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Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models
Geoderma ( IF 6.1 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.geoderma.2020.114793
Ruhollah Taghizadeh-Mehrjardi , Karsten Schmidt , Norair Toomanian , Brandon Heung , Thorsten Behrens , Amirhosein Mosavi , Shahab S. Band , Alireza Amirian-Chakan , Aboalhasan Fathabadi , Thomas Scholten

Abstract The low potential of agricultural productivity in the majority of central Iran is mainly attributed to high levels of soil salinity. To increase agricultural productivity, while preventing any further salinization, and implement effective soil reclamation programs, precise information about the spatial patterns and magnitude of soil salinity is essential. In this study, soil salinity was predicted and mapped using machine learning (ML) and digital soil mapping approaches. Specifically, support vector regression (SVR) was combined with wavelet transformation (W-SVR) of a wide range of environmental covariates derived from a digital elevation model, remote sensing, and climatic data. Predictions of soil salinity were carried out for six standard depth increments (0–5, 5–15, 15–30, 30–60, 60–100, 100–200 cm). Cross-validation was carried out by partitioning the data into 70% used for training the model and 30% for testing the model. Uncertainty of the ML algorithms was quantified using the uncertainty estimation based on local errors and clustering (UNEEC) method. The results indicated that W-SVR performed better in predicting soil salinity for all six depth increments. The differences were most apparent for the lowest soil depth increments where W-SVR resulted in ~1.4 times higher correlation coefficient when compared to the SVR. At lower soil depths increments, covariate importance analysis indicated that topographic derivatives were the most relevant covariates in the models. For topsoil salinity, remote sensing covariates were the most relevant predictors of soil salinity. Regardless of soil depth, climatic predictors were the most important predictors. Uncertainty analysis also indicated that for all depth increments, the estimated prediction interval for SVR obtained by the UNEEC method was wider than that of W-SVR and further indicating the higher performance of W-SVR in comparison to the SVR. The predicted salinity maps showed the highest salinity for soils in the eastern parts of central Iran, which was consistent with the Agro-climatic Zoning of Isfahan Province.

中文翻译:

使用小波变换和支持向量回归模型改进干旱地区土壤盐度的空间预测

摘要 伊朗中部大部分地区农业生产力潜力低下,主要是由于土壤盐分高。为了提高农业生产力,同时防止任何进一步的盐渍化,并实施有效的土壤复垦计划,关于土壤盐分的空间格局和大小的精确信息是必不可少的。在这项研究中,使用机器学习 (ML) 和数字土壤绘图方法预测和绘制了土壤盐度。具体来说,支持向量回归 (SVR) 与来自数字高程模型、遥感和气候数据的各种环境协变量的小波变换 (W-SVR) 相结合。对六个标准深度增量(0-5、5-15、15-30、30-60、60-100、100-200 cm)进行了土壤盐度预测。通过将数据划分为 70% 用于训练模型和 30% 用于测试模型来进行交叉验证。使用基于局部误差和聚类 (UNEEC) 方法的不确定性估计来量化 ML 算法的不确定性。结果表明,W-SVR 在预测所有六个深度增量的土壤盐度方面表现更好。最低土壤深度增量的差异最为明显,与 SVR 相比,W-SVR 导致相关系数高约 1.4 倍。在较低的土壤深度增量下,协变量重要性分析表明,地形导数是模型中最相关的协变量。对于表土盐度,遥感协变量是土壤盐度最相关的预测因子。无论土壤深度如何,气候预测因子是最重要的预测因子。不确定性分析还表明,对于所有深度增量,UNEEC 方法获得的 SVR 估计预测区间比 W-SVR 更宽,进一步表明 W-SVR 与 SVR 相比具有更高的性能。预测的盐度图显示伊朗中部东部土壤的盐度最高,这与伊斯法罕省的农业气候区划一致。
更新日期:2021-02-01
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