Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive networkbased fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009.
水文和統(tǒng)計模型在水文預(yù)報中發(fā)揮著越來越重要的作用，特別是對于具有不同時間尺度數(shù)據(jù)的流域。 在這項研究中，統(tǒng)計模型，例如 基于粒子群優(yōu)化（PSO），遺傳算法（GA）和數(shù)據(jù)預(yù)處理等參數(shù)優(yōu)化方法，開發(fā)了人工神經(jīng)網(wǎng)絡(luò)，基于自適應(yīng)網(wǎng)絡(luò)的模糊推理系統(tǒng)，遺傳程序，最小二乘支持向量機，多元線性回歸 利用小波分解（WD）等技術(shù)，利用1954-2009年期間來自四個水文站的每日流量數(shù)據(jù)進行河流流量模擬。