EcoRisk-AI: A multimodal artificial intelligence framework for early prediction of mining environmental risks in Indonesia
DOI:
https://doi.org/10.61511/rstde.v2i2.2025.2409Keywords:
artificial intelligence, environment, mining, prediction, sustainabilityAbstract
Background: Mining contributes significantly to Indonesia’s economy but simultaneously generates major ecological risks such as land degradation, acid mine drainage, and landslides, which threaten ecosystems and local communities. Conventional monitoring systems remain fragmented and reactive, creating an urgent need for a preventive and predictive solution tailored to local conditions. Methods: This study introduces EcoRisk-AI, a multimodal artificial intelligence framework designed for early prediction of mining-related environmental risks, with a conceptual application focus on high-risk regions such as Kalimantan and Sulawesi. The system integrates diverse data sources, including satellite imagery, ground-based Internet of Things (IoT) sensors, meteorological datasets, and field inspection reports. EcoRisk-AI consists of four components: data aggregation, a detailed spatio-temporal preprocessing unit, a hybrid machine learning engine, and a decision-support interface. The analytical process sequentially processes data, using Convolutional Neural Networks (CNNs) for spatial features, Long Short-Term Memory (LSTM) for temporal trends, and decision tree-based models for final risk classification. Findings: EcoRisk-AI demonstrates the capacity to provide adaptive, location-specific predictions of ecological hazards in mining regions. The integration of multimodal data enhances sensitivity and accuracy, while the cloud-based visualization dashboard allows stakeholders to access interactive risk maps and automated alerts. The framework's validity is conceptually demonstrated through quantitative "what-if" scenarios, supported by Digital Twin simulations, to test system resilience. This paper details the system architecture and its proposed validation metrics such as Accuracy, Precision, Recall, F1-Score. Conclusion: EcoRisk-AI offers a proactive solution for sustainable mining risk management in Indonesia, enabling early warning and preventive measures against ecological disasters. Novelty/Originality of this article: This work introduces a unique integration of multimodal environmental data and hybrid artificial intelligence techniques specifically adapted to the Indonesian mining context. EcoRisk-AI contributes an innovative predictive framework that bridges technological capability with sustainable development goals, offering new insights into disaster mitigation and environmental governance. The framework is designed for scalability and replicability, offering a model adaptable to other developing contexts.
References
Agusdinata, D. B., Liu, W., Sulistyo, S., LeBillon, P., & Wegner, J. (2022). Evaluating sustainability impacts of critical mineral extractions: Integration of life cycle sustainability assessment and SDGs frameworks. Journal of Industrial Ecology, 27(3), 746–759. https://doi.org/10.1111/jiec.13317
Akhyar, A., Asyraf Zulkifley, M., Lee, J., Song, T., Han, J., Cho, C., Hyun, S., Son, Y., & Hong, B.-W. (2024). Deep artificial intelligence applications for natural disaster management systems: A methodological review. Ecological Indicators, 163, 112067. https://doi.org/10.1016/j.ecolind.2024.112067
Ali, S. H., Giurco, D., Arndt, N., Nickless, E., Brown, G., Demetriades, A., Durrheim, R., Enriquez, M. A., Kinnaird, J., Littleboy, A., Meinert, L. D., Oberhänsli, R., Salem, J., Schodde, R., Schneider, G., Vidal, O., & Yakovleva, N. (2017). Mineral supply for sustainable development requires resource governance. Nature, 543(7645), 367–372. https://doi.org/10.1038/nature21359
Alotaibi, E., & Nassif, N. (2024). Artificial intelligence in environmental monitoring: In-depth analysis. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00198-1
Ambika, K., Alzaben, N., Alghamdi, A. G., & Venkatraman, S. (2025). Integrated geotechnical and remote sensing-based monitoring of unstable slopes for landslide early warning using IoT and sensor networks. Journal of South American Earth Sciences, 164, 105666. https://doi.org/10.1016/j.jsames.2025.105666
