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Engineering Proceedings
Volume 9
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Proceeding Paper

A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios †

by
Roberto F. Silva
1,*,
Maria C. Fava
2,
Antonio M. Saraiva
3,
Eduardo M. Mendiondo
4,
Carlos E. Cugnasca
3 and
Alexandre C. B. Delbem
5
1
Institute of Advanced Studies (IEA), University of São Paulo (USP), Sao Paulo 05508-060, Brazil
2
Institute of Exact and Technological Sciences (IEP), Federal University of Viçosa (UFV), Rio Paranaiba 38810-000, Brazil
3
Polytechnic School, University of São Paulo (USP), Sao Paulo 05508-010, Brazil
4
São Carlos School of Engineering (EESC), University of São Paulo (USP), Sao Carlos 13566-590, Brazil
5
Institute of Mathematics and Computer Sciences (ICMC), University of São Paulo (USP), Sao Carlos 13566-590; Brazil
*
Author to whom correspondence should be addressed.
†
Presented at the 13th EFITA International Conference, online, 25–26 May 2021.
Eng. Proc. 2021, 9(1), 39; https://doi.org/10.3390/engproc2021009039
Published: 31 December 2021
(This article belongs to the Proceedings of The 13th EFITA International Conference)
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Abstract

This work proposes a data-driven theoretical framework for addressing: (i) extreme climate events prediction through multi-hazard risk mapping using remote sensing, artificial intelligence, and hydrological models, considering multiple hazards; and (ii) environmental monitoring using on-site data collection and IoT technologies. The framework considers the possibility of evaluating multiple climate change scenarios for improving decision-making in terms of Government policies and farm planning. Its main requirements are gathered based on a literature review. Several essential metrics that can be evaluated, considering both supervised and unsupervised metrics and key performance indicators considering the triple bottom line aspects, are also proposed. The framework also adopts multi-hazard (considering several hazards) and multi-risk (considering several relevant stakeholders) aspects and can be used to simulate different scenarios, an essential task for improving decision-making.
Keywords: artificial intelligence; climate change; environmental monitoring; IoT technologies; multi-hazard risk mapping artificial intelligence; climate change; environmental monitoring; IoT technologies; multi-hazard risk mapping

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MDPI and ACS Style

Silva, R.F.; Fava, M.C.; Saraiva, A.M.; Mendiondo, E.M.; Cugnasca, C.E.; Delbem, A.C.B. A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios. Eng. Proc. 2021, 9, 39. https://doi.org/10.3390/engproc2021009039

AMA Style

Silva RF, Fava MC, Saraiva AM, Mendiondo EM, Cugnasca CE, Delbem ACB. A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios. Engineering Proceedings. 2021; 9(1):39. https://doi.org/10.3390/engproc2021009039

Chicago/Turabian Style

Silva, Roberto F., Maria C. Fava, Antonio M. Saraiva, Eduardo M. Mendiondo, Carlos E. Cugnasca, and Alexandre C. B. Delbem. 2021. "A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios" Engineering Proceedings 9, no. 1: 39. https://doi.org/10.3390/engproc2021009039

APA Style

Silva, R. F., Fava, M. C., Saraiva, A. M., Mendiondo, E. M., Cugnasca, C. E., & Delbem, A. C. B. (2021). A Theoretical Framework for Multi-Hazard Risk Mapping on Agricultural Areas Considering Artificial Intelligence, IoT, and Climate Change Scenarios. Engineering Proceedings, 9(1), 39. https://doi.org/10.3390/engproc2021009039

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