk reduction. Progress in Disaster Science, 21. https://doi.org/10.1016/j.pdisas.2024.100313

Saraswat, J. K., & Choudhari, S. (2025). Integrating big data and cloud computing into the existing system and performance impact: A case study in manufacturing. Technological Forecasting and Social Change, 210, 123883. https://doi.org/10.1016/j.techfore.2024.123883

Sentinel Hub by Planet Labs. (n.d.). Sentinel Hub Collections.

Sparks, A. (2018). nasapower: A NASA POWER global meteorology, surface solar energy and climatology data client for R. Journal of Open Source Software, 3(30), 1035. https://doi.org/10.21105/joss.01035

Tablazon, J., Caro, C. V., Lagmay, A. M. F., Briones, J. B. L., Dasallas, L., Lapidez, J. P., Santiago, J., Suarez, J. K., Ladiero, C., Gonzalo, L. A., Mungcal, M. T. F., & Malano, V. (2015). Probabilistic storm surge inundation maps for Metro Manila based on Philippine public storm warning signals. Natural Hazards and Earth System Sciences, 15(3), 557–570. https://doi.org/10.5194/nhess-15-557-2015

Wang, P., Huang, C., Brown de Colstoun, E. C., Tilton, J. C., & Tan, B. (2017). Global human built-up and settlement extent (HBASE) dataset from Landsat (Version 1.00) [Dataset and images via ArcGIS map service]. Palisades, NY: NASA SEDAC.

Downloads

Published

2025-06-30

How to Cite

Pascual, M. N., & Leonen, J. (2025). Development of an Integrated Natural and Socioeconomic Indicators Monitoring System for Bulacan Using Earth Intelligence Tools. Isabela State University Linker: Journal of Engineering, Computing and Technology, 2(1), 1–19. https://doi.org/10.65141/ject.v2i1.n1