Soil temperature estimation at different depths using machine learning paradigms based on meteorological data

IF 2.9 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Anurag Malik, Gadug Sudhamsu, Manjinder Kaur Wratch, Sandeep Singh, Srinadh Raju Sagiraju, Lamjed Mansour, Priya Rai, Rawshan Ali, Alban Kuriqi, Krishna Kumar Yadav
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Abstract

Knowledge of soil temperature (ST) is important for analysing environmental conditions and climate change. Moreover, ST is a vital element of soil that impacts crop growth as well as the germination of the seeds. In this study, four machine-learning (ML) paradigms including random forest (RF), radial basis neural network (RBNN), multi-layer perceptron neural network (MLPNN), and co-active neuro-fuzzy inference system (CANFIS) were used for estimation of daily ST at different soil depths (i.e. 5 cm: ST5; 15 cm: ST15; and 30 cm: ST30) during 2016–2019 at Bathinda weather station, located in South-western Punjab (India). Five different combinations were formulated using four meteorological data, namely Tmean (mean air temperature), RH (relative humidity), WS (wind speed), and SSH (bright sunshine hours), and the optimal one was nominated by employing the gamma test (GT) for each soil depths, respectively. During the validation period, the outcomes of the RF, RBNN, MLPNN, and CANFIS models were evaluated according to performance metrics such as mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), coefficient of efficiency (COE), Pearson correlation coefficient (PCC), and index of agreement (IOA), as well as through pictorial interpretation (Taylor diagram, box-whisker plots, time-variation, scatter plot, and radar chart). The comparison of the results of ML paradigms revealed the highest accuracy was achieved by the CANFIS model at all depths with MAE (RMSE) = 0.788, 0.636, 0.806 (1.074, 0.854, 1.041) °C, SI = 0.040, 0.033, 0.040, and COE (PCC)/IOA = 0.986, 0.991, 0.985 (0.994, 0.995, 0.993)/0.996, 0.998, 0.996. Thus, the results highlight the capability of the CANFIS model with Tmean, RH, WS, and SSH inputs for daily ST estimation at different soil depths on the study site.

基于气象数据的机器学习模式对不同深度土壤温度的估计
土壤温度对分析环境条件和气候变化具有重要意义。此外,ST是影响作物生长和种子发芽的重要土壤元素。本研究采用随机森林(RF)、径向基神经网络(RBNN)、多层感知器神经网络(MLPNN)和协同神经模糊推理系统(CANFIS)四种机器学习(ML)范式,对不同土壤深度(即5 cm: ST5;15厘米:ST15;2016-2019年,位于印度旁遮普省西南部的Bathinda气象站的气温为30厘米(ST30)。利用平均气温(Tmean)、相对湿度(RH)、风速(WS)、日照时数(SSH) 4个气象数据,制定了5种不同的组合,并分别对每个土壤深度采用伽玛检验(GT)提出了最优组合。在验证期间,根据平均绝对误差(MAE)、均方根误差(RMSE)、散点指数(SI)、效率系数(COE)、Pearson相关系数(PCC)和一致性指数(IOA)等性能指标以及图像解释(泰勒图、盒须图、时变、散点图和雷达图)对RF、RBNN、MLPNN和CANFIS模型的结果进行评估。结果表明,CANFIS模型在各深度的准确率最高,MAE (RMSE)分别为0.788、0.636、0.806(1.074、0.854、1.041)°C, SI分别为0.040、0.033、0.040,COE (PCC)/IOA分别为0.986、0.991、0.985(0.994、0.995、0.993)/0.996、0.998、0.996。因此,结果突出了CANFIS模型在研究地点不同土壤深度下的Tmean, RH, WS和SSH输入的每日ST估计能力。
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来源期刊
Environmental Monitoring and Assessment
Environmental Monitoring and Assessment 环境科学-环境科学
CiteScore
4.70
自引率
6.70%
发文量
1000
审稿时长
7.3 months
期刊介绍: Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.
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