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Soil swelling potential across Colorado: A digital soil mapping assessment

机译:科罗拉多州的土壤膨胀潜力:数字土壤映射评估

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Swelling soils contain high amounts of expansive clay minerals which swell upon wetting and shrink upon drying. Urban growth across swelling soils causes billions of dollars in infrastructure damage annually throughout the United States. Here, continuous spatial information of soil swelling properties is provided across Colorado, a state experiencing extensive urban growth across swelling soils. The objectives were: 1) model the spatial variability and associated uncertainty of soil swelling-related properties across Colorado; 2) generate a continuous statewide map of soil swelling potential and associated uncertainty at 1x1km resolution; and 3) identify urban areas prone to experience swelling conditions. A digital soil mapping (DSM) framework for extracting pedologically-relevant information from legacy maps was used to train machine learning models to analyze multiple sources of information to represent the soil forming environment (e.g., soils, climate, organisms, topography). Best predictions were based on regression trees and explain over 80% of soil swelling spatial variability across the state (10-fold cross validation strategy). Over 20% of urbanized areas were identified as being prone to experience swelling conditions. Special considerations must be undertaken to couple urban development with soil functionality in order to prevent future damages and economic losses.
机译:膨胀土壤含有大量的膨胀粘土矿物,在润湿并在干燥时萎缩。膨胀土壤的城市成长导致整个美国每年的基础设施损坏数十亿美元。在这里,在科罗拉多州提供土壤膨胀性质的连续空间信息,这是一种在膨胀土壤中经历广泛的城市生长的状态。目的是:1)模拟跨科罗拉多州土壤肿胀相关性能的空间变异性和相关的不确定性。 2)在1x1km分辨率下产生土壤膨胀潜力的连续立式型材和相关的不确定性; 3)确定城市地区容易发生肿胀条件。用于从传统地图中提取专科相关信息的数字土壤映射(DSM)框架用于培训机器学习模型,以分析多种信息来源,以代表土壤形成环境(例如,土壤,气候,生物,地形)。最佳预测基于回归树木,解释了跨越状态的80%的土壤膨胀空间变异(10倍交叉验证策略)。超过20%的城市化地区被确定为易患肿胀条件。必须采取特殊考虑,以利用土壤功能耦合城市发展,以防止未来的损失和经济损失。

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