Community Fault Reporting Model for the Proactive Disaster Management of Environmental Risks

Environmental risks, such as infrastructure damage and environmental issues like potholes, overgrown trees, clogged drains, and broken streetlights, pose significant threats to public safety. This paper proposes the Community Fault Reporting Conceptual Model, named Urban Alert!, to improve environmental risk management through community engagement and data analytics in Malaysia. The model integrates geo-tagged, multi-platform community reporting with a data pipeline, followed by Business Intelligence (BI) and Machine Learning (ML) analytics to support the identification of recurring and risk-prone areas, enabling risk prioritization and early intervention by responsible authorities to implement proactive measures. The software prototype architecture and user interaction model are also presented in this paper. This study concentrates on the evaluation of architectural validation, prototype implementation, and preliminary analytic capabilities rather than extensive empirical deployment. It also improves the community fault reporting model by improving community data reporting, location tracking, data processing and cleansing, integrated with predictive analysis for proactive disaster management. This study contributes to SDG 11, Sustainable Cities and Communities that aims to make cities and human settlements inclusive, safe, resilient and sustainable by improving fault reporting in proactive disaster management.

Fault Reporting, Business Intelligence, Proactive Disaster, Data Model, Environment risk

1. [1] Jabatan Perkhidmatan Awam, “Sistem Pengurusan Aduan Awam SISPAA,” Kerajaan Malaysia, 2023. https://jpa.spab.gov.my/eApps/system/index.do [Google Scholar] [Crossref]

2. [2] S. D. Harijanti, “Complaint Handling Systems In The Public Sector: A Comparative Analysis Between Indonesia and Australia,” Indones. Comp. Law Rev., vol. 3, no. 1, pp. 1–24, 2020, doi: 10.18196/iclr.v3i1.11454. [Google Scholar] [Crossref]

3. [3] M. K. A. Abdullah, M. K. Musa, and M. A. Abdul Rahman, “Persepsi Penduduk Terhadap Faktor Bencana Banjir: Kajian Kes di Taman Sri Muda, ShahAlam, Selangor,” Prog. Eng. Appl. Technol., vol. 4, no. 2, pp. 611–617, 2023. [Google Scholar] [Crossref]

4. [4] N. S. Razmi and M. H. Arifin, “Kejadian Tanah Runtuh Di Kawasan Tanah Tinggi Malaysia,” Majalah Sains, Sep. 2022. [Online]. Available: https://www.majalahsains.com/kejadian-tanah-runtuh-di-kawasan-tanah-tinggi-malaysia/ [Google Scholar] [Crossref]

5. [5] M. I. I. Tahir, “’ Lubang maut ’ ancam nyawa penduduk Puncak Alam,” Sinar Harian, May 21, 2024. [Online]. Available: https://www.sinarharian.com.my/article/665894/edisi/selangor-kl/lubang-maut-ancam-nyawa-penduduk-puncak-alam [Google Scholar] [Crossref]

6. [6] M. R. Mamat, “Sampah dalam parit punca banjir kilat di Cameron Highlands,” Harian Metro, Feb. 2024. [Online]. Available: https://www.hmetro.com.my/mutakhir/2024/02/1058174/sampah-dalam-parit-punca-banjir-kilat-di-cameron-highlands [Google Scholar] [Crossref]

7. [7] S. L. Wei, “Pastikan Pokok Bandar ‘Sihat’, Elak Risiko Tumbang,” Fokus Bernama, Jul. 03, 2024. [Online]. Available: https://www.bernama.com/bm/bfokus/news.php?id=2313727). [Google Scholar] [Crossref]

8. [8] J. Soares and C. Coutinho, “Urban Issue Reporting Applications Towards Government 2.0,” ISMSIT 2024 - 8th Int. Symp. Multidiscip. Stud. Innov. Technol. Proc., 2024, doi: 10.1109/ISMSIT63511.2024.10757230. [Google Scholar] [Crossref]

9. [9] K. J. Schiff, “Does collective citizen input impact government service provision? Evidence from SeeClickFix requests,” Public Adm. Rev., vol. 85, no. 1, pp. 32–45, 2025, doi: 10.1111/puar.13747. [Google Scholar] [Crossref]

10. [10] M. H. Belarbi and Toufik, “The contribution of the Open data to the development of government digital services , ” SeeClickFix ” model . " SeeClickFix " جذونم ، ةيموكلحا ةيمقرلا تامدلخا ريوطت في ةحوتفلما تانايبلا ةمهاسم,” Al-riyada Bus. Econ. Journal/, vol. 06, no. June, pp. 414–428, 2020. [Google Scholar] [Crossref]

