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DETERMINATION AND ANALYSIS OF INTERRILL EROSION OF A SOIL WITH COARSE FRAGMENTS IN TAIWAN

机译:台湾地区粗颗粒土壤的层间侵蚀测定与分析

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摘要

To evaluate the effects of coarse fragments in soil on interrill soil erosion, a programmable rainfall simulator and soil erosion boxes were designed and fabricated. Simulated rainfall erosion tests were conducted on soils with coarse fragment contents of 0%, 7.5%, 15%, 30%, and 45%; at slope steepnesses of 9%, 20%, and 30%; and under simulated rainfall intensities of 40, 60, 80, and 100 mm h{sup}(-1). A total of 132 data sets were obtained. It was found from the test results that coarse fragments had a mitigating effect on soil erosion at high rainfall intensities, and the steeper the slope, the better the effect. However, there was no apparent positive correlation between such mitigating effect and the percentage of coarse fragments in test samples. When part of the coarse fragments were exposed at the soil surface, both positive and negative effects might result, and a quantitative evaluation thereof requires further study. Furthermore, regression analysis and an artificial neural network were used in this study to establish a general equation and a three-layer back-propagation neural network model for estimating interrill soil erosion. While the regression equation is in harmony with the conceptual mechanisms of soil erosion, with an R{sup}2 value of 0.900 and an RMSE of 0.429, the artificial neural network model has a higher R{sup}2 value of 0.962 and a lower RMSE of 0.342, indicating that the artificial neural network model may provide better estimation of interrill soil erosion.
机译:为了评估土壤中的粗碎物对小孔间土壤侵蚀的影响,设计并制造了可编程降雨模拟器和土壤侵蚀箱。在粗粒含量分别为0%,7.5%,15%,30%和45%的土壤上进行了模拟降雨侵蚀试验;坡度分别为9%,20%和30%时;在40、60、80和100 mm h {sup}(-1)的模拟降雨强度下。总共获得了132个数据集。从测试结果发现,在高降雨强度下,粗碎屑对土壤侵蚀具有缓解作用,并且坡度越陡,效果越好。但是,这种缓解效果与测试样品中粗碎片的百分比之间没有明显的正相关。当部分粗颗粒暴露在土壤表面时,可能会产生正反作用,因此对其定量评估尚需进一步研究。此外,在本研究中,使用回归分析和人工神经网络建立了一个通用方程和一个三层反向传播神经网络模型,用于估算钻头间土壤侵蚀。虽然回归方程与土壤侵蚀的概念机制相符,R {sup} 2值为0.900,RMSE为0.429,但人工神经网络模型的R {sup} 2值较高,为0.962,而较低。 RMSE为0.342,这表明人工神经网络模型可以更好地估计钻间土壤侵蚀。

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