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Application of computational intelligence methods in modelling river flow prediction: A review

机译：计算智能方法在河流流量预测建模中的应用

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Rainfall and river flow are one of the most difficult elements of hydrological cycle to predict. This is due to tremendous range of variability it displays over a wide range of scale both in terms of space and time. The situation is further aggravated by the fact that rainfall-runoff is also very difficult to measure at scales of interest to hydrology and climatologic. Computational intelligence techniques provide efficient and fast results for modelling non-linear and complex data. Computational intelligence methods which inspired by the capability of learning that derive meaning from unknown relationship provide guidance for a sensible decision making. This advantage creates them adaptable and talented methods for modelling real world problems. This paper is an attempt to present the introduction to computational intelligence methods; applications to river flow modelling and its performance with regards to the parameter and method used. The methods include artificial neural networks, fuzzy logic, evolutionary computation, support vector machine; swarm intelligence and hybrid method are critically compared mainly on computational results and prediction accuracy.
机译：降雨和河流流量是预测水文周期最困难的要素之一。这是由于它在空间和时间方面都显示了很大范围的可变性。降雨径流在水文和气候学感兴趣的尺度上也很难测量，这一事实使情况进一步恶化。计算智能技术为建模非线性和复杂数据提供了高效而快速的结果。受学习能力启发的计算智能方法可以从未知的关系中获取含义，从而为明智的决策提供指导。这种优势使他们可以采用灵活而有才华的方法对现实世界中的问题建模。本文试图介绍计算智能方法。参数和方法在河流流量建模中的应用及其性能。这些方法包括人工神经网络，模糊逻辑，进化计算，支持向量机;以及群体智能和混合方法主要在计算结果和预测准确性方面进行了严格的比较。

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