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High Temporal Resolution Rainfall Information Retrieval from Tipping-Bucket Rain Gauge Measurements:12th International Conference on Hydroinformatics (HIC 2016) - Smart Water for the Future

机译:从小费桶雨量计测量中获得的高时间分辨率降雨信息:第十二届国际水信息学大会(HIC 2016)-面向未来的智能水

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摘要

Disdrometer can play a vital role in restoring detailed rainfall process by providing rainfall at a high temporal resolution. Rainfall rate derived from the widely used “Tipping-Bucket rain gauge” usually neglects its temporal variation especially during the low rainfall intensity periods. This study explores a heuristic artificial neural networks (ANN) approach along with the conventional Cubic Spline Algorithm (CSA) and Multivariate Linear Regression method (MLR) for high temporal resolution rainfall rate retrieval for the period of 2007 to 2009 at Chilbolton, U.K. The Supervised Levenberg-Marquardt backpropagation algorithm and the K-folds cross-validation method are integrated in a feed-forward neural network as to implicitly detect complex nonlinear relationships and to avoid model overfitting. Results indicate ANN is performing equivalently well with CSA after training, however, with poor generalisation in test due to low correlation between input and target data, as well as the curse of dimensionality in optimum model complexity selection. MLR can be an alternative approach in rainfall rate estimation but it highly depends on the data quality.
机译:通过提供高时间分辨率的降雨,Disdrometer可以在恢复详细的降雨过程中发挥至关重要的作用。普遍使用的“小桶雨量计”得出的降雨率通常会忽略其时间变化,尤其是在低降雨强度时期。这项研究探索了启发式人工神经网络(ANN)方法以及常规的三次样条算法(CSA)和多元线性回归方法(MLR),用于在2007年至2009年期间在英国奇尔伯顿进行高分辨率的降雨率检索。 Levenberg-Marquardt反向传播算法和K折交叉验证方法集成在前馈神经网络中,以隐式检测复杂的非线性关系并避免模型过度拟合。结果表明,训练后的人工神经网络与CSA的性能相当,但是,由于输入数据和目标数据之间的相关性较低,以及在优化模型复杂度选择中的维度诅咒,测试的泛化性较差。 MLR可以作为降雨率估算的替代方法,但它在很大程度上取决于数据质量。

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