利用多目标回归预测土壤水分和蒸散量,探索一种可解释的灌溉调度方法

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Emna Ben Abdallah, Rima Grati, Khouloud Boukadi
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引用次数: 2

摘要

显著的人口增长和持续的社会经济发展增加了对灌溉农业和农业集约化的依赖。然而,准确预测作物需水量是有问题的,因为它受到天气、土壤和水性质等多种因素的影响。许多研究表明,基于两种灌溉策略(即蒸散发和土壤灌溉)的混合灌溉系统可以提供可靠的灌溉系统。后者还可以提醒农民和其他专家注意噪声、错误的传感器信号、大量相关的输入和目标变量以及不完整或缺失的数据等现象,特别是当两种灌溉策略产生不一致的结果时。因此,我们提出了多目标土壤水分和蒸散发预测(Multi-Target soil moisture and evapotranspiration prediction, MTR-SMET)来估算土壤水分和蒸散发。然后将这些预测用于根据粮农组织(FAO)和基于土壤的方法计算水需求。此外,我们提出了一个可解释的MTR-SMET (xmrr - smet),它使用几个可解释的人工智能为给定的预测提供简单的视觉解释,向农民/用户解释基于ml的灌溉。这是第一次尝试解释并为基于机器学习的灌溉方法的输出提供有意义的见解。实验表明,提出的MTR-SMET模型错误率低(即MSE = 0.00015, RMSE = 0.0039, MAE = 0.002), r2得分高(即0.9676)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards an explainable irrigation scheduling approach by predicting soil moisture and evapotranspiration via multi-target regression
Significant population growth and ongoing socioeconomic development have increased reliance on irrigated agriculture and agricultural intensification. However, accurately predicting crop water demand is problematic since it is affected by several factors such as weather, soil, and water properties. Many studies have shown that a hybrid irrigation system based on two irrigation strategies (i.e., evapotranspiration and soil-based irrigation) can provide a credible and reliable irrigation system. The latter can also alert farmers and other experts to phenomena such as noise, erroneous sensor signals, numerous correlated input and target variables, and incomplete or missing data, especially when the two irrigation strategies produce inconsistent results. Hence, we propose Multi-Target soil moisture and evapotranspiration prediction (MTR-SMET) for estimating soil moisture and evapotranspiration. These predictions are then used to compute water needs based on Food and Agriculture Organization (FAO) and soil-based methods. Besides, we propose an explainable MTR-SMET (xMTR-SMET) that explains the ML-based irrigation to the farmers/users using several explainable AI to provide simple visual explanations for the given predictions. It is the first attempt that explains and offers meaningful insights into the output of a machine learning-based irrigation approach. The conducted experiments showed that the proposed MTR-SMET model achieves low error rates (i.e., MSE = 0.00015, RMSE = 0.0039, MAE = 0.002) and high R 2 score (i.e., 0.9676).
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来源期刊
Journal of Ambient Intelligence and Smart Environments
Journal of Ambient Intelligence and Smart Environments COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
4.30
自引率
17.60%
发文量
23
审稿时长
>12 weeks
期刊介绍: The Journal of Ambient Intelligence and Smart Environments (JAISE) serves as a forum to discuss the latest developments on Ambient Intelligence (AmI) and Smart Environments (SmE). Given the multi-disciplinary nature of the areas involved, the journal aims to promote participation from several different communities covering topics ranging from enabling technologies such as multi-modal sensing and vision processing, to algorithmic aspects in interpretive and reasoning domains, to application-oriented efforts in human-centered services, as well as contributions from the fields of robotics, networking, HCI, mobile, collaborative and pervasive computing. This diversity stems from the fact that smart environments can be defined with a variety of different characteristics based on the applications they serve, their interaction models with humans, the practical system design aspects, as well as the multi-faceted conceptual and algorithmic considerations that would enable them to operate seamlessly and unobtrusively. The Journal of Ambient Intelligence and Smart Environments will focus on both the technical and application aspects of these.
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