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 Journal of Ambient Intelligence and Smart Environments Pub Date : 2023-03-17 DOI:10.3233/ais-220477
Emna Ben Abdallah, Rima Grati, Khouloud Boukadi
{"title":"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","authors":"Emna Ben Abdallah, Rima Grati, Khouloud Boukadi","doi":"10.3233/ais-220477","DOIUrl":null,"url":null,"abstract":"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).","PeriodicalId":49316,"journal":{"name":"Journal of Ambient Intelligence and Smart Environments","volume":"9 1","pages":"89-110"},"PeriodicalIF":1.8000,"publicationDate":"2023-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Towards an explainable irrigation scheduling approach by predicting soil moisture and evapotranspiration via multi-target regression\",\"authors\":\"Emna Ben Abdallah, Rima Grati, Khouloud Boukadi\",\"doi\":\"10.3233/ais-220477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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).\",\"PeriodicalId\":49316,\"journal\":{\"name\":\"Journal of Ambient Intelligence and Smart Environments\",\"volume\":\"9 1\",\"pages\":\"89-110\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-03-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Ambient Intelligence and Smart Environments\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.3233/ais-220477\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Ambient Intelligence and Smart Environments","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/ais-220477","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
Evaluation factors of adopting smart home IoT: The hybrid fuzzy MCDM approach for robot vacuum Hybrid fuzzy response threshold-based distributed task allocation in heterogeneous multi-robot environment From programming-to-modeling-to-prompts smart ubiquitous applications A UAV deployment strategy based on a probabilistic data coverage model for mobile CrowdSensing applications Memoization based priority-aware task management for QoS provisioning in IoT gateways
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1