Amir, N., Grace Natalia Mintia, Lady, & Maulina Kharis, T. (2019). Responsibilities of mining entrepreneurs for losses from mining activities in Indonesia (Case study in Samarinda Province of East Kalimantan). Proceedings of the 2nd International Conference on Indonesian Legal Studies (ICILS 2019). https://doi.org/10.2991/icils-19.2019.24
BPS. (2025). Distribusi Persentase Produk Domestik Bruto Atas Dasar Harga Berlaku Menurut Lapangan Usaha. Badan Pusat Statistik. https://www.bps.go.id/id/statistics-table/3/T0UxS09GQlBTbk5QWTBNdlVWUmxSMjV3Y3l0VWR6MDkjMw==/distribusi-persentase-produk-domestik-bruto-atas-dasar-harga-berlaku-menurut-lapangan-usaha--persen---2021.html?year=2024
Bhowmik, R. T., Jung, Y. S., Aguilera, J. A., Prunicki, M., & Nadeau, K. (2023). A multi-modal wildfire prediction and early-warning system based on a novel machine learning framework. Journal of Environmental Management, 341, 117908. https://doi.org/10.1016/j.jenvman.2023.117908
Cabello-Solorzano, K., Ortigosa de Araujo, I., Peña, M., Correia, L., & J. Tallón-Ballesteros, A. (2023). The Impact of Data Normalization on the Accuracy of Machine Learning Algorithms: A Comparative Analysis. In Lecture Notes in Networks and Systems (pp. 344–353). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-42536-3_33
Dayo-Olupona, O., Genc, B., Celik, T., & Bada, S. (2023). Adoptable approaches to predictive maintenance in mining industry: An overview. Resources Policy, 86, 104291. https://doi.org/10.1016/j.resourpol.2023.104291
Dey, P., Chaulya, S. K., & Kumar, S. (2021). Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system. Process Safety and Environmental Protection, 152, 249–263. https://doi.org/10.1016/j.psep.2021.06.005
Dong, L., Zhang, J., Zhang, Y., & Zhang, B. (2024). Landslide risk assessment in mining areas using hybrid machine learning methods under fuzzy environment. Ecological Indicators, 167, 112736. https://doi.org/10.1016/j.ecolind.2024.112736
Essamlali, I., Nhaila, H., & El Khaili, M. (2024). Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon, 10(6), e27920. https://doi.org/10.1016/j.heliyon.2024.e27920
Gal, Y., & Ghahramani, Z. (2015, June 6). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. arXiv.Org. https://arxiv.org/abs/1506.02142
Garcia, A., Saez, Y., Harris, I., Huang, X., & Collado, E. (2025). Advancements in air quality monitoring: A systematic review of IoT-based air quality monitoring and AI technologies. Artificial Intelligence Review, 58(9). https://doi.org/10.1007/s10462-025-11277-9
Gerassis, S., Giráldez, E., Pazo-Rodríguez, M., Saavedra, Á., & Taboada, J. (2021). AI approaches to environmental impact assessments (eias) in the mining and metals sector using automl and bayesian modeling. Applied Sciences, 11(17), 7914. https://doi.org/10.3390/app11177914
Greif, L., Kimmig, A., El Bobbou, S., Jurisch, P., & Ovtcharova, J. (2024). Strategic view on the current role of AI in advancing environmental sustainability: A SWOT analysis. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00146-z
Guo, Y., Wang, C., Lei, S., Yang, J., & Zhao, Y. (2020). A framework of spatio-temporal fusion algorithm selection for landsat NDVI time series construction. ISPRS International Journal of Geo-Information, 9(11), 665. https://doi.org/10.3390/ijgi9110665
Hammerschmidt, T., Stolz, K., & Posegga, O. (2025). Bridging the gap: Inequalities that divide those who can and cannot create sustainable outcomes with AI. Behaviour & Information Technology, 1–30. https://doi.org/10.1080/0144929x.2025.2500451
Kurniawan, A. R., Murayama, T., & Nishikizawa, S. (2019). A qualitative content analysis of environmental impact assessment in Indonesia: A case study of nickel smelter processing. Impact Assessment and Project Appraisal, 38(3), 194–204. https://doi.org/10.1080/14615517.2019.1672452
Lary, D. J., Alavi, A. H., Gandomi, A. H., & Walker, A. L. (2016). Machine learning in geosciences and remote sensing. Geoscience Frontiers, 7(1), 3–10. https://doi.org/10.1016/j.gsf.2015.07.003
