Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
Implementation of hybrid particle swarm optimization-differential evolution algorithms coupled with multi-layer perceptron for suspended sediment load estimation
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DOI：
10.1016/j.catena.2020.105024
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发表时间：
2021-01-04
期刊：
影响因子：
6.2
通讯作者：
Safari, Mir Jafar Sadegh
中科院分区：
文献类型：
--
作者：
Mohammadi, Babak;Guan, Yiqing;Safari, Mir Jafar Sadegh
河流悬浮泥沙负荷（SSL）估算在水资源工程和水文建模中具有重要意义。在本研究中，推荐了一种新颖的混合方法用于 SSL 估计，其中多层感知器（MLP）与粒子群优化（PSO）混合，然后与差分进化算法（DE）集成，称为 MLP-PSODE。采用混合 MLP-PSODE 模型对位于伊朗西北部的 Mahabad 河的 SSL 进行建模。为了检验MLP-PSODE模型的性能，选择了多层感知器（MLP）、多层感知器与粒子群优化集成（MLP-PSO）、径向基函数（RBF）和支持向量机（SVM）等技术作为基准。为此，建模考虑了五种不同的场景。结果表明，新的MLP-PSODE混合模型通过考虑流量（Q）的单一输入成功地估算了SSL，与替代方案相比，其RMSE = 1794.4 ton.day(-1)，MAPE = 41.50%和RRMSE = 107.09%，远低于基于MLP的模型的RMSE = 3133.7 ton.day(-1)，MAPE。 = 121.40%，RRMSE = 187.03%。所开发的 MLP-PSODE 模型不仅在极值估计的准确性方面优于同类模型，而且它是一种简约模型，在 SSL 估计的结构中包含较少数量的输入参数。
River suspended sediment load (SSL) estimation is of importance in water resources engineering and hydrological modeling. In this study, a novel hybrid approach is recommended for SSL estimation in which multi-layer perceptron (MLP) is hybridized with particle swarm optimization (PSO) and then, integrated with differential evolution algorithm (DE) called as MLP-PSODE. The hybrid MLP-PSODE model is implemented to model the SSL of Mahabad river located at northwest of Iran. For the sake of examination of the MLP-PSODE model performance, several techniques including multi-layer perceptron (MLP), multi-layer perceptron integrated with particle swarm optimization (MLP-PSO), radial basis function (RBF) and support vector machine (SVM) are selected as benchmarks. For this purpose, five different scenarios are considered for the modeling. The results indicated that the new hybrid model of MLP-PSODE is successful in estimating SSL by considering single input of discharge (Q) with high accuracy as compared to its alternatives with RMSE = 1794.4 ton.day(-1), MAPE = 41.50% and RRMSE = 107.09%, which were much lower than those of MLP based model with RMSE = 3133.7 ton.day(-1), MAPE = 121.40% and RRMSE = 187.03%. The developed MLP-PSODE model, not only outperforms its counterparts in terms of accuracy in extreme values estimation, but also it is found as a parsimonious model that incorporates lower number of input parameters in its structure for SSL estimation.