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首页> 外文期刊>Journal of Hydroinformatics >Geomorphology-based genetic programming approach for rainfall-runoff modeling
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Geomorphology-based genetic programming approach for rainfall-runoff modeling

机译：基于地貌的遗传规划方法用于降雨径流模拟

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摘要

Nowadays, artificial intelligence approaches such as artificial neural network (ANN) as a self-learn non-linear simulator and genetic programming (GP) as a tool for function approximations are widely used for rainfall-runoff modeling. Both approaches are usually created based on temporal characteristics of the process. Hence, the motivation to present a comprehensive model which also employs the watershed geomorphological features as spatial data, in this paper, two different scenarios, separated and integrated geomorphological GP (GGP) modeling based on observed time series and spatially varying geomorphological parameters, were presented for rainfall-runoff modeling of the Eel River watershed. In the first scenario, the model could present a good insight into the watershed hydrologic operation via GGP formulation. In the second scenario, an integrated model was proposed to predict runoff in stations with lack of data or any point within the watershed due to employing the spatially variable geomorphic parameters and rainfall time series of the sub-basins as the inputs. This ability of the integrated model for the spatiotemporal modeling of the process was examined through the cross-validation technique. The results of this research demonstrate the efficiency of the proposed approaches due to taking advantage of geomorphological features of the watershed.
机译：如今，诸如人工神经网络（ANN）作为自学习非线性模拟器和基因编程（GP）作为函数逼近工具之类的人工智能方法已广泛用于降雨径流建模。两种方法通常都是基于过程的时间特性创建的。因此，提出了提出一个综合模型的动机，该模型还利用流域地貌特征作为空间数据，在本文中，提出了两种不同的情况，即基于观测的时间序列和空间变化的地貌参数的分离和集成地貌GP（GGP）建模。用于Eel河流域的降雨径流模拟。在第一种情况下，该模型可以通过GGP公式提供对流域水文操作的深入了解。在第二种情况下，提出了一个综合模型来预测缺乏数据的流域或流域内任何点的径流，这是由于采用了子盆地的空间可变地貌参数和降雨时间序列作为输入。通过交叉验证技术检查了集成模型对过程的时空建模的这种能力。这项研究的结果证明了利用流域的地貌特征所提出的方法的有效性。

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