11. [11] T. Seto, “Trends in Citizen-Generated and Collaborative Urban Infrastructure Feedback Data : Toward Citizen-Oriented Infrastructure Management in Japan,” 2019, doi: 10.3390/ijgi8030115. [Google Scholar] [Crossref]

12. [12] T. Seto et al., “The Development of Open Source Based Citizen Collaboration Applications for Infrastructure Management: My City Report,” Proc. ICA, vol. 2, no. July, pp. 1–4, 2019, doi: 10.5194/ica-proc-2-116-2019. [Google Scholar] [Crossref]

13. [13] J. Evans, “FixMyStreet Update,” 2024. [Google Scholar] [Crossref]

14. [14] M. Guyot, I. Thomas, and S. O. Vanwambeke, “From complaints to insights: A geographical analysis of illegal dumping by citizen sensor data,” Cities, vol. 161, p. 105892, 2025, doi: https://doi.org/10.1016/j.cities.2025.105892. [Google Scholar] [Crossref]

15. [15] M. Ghahramani, N. J. Galle, F. Duarte, C. Ratti, and F. Pilla, “Leveraging artificial intelligence to analyze citizens’ opinions on urban green space,” City Environ. Interact., vol. 10, no. February, p. 100058, 2021, doi: 10.1016/j.cacint.2021.100058. [Google Scholar] [Crossref]

16. [16] M. Granero Moya, T. T. Phan, and D. Gatica-Perez, Zurich like New: Analyzing Open Urban Multimodal Data, vol. 1, no. 1. Association for Computing Machinery, 2021. doi: 10.1145/3475721.3484310. [Google Scholar] [Crossref]

17. [17] Majlis Keselamatan Negara, “Aplikasi MyJalan KKR,” Bernama, 2024. https://www.mkn.gov.my/web/ms/2024/10/03/aplikasi-myjalan-kkr/ [Google Scholar] [Crossref]

18. [18] F. Zeng, C. Pang, and H. Tang, “Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review,” Sensors, vol. 23, no. 17, 2023, doi: 10.3390/s23177475. [Google Scholar] [Crossref]

19. [19] M. S. Samsurijan, A. Ebekozien, N. A. Nor Azazi, M. M. Shaed, and R. F. Radin Badaruddin, “Artificial intelligence in urban services in Malaysia: a review,” PSU Res. Rev., 2023, doi: 10.1108/PRR-07-2021-0034. [Google Scholar] [Crossref]

20. [20] S. M. H. S. Rezvani, M. J. Falcão, D. Komljenovic, and N. M. de Almeida, “A Systematic Literature Review on Urban Resilience Enabled with Asset and Disaster Risk Management Approaches and GIS-Based Decision Support Tools,” Appl. Sci., vol. 13, no. 4, 2023, doi: 10.3390/app13042223. [Google Scholar] [Crossref]

21. [21] S. Gupta, S. Modgil, A. Kumar, U. Sivarajah, and Z. Irani, “Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations,” Int. J. Prod. Econ., vol. 254, no. January, p. 108642, 2022, doi: 10.1016/j.ijpe.2022.108642. [Google Scholar] [Crossref]

22. [22] N. Fukuda, C. Wu, S. Horiuchi, and K. Tayama, “Fault Report Generation for ICT Systems by Jointly Learning Time-series and Text Data,” Proc. IEEE/IFIP Netw. Oper. Manag. Symp. 2022 Netw. Serv. Manag. Era Cloudification, Softwarization Artif. Intell. NOMS 2022, pp. 1–9, 2022, doi: 10.1109/NOMS54207.2022.9789784. [Google Scholar] [Crossref]

23. [23] A. Kumar, A. Mishra, and S. Kumar, “Business Intelligence,” in Architecting a Modern Data Warehouse for Large Enterprises : Build Multi-cloud Modern Distributed Data Warehouses with Azure and AWS, Berkeley, CA: Apress, 2024, pp. 307–349. doi: 10.1007/979-8-8688-0029-0_7. [Google Scholar] [Crossref]

24. [24] J. Cao, “Introduction,” in E-Commerce Big Data Mining and Analytics, Singapore: Springer Nature Singapore, 2023, pp. 1–18. doi: 10.1007/978-981-99-3588-8_1. [Google Scholar] [Crossref]

25. [25] M. M. Ahmadian, D. Baker, and A. Paz, “Leveraging business intelligence solutions for urban parking management,” City, Cult. Soc., vol. 37, no. March, p. 100579, 2024, doi: 10.1016/j.ccs.2024.100579. [Google Scholar] [Crossref]

