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Predicting Soil Particulate Organic Mattter Using Artificial Neural Network with Wavelet Function
Communications in Soil Science and Plant Analysis ( IF 1.8 ) Pub Date : 2020-08-05 , DOI: 10.1080/00103624.2020.1808012
Paria Baligh 1 , Naser Honarjoo 1 , Arash Totonchi 2 , Ahmad Jalalian 1
Affiliation  

ABSTRACT Particulate organic matter is a part of the labile, easily decomposable, pool of soil organic matter; therefore its study in soil is very important. Estimating particulate organic matter through some related variables at an acceptable level of accuracy is very importance. In this study three model were utilized and compared for predicting particulate organic matter. (i) wavelet function of artificial neural network, (ii) perceptron function of artificial neural network and III) multiple linear regression. The highest R2 value and lowest NRMSE in the regression models were 0.94 and 0.071, respectively, in the wavelet function of artificial neural network were 0.97 and 2.10 E-05, respectively. These values in perceptron function of artificial neural network were intermediate. The results indicated that although wavelet function of artificial neural network was the most reliable model for predicting POM, but multiple linear regression was also an appropriate predictor due to its acceptable R2, especially when the logarithmic forms of the data were used in the models. The results revealed that the best inputs for modelings are soil physicochemical properties only. Wavelet function of the ANN was utilized in the POM prediction for the first time.

中文翻译:

使用具有小波函数的人工神经网络预测土壤颗粒有机物

摘要 颗粒有机质是不稳定、易分解的土壤有机质池的一部分;因此它在土壤中的研究非常重要。通过一些相关变量以可接受的准确度估算颗粒有机物非常重要。在这项研究中,三个模型被用来预测颗粒有机物并进行比较。(i) 人工神经网络的小波函数,(ii) 人工神经网络的感知器函数和 III) 多元线性回归。回归模型中最高R2值和最低NRMSE分别为0.94和0.071,人工神经网络小波函数分别为0.97和2.10 E-05。人工神经网络的感知器函数中的这些值是中间的。结果表明,虽然人工神经网络的小波函数是预测POM最可靠的模型,但多元线性回归由于其可接受的R2也是一个合适的预测器,特别是当模型中使用数据的对数形式时。结果表明,建模的最佳输入仅是土壤物理化学特性。人工神经网络的小波函数首次用于 POM 预测。
更新日期:2020-08-05
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