Utilization of Artificial Intelligence for Spatial Decision Support System
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Artificial Intelligence, Spatial Decision, Urban Planning
Abstract
The integration of Artificial Intelligence (AI) into Spatial Decision Support Systems (SDM) is a transformative advancement in improving decision-making processes in various fields, including urban planning, environmental management, and disaster response. This research uses a literature review methodology to systematically collect, analyze, and synthesize existing scientific articles, conference papers, and relevant reports related to AI applications in SDSS. The findings of this study reveal that AI technologies, such as machine learning and natural language processing, significantly enhance data processing capabilities, enabling the analysis of complex spatial data and the identification of hidden patterns that may be missed by traditional methods. Despite the great benefits, challenges related to data quality, ethical considerations, and the need for capacity building among stakeholders are critical to the successful implementation of AI in SDSS. It can be concluded that while AI has the potential to revolutionize spatial decision-making, ongoing research is essential to develop best practices, address ethical implications, and foster collaboration among various stakeholders to create a more sustainable and resilient society.
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References
Alamanos, A., Rolston, A., & Papaioannou, G. (2021). Development of a Decision Support System for Sustainable Environmental Management and Stakeholder Engagement. Hydrology, 8(1), 40. https://doi.org/10.3390/hydrology8010040
Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. Applied Sciences, 11(11), 5088. https://doi.org/10.3390/app11115088
Bazan-Krzywosza?ska, A., Lach, R., & Mrówczy?ska, M. (2020). City as a System Supported by Artificial Intelligence. Urban and Regional Planning, 5(2), 32. https://doi.org/10.11648/j.urp.20200502.11
Ding, R.-X., Palomares, I., Wang, X., Yang, G.-R., Liu, B., Dong, Y., Herrera-Viedma, E., & Herrera, F. (2020). Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion, 59, 84–102. https://doi.org/10.1016/j.inffus.2020.01.006
Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., & Sousa, J. M. C. (2020). Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artificial Intelligence in Medicine, 102, 101762. https://doi.org/10.1016/j.artmed.2019.101762
Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308(1–2), 215–274. https://doi.org/10.1007/s10479-020-03856-6
Kim, B., Park, J., & Suh, J. (2020). Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information. Decision Support Systems, 134, 113302. https://doi.org/10.1016/j.dss.2020.113302
Latue, P. C., & Rakuasa, H. (2024). SPATIAL TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGES IN AMBON ISLAND FROM LANDSAT 8 IMAGE DATA USING GEOGLE EARTH ENGINE. Journal of Data Analytics, Information, and Computer Science, 1(3), 134–142. https://doi.org/10.59407/jdaics.v1i3.751
Muin, A., & Rakuasa, H. (2023). Pemanfaat Geographic Artificial Intelligence (Geo-AI) Untuk Identifikasi Daerah Rawan Banjir Di Kota Ambon. Gudang Jurnal Multidisiplin Ilmu, 1(2), 58-63. https://doi.org/https://doi.org/10.59435/gjmi.v1i2.24
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., Kompatsiaris, I., Kinder?Kurlanda, K., Wagner, C., Karimi, F., Fernandez, M., Alani, H., Berendt, B., Kruegel, T., Heinze, C., … Staab, S. (2020). Bias in data?driven artificial intelligence systems—An introductory survey. WIREs Data Mining and Knowledge Discovery, 10(3). https://doi.org/10.1002/widm.1356
Rakuasa, H., & Latue, P. C. (2024). Modelling Mangrove Biomass 2019-2023 with Global Ecosystem Dynamics Investigation (GEDI) Data using Google Earth Engine, Case Study: Kayeli Bay, Buru Island, Indonesia. Journal of International Multidisciplinary Research, 2(8), 37–45. https://doi.org/https://doi.org/10.62504/62a3w568
Rakuasa, H., Hehanussa, F. S., & Latue, P. C. (2024). Evaluating the Impact of Climate Change on Puncak Jaya’s, Indonesia Glaciers through Satellite Data and Google Earth Engine. Journal of Moeslim Research Technik, 1(4), 183–190. https://doi.org/https://doi.org/10.70177/technik.v1i4.1186
Rakuasa, H. (2023). Integration of Artificial Intelligence in Geography Learning: Challenges and Opportunities. Sinergi International Journal of Education, 1(2), 75–83. https://doi.org/https://doi.org/10.61194/education.v1i2.71
Rakuasa, H., Joshua, B., & Somae, G. (2024). Modeling Flood Hazards in Ambon City Watersheds: Case Studies of Wai Batu Gantung. Journal of Information Systems and Technology Research, 3(2), 86–91. https://doi.org/10.55537/jistr.v3i2.836
Rakuasa, H., Ria Karuna, J., & Christi Latue, P. (2024). URBAN LANDSCAPE TRANSFORMATION: LAND COVER CHANGE ANALYSIS IN SIRIMAU SUB-DISTRICT, AMBON CITY. Journal of Data Analytics, Information, and Computer Science, 1(2), 63–70. https://doi.org/10.59407/jdaics.v1i2.649
Tyler, N. S., & Jacobs, P. G. (2020). Artificial Intelligence in Decision Support Systems for Type 1 Diabetes. Sensors, 20(11), 3214. https://doi.org/10.3390/s20113214
Wen, R., & Li, S. (2022). Spatial Decision Support Systems with Automated Machine Learning: A Review. ISPRS International Journal of Geo-Information, 12(1), 12. https://doi.org/10.3390/ijgi12010012
Zhai, Z., Martínez, J. F., Beltran, V., & Martínez, N. L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256
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Copyright (c) 2024 Glendy Somae, Heinrich Rakuasa
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