A Framework for Monitoring the Effectiveness of Ecosystem-Based Adaptation Strategies Using Internet of Things and Machine Learning Techniques

Authors

  • Martin Kuradusenge

    Department of Computer and software Engineering, School of ICT, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
  • Eric Hitimana

    Department of Computer and software Engineering, School of ICT, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
  • Jean Baptiste Minani

    Department of Computer Sciences and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
  • Zubeda Ukundimana

    Department of Chemistry, School of Science, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
  • Florence Mukamanzi

    Department of Business and Information Technology, School of Economics, College of Business and Economics, University of Rwanda, Kigali P.O. Box 4285, Rwanda
  • Omar Janvier Sinayobye

    Department of Computer and software Engineering, School of ICT, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
  • Aime Thierry Nizeyimana

    Department of Computer and software Engineering, School of ICT, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda
  • Nadege Gaju

    Department of Computer and software Engineering, School of ICT, College of Science and Technology, University of Rwanda, Kigali P.O. Box 4285, Rwanda

DOI:

https://doi.org/10.30564/jees.v6i3.6962
Received: 28 July 2024 | Revised: 30 August 2024 | Accepted: 2 September 2024 | Published Online: 8 October 2024

Abstract

Climate change poses a threat to the global ecosystem. Many countries adopt various approaches, including ecosystem-based adaptation (EbA), to address this problem. However, the assessment of the effectiveness of the EbA interventions is conducted manually, is resource-intensive, and is focused on short-term outputs. These limitations underscore a critical gap: the need for a comprehensive, automated system that enables long-term monitoring and predictive analysis. This study aimed to address this gap by developing an innovative framework that integrates Internet of Things (IoT) devices and machine learning (ML) algorithms to continuously monitor weather, hydrological, environmental, and other variables. We conducted a thorough analysis to design an appropriate framework. In addition, to obtain the relevant information and data, we conducted interviews with the local community and collected secondary data from various sources. The proposed framework consists of five layers: (i) EbA interventions; (ii) IoT-based key performance indicators (KPI) for monitoring and evaluation (M&E); (iii) primary data collection; (iv) data storage; and (v) application. As a proof of concept, we developed a system that supports early flood and drought alerts while simultaneously providing long-term evaluations of the effectiveness of EbA strategies. The developed system consists of IoT devices and a web application integrated with machine learning (ML). We set up and tested the IoT devices before deploying them in the study area. The devices capture data for two primary purposes: (1) short-term: flood detection and alerting, and (2) long-term: drought prediction and evaluation of EbA effectiveness through continuous data analysis. This research represents a significant advancement in the automation and long-term assessment of climate adaptation measures, offering a scalable and effective solution to disaster risk reduction.

Keywords:

EbA; flood; drought; IoT; weather monitoring; machine learning; KPI

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Kuradusenge, M., Hitimana, E., Minani, J. B., Ukundimana, Z., Mukamanzi, F., Sinayobye, O. J., Nizeyimana, A. T., & Gaju, N. (2024). A Framework for Monitoring the Effectiveness of Ecosystem-Based Adaptation Strategies Using Internet of Things and Machine Learning Techniques. Journal of Environmental & Earth Sciences, 6(3), 197–216. https://doi.org/10.30564/jees.v6i3.6962

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Vol. 6 , Iss. 3 (October 2024)

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Copyright © 2024 Martin Kuradusenge, Eric Hitimana, Jean Baptiste Minani, Zubeda Ukundimana, Florence Mukamanzi, Omar Janvier Sinayobye, Aime Thierry Nizeyimana, Nadege Gaju

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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