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

H. Nurwarsito, D. Suprayogo, S. P. Sakti, Cahyo Prayogo, Novanto Yudistira, Muhammad Rifqi Fauzi, Simon Oakley, W. Mahmudy
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Abstract

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.
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利用机器学习估算咖啡-松树农林业缺失的小气候数据
本研究全面分析了在 UB 森林的咖啡-松树农林地背景下处理缺失小气候数据的各种估算方法。研究利用大数据和机器学习方法,评估了插值法、移位插值法、K-近邻法(KNN)和线性回归法在多个时间框架内(6 小时、每天、每周和每月)对缺失微气候数据进行估算的有效性。使用四个关键评估指标平均绝对误差 (MAE)、平均平方误差 (MSE)、均方根误差 (RMSE) 和平均绝对百分比误差 (MAPE) 对这些方法的性能进行了细致的评估。结果表明,线性回归法在所有时间范围内的表现始终优于其他方法,在 MAE、MSE、RMSE 和 MAPE 方面的误差率最低。这一发现强调了线性回归法在处理农林系统中小气候数据固有的变异性时的稳健性和精确性。这项研究强调了准确的数据估算在农林业研究中的关键作用,并指出了机器学习技术在推进环境数据分析方面的潜力。从这项研究中获得的见解为环境科学领域做出了重大贡献,为提高农林业小气候模型的准确性提供了可靠的方法论,从而促进了可持续生态系统管理的知情决策。
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来源期刊
International Journal of Advances in Intelligent Informatics
International Journal of Advances in Intelligent Informatics Computer Science-Computer Vision and Pattern Recognition
CiteScore
3.00
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0.00%
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