Imputation of missing microclimate data of coffee-pine agroforestry with machine learning

Nurwarsito, Heru; Suprayogo, Didik; Sakti, Setyawan Purnomo; Prayogo, Cahyo; Yudistira, Novanto; Fauzi, Muhammad Rifqi; Oakley, Simon ORCID: https://orcid.org/0000-0002-5757-7420; Mahmudy, Wayan Firdaus. 2024 Imputation of missing microclimate data of coffee-pine agroforestry with machine learning. International Journal of Advances in Intelligent Informatics, 10 (1), 27. 22, pp. https://doi.org/10.26555/ijain.v10i1.1439

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Abstract/Summary

This research presents a comprehensive analysis of various imputation methods for addressing missing microclimate data in the context of coffee-pine agroforestry land in UB Forest. Utilizing Big data and Machine learning methods, the research evaluates the effectiveness of imputation missing microclimate data with Interpolation, Shifted Interpolation, K-Nearest Neighbors (KNN), and Linear Regression methods across multiple time frames - 6 hours, daily, weekly, and monthly. The performance of these methods is meticulously assessed using four key evaluation metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that Linear Regression consistently outperforms other methods across all time frames, demonstrating the lowest error rates in terms of MAE, MSE, RMSE, and MAPE. This finding underscores the robustness and precision of Linear Regression in handling the variability inherent in microclimate data within agroforestry systems. The research highlights the critical role of accurate data imputation in agroforestry research and points towards the potential of machine learning techniques in advancing environmental data analysis. The insights gained from this research contribute significantly to the field of environmental science, offering a reliable methodological approach for enhancing the accuracy of microclimate models in agroforestry, thereby facilitating informed decision-making for sustainable ecosystem management.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.26555/ijain.v10i1.1439
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2442-6571
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	microclimate data, interpolation, shifted interpolation, k-nearest neighbors (KNN), linear regression
NORA Subject Terms:	Ecology and Environment Botany Data and Information
Date made live:	22 Mar 2024 10:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537154

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