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An ANN-based approach to modelling sediment yield: a case study in a semi-arid area of Brazil

机译：基于人工神经网络的沉积物产量模拟方法：以巴西半干旱地区为例

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摘要

This paper describes an Artificial Neural Network (ANN) model for estimating sediment yield based on runoff and climatological data. The model has been applied to an erosion plot inside the Sao Joao do Cariri experimental basin, which is located in the semi-arid portion of Paraiba State, Brazil. Large quantities of sediment tend to be generated only periodically in semi-arid regions, thus accurate estimations of when sediment yields are likely to be high are needed to improve erosion management in such areas. A total of 61 rainfall events, which occurred between 1999 and 2002, were utilized to calibrate and test the model. Another model, based on multiple linear regression (MLR) was used for comparison. The results produced by the ANN model appear to be superior to those generated by the MLR model. The results also indicate that the ANN model is suitable for identifying and extracting nonlinear trends for significant variables.
机译：本文介绍了一种基于径流和气候数据估算沉积物产量的人工神经网络（ANN）模型。该模型已应用于位于巴西帕拉伊巴州半干旱地区的圣若昂杜卡里里实验盆地内部的侵蚀区。大量的沉积物往往仅在半干旱地区周期性地产生，因此需要准确估算何时可能会有较高的沉积物产量，以改善此类地区的侵蚀管理。在1999年至2002年之间发生的总共61次降雨事件被用于校准和测试该模型。使用基于多元线性回归（MLR）的另一个模型进行比较。 ANN模型产生的结果似乎优于MLR模型产生的结果。结果还表明，ANN模型适用于识别和提取重要变量的非线性趋势。

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