Hybrid Generalized Regularized Extreme Learning Machine Through Gradient-Based Optimizer Model for Self-Cleansing Nondeposition with Clean Bed Mode of Sediment Transport.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-08-01 Epub Date: 2023-03-07 DOI:10.1089/big.2022.0120
Enes Gul, Mir Jafar Sadegh Safari
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引用次数: 0

Abstract

Sediment transport modeling is an important problem to minimize sedimentation in open channels that could lead to unexpected operation expenses. From an engineering perspective, the development of accurate models based on effective variables involved for flow velocity computation could provide a reliable solution in channel design. Furthermore, validity of sediment transport models is linked to the range of data used for the model development. Existing design models were established on the limited data ranges. Thus, the present study aimed to utilize all experimental data available in the literature, including recently published datasets that covered an extensive range of hydraulic properties. Extreme learning machine (ELM) algorithm and generalized regularized extreme learning machine (GRELM) were implemented for the modeling, and then, particle swarm optimization (PSO) and gradient-based optimizer (GBO) were utilized for the hybridization of ELM and GRELM. GRELM-PSO and GRELM-GBO findings were compared to the standalone ELM, GRELM, and existing regression models to determine their accurate computations. The analysis of the models demonstrated the robustness of the models that incorporate channel parameter. The poor results of some existing regression models seem to be linked to the disregarding of the channel parameter. Statistical analysis of the model outcomes illustrated the outperformance of GRELM-GBO in contrast to the ELM, GRELM, GRELM-PSO, and regression models, although GRELM-GBO performed slightly better when compared to the GRELM-PSO counterpart. It was found that the mean accuracy of GRELM-GBO was 18.5% better when compared to the best regression model. The promising findings of the current study not only may encourage the use of recommended algorithms for channel design in practice but also may further the application of novel ELM-based methods in alternative environmental problems.

基于梯度优化器的混合广义正则化极限学习机模型,用于自清洁非沉积与清洁床模式的沉积物输送。
泥沙输运模型是一个重要问题,可最大限度地减少明渠中的泥沙淤积,从而减少意外的运行费用。从工程角度看,根据流速计算所涉及的有效变量开发精确模型,可为渠道设计提供可靠的解决方案。此外,泥沙输运模型的有效性与模型开发所使用的数据范围有关。现有的设计模型是在有限的数据范围内建立的。因此,本研究旨在利用文献中的所有实验数据,包括最近发表的涵盖广泛水力特性的数据集。在建模过程中采用了极限学习机(ELM)算法和广义正则化极限学习机(GRELM),然后利用粒子群优化(PSO)和基于梯度的优化器(GBO)对 ELM 和 GRELM 进行混合。GRELM-PSO 和 GRELM-GBO 的结果与独立的 ELM、GRELM 和现有回归模型进行了比较,以确定其计算的准确性。对模型的分析表明,包含信道参数的模型具有稳健性。一些现有回归模型的结果不佳,似乎与忽略信道参数有关。对模型结果的统计分析表明,GRELM-GBO 的性能优于 ELM、GRELM、GRELM-PSO 和回归模型,但 GRELM-GBO 的性能略高于 GRELM-PSO。研究发现,与最佳回归模型相比,GRELM-GBO 的平均准确率高出 18.5%。当前研究的良好结果不仅可以鼓励在实践中使用推荐算法进行通道设计,还可以进一步推动基于 ELM 的新型方法在其他环境问题中的应用。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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