26. [26] R. Chalmeta and M. Ferrer Estevez, “Developing a business intelligence tool for sustainability management,” Bus. Process Manag. J., vol. 29, no. 8, pp. 188–209, 2023, doi: 10.1108/BPMJ-03-2023-0232. [Google Scholar] [Crossref]

27. [27] M. Muntean, “Business intelligence issues for sustainability projects,” Sustain., vol. 10, no. 2, 2018, doi: 10.3390/su10020335. [Google Scholar] [Crossref]

28. [28] M. Awad, A. Al Redhaei, and S. Fraihat, “Using Business Intelligence to Analyze Road Traffic Accidents,” ACM Int. Conf. Proceeding Ser., pp. 83–92, 2022, doi: 10.1145/3551504.3551507. [Google Scholar] [Crossref]

29. [29] D. J. Jenkins, “Application of Business Analytics Approaches to Address Climate-Change-Related Challenges,” 2024. [Google Scholar] [Crossref]

30. [30] D. Divya, B. Marath, and M. B. Santosh Kumar, “Review of fault detection techniques for predictive maintenance,” J. Qual. Maint. Eng., vol. 29, no. 2, pp. 420–441, Jan. 2023, doi: 10.1108/JQME-10-2020-0107. [Google Scholar] [Crossref]

31. [31] S. Pachar, D. Dudeja, N. Batra, V. Tomar, J. P. Bhimavarapu, and A. K. Singh, “Recent Developments in Machine Learning Predictive Analytics for Disaster Resource Allocation,” Engineering Proceedings, vol. 59, no. 1. 2023. doi: 10.3390/engproc2023059019. [Google Scholar] [Crossref]

32. [32] N. N., R. T. B., and R. A Kishorelal, “Predictive Analysis of Machine Learning Algorithms Applicable for Natural Disaster Management,” in Utilizing AI and Machine Learning for Natural Disaster Management, D. Satishkumar and M. Sivaraja, Eds., Hershey, PA, USA: IGI Global, 2024, pp. 65–79. doi: 10.4018/979-8-3693-3362-4.ch004. [Google Scholar] [Crossref]

33. [33] T. M. Michael, A. D. Michael, O. Alexander, S. O. Johnson, W. B. Preye, and O. H. Daraojimba, “A review of data analytics techniques in enhancing environmental risk assessments in the U.S. Geology Sector,” World J. Adv. Res. Rev., vol. 21, no. 1, pp. 1395–1411, 2024, doi: 10.30574/wjarr.2024.21.1.0169. [Google Scholar] [Crossref]

34. [34] C. C. Ebulue, V. E. Ogochukwu, R. E. Ogochukwu, and S. E. Chukwunonso, “Environmental data in epidemic forecasting: Insights from predictive analytics,” Comput. Sci. IT Res. J., vol. 5, no. 5, pp. 1113–1125, 2024, doi: 10.51594/csitrj.v5i5.1118. [Google Scholar] [Crossref]

35. [35] A. B. Bouttell et al., “Predictive Analytics for Supporting Environmental Sustainability and Disaster Management,” in 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 2022, pp. 1256–1261. doi: 10.1109/COMPSAC54236.2022.00198. [Google Scholar] [Crossref]

36. [36] S. Ghaffarian, F. R. Taghikhah, and H. R. Maier, “Explainable artificial intelligence in disaster risk management: Achievements and prospective futures,” Int. J. Disaster Risk Reduct., vol. 98, p. 104123, 2023, doi: https://doi.org/10.1016/j.ijdrr.2023.104123. [Google Scholar] [Crossref]

37. [37] P. Anuja, M. Kaustubh, and P. Atharva, “Predictive Analytics for Disaster Management,” Int. J. Eng. Res., vol. V9, no. 02, pp. 773–774, 2020, doi: 10.17577/ijertv9is020415. [Google Scholar] [Crossref]

38. [38] H. Wu, “Efficient Environmental Monitoring System Based on Data Fusion and Predictive Prediction BT - Cyber Security Intelligence and Analytics,” Z. Xu, R. M. Parizi, O. Loyola-González, and X. Zhang, Eds., Cham: Springer International Publishing, 2021, pp. 246–252. [Google Scholar] [Crossref]

39. [39] F. N. M. Leza, N. I. M. Saleh, and N. A. Arbain, “Analyzing Factors of Public Engagement with Community Reporting Systems Through a Business Intelligence Approach,” in Advances in Visual Informatics, H. Badioze Zaman, P. Robinson, A. F. Smeaton, T. K. Shih, B. N. Jørgensen, Z. Huajing, L. Xiaoping, N. Mohamad Ali, and E. S. Mat Surin, Eds., Singapore: Springer Nature Singapore, 2026, pp. 417–433. [Google Scholar] [Crossref]

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