Lin, S., Liang, Z., Guo, H., Hu, Q., Cao, X., & Zheng, H. (2025). Application of machine learning in early warning system of geotechnical disaster: A systematic and comprehensive review. Artificial Intelligence Review, 58(6). https://doi.org/10.1007/s10462-025-11175-0
Lundberg, S., & Lee, S.-I. (2017, May 22). A unified approach to interpreting model predictions. arXiv.Org. https://arxiv.org/abs/1705.07874
Mathys, A. S., van Vianen, J., Rowland, D., Narulita, S., Palomo, I., Pascual, U., Sutherland, I. J., Ahammad, R., & Sunderland, T. (2023). Participatory mapping of ecosystem services across a gradient of agricultural intensification in West Kalimantan, Indonesia. Ecosystems and People, 19(1). https://doi.org/10.1080/26395916.2023.2174685
Muhammad, S., Arifin, S., Syam, R., Tamma, S., Hans, A., Hanami, Z. A., Aprianto, & Putra, B. A. (2024). Corporate social responsibility programs in mining areas: Insights from stakeholder groups in Indonesia. Cogent Social Sciences, 10(1). https://doi.org/10.1080/23311886.2024.2357675
Nobahar, P., Xu, C., Dowd, P., & Shirani Faradonbeh, R. (2024). Exploring digital twin systems in mining operations: A review. Green and Smart Mining Engineering, 1(4), 474–492. https://doi.org/10.1016/j.gsme.2024.09.003
Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A., & Oladapo, B. I. (2024). Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions. Hygiene and Environmental Health Advances, 12, 100114. https://doi.org/10.1016/j.heha.2024.100114
Sanchez, T. W., Brenman, M., & Ye, X. (2024). The ethical concerns of artificial intelligence in urban planning. Journal of the American Planning Association, 91(2), 294–307. https://doi.org/10.1080/01944363.2024.2355305
Scalambrin, L., Zanella, A., & Vilajosana, X. (2023). LoRa multi-hop networks for monitoring underground mining environments. 2023 IEEE Globecom Workshops (GC Wkshps), 696–701. https://doi.org/10.1109/gcwkshps58843.2023.10464954
Singh, M., & Yan, S. (2021). Spatial–temporal variations in deforestation hotspots in Sumatra and Kalimantan from 2001–2018. Ecology and Evolution, 11(12), 7302–7314. https://doi.org/10.1002/ece3.7562
Tost, M., Hitch, M., Chandurkar, V., Moser, P., & Feiel, S. (2018). The state of environmental sustainability considerations in mining. Journal of Cleaner Production, 182, 969–977. https://doi.org/10.1016/j.jclepro.2018.02.051
United Nations. (2015). THE 17 GOALS. Sustainable Development. https://sdgs.un.org/goals
Viet Du, Q. V., Nguyen, H. D., Pham, V. T., Nguyen, C. H., Nguyen, Q.-H., Bui, Q.-T., Doan, T. T., Tran, A. T., & Petrisor, A.-I. (2023). Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam. Geocarto International, 38(1). https://doi.org/10.1080/10106049.2023.2172218
Wahyono, Y., Sasongko, N. A., Trench, A., Anda, M., Hadiyanto, H., Aisyah, N., Anisah, A., Ariyanto, N., Kumalasari, I., Putri, V. Z. E., Lestari, M. C., Panggabean, L. P., Ridlo, R., Sundari, S., Suryaningtyas, A. D., Novianti, E. D., Hakim, M. R. F., Prihatin, A. L., & Matin, H. H. A. (2024). Evaluating the impacts of environmental and human health of the critical minerals mining and processing industries in Indonesia using life cycle assessment. Case Studies in Chemical and Environmental Engineering, 10, 100944. https://doi.org/10.1016/j.cscee.2024.100944
Yadav, S., Samadhiya, A., Kumar, A., Luthra, S., & Pandey, K. K. (2024). Environmental, social, and governance (ESG) reporting and missing (M) scores in the industry 5.0 era: Broadening firms’ and investors’ decisions to achieve sustainable development goals. Sustainable Development, 33(3), 3455–3477. https://doi.org/10.1002/sd.3306
Zhan, S., Huang, L., Luo, G., Zheng, S., Gao, Z., & Chao, H.-C. (2025). A review on federated learning architectures for privacy-preserving AI: Lightweight and secure cloud–edge–end collaboration. Electronics, 14(13), 2512. https://doi.org/10.3390/electronics14132512
Downloads
Published
Issue
Section
Citation Check
License
Copyright (c) 2025 Muhammad Ilham Ramadhan
This work is licensed under a Creative Commons Attribution 4.0 International License.
