Trends and Changes in Hydrologic Cycle in the Huanghuaihai River Basin from 1956 to 2018

Yang, Xiaotian; Bao, Zhenxin; Wang, Guoqing; Liu, Cuishan; Jin, Junliang

doi:10.3390/w14142148

Cited by 3 publications

(1 citation statement)

References 46 publications

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“…An ELM, a single-hidden-layer feedforward neural network (SLFN), is employed to expedite the training process [29]. This training methodology is notably superior to traditional SLFN algorithms, with ELM selecting random weights for input layers, hiddenlayer bias, and output-layer weight, determined through minimization of a loss function-a sum of the training error term and a regular term reflecting the output-layer weight norm [30].…”

Section: Prediction Modelmentioning

confidence: 99%

An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

Wu,

Wen,

Zhu

2023

Atmosphere

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Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model’s results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.

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Section: Prediction Modelmentioning

confidence: 99%

An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

Wu,

Wen,

Zhu

2023

Atmosphere

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show abstract

Biochar application does not improve crop growth and yield in a semi-humid region in the HuangHuaiHai Plain of China: A 7-year consecutive field experiment

Cheng,

Ma,

Zhao

et al. 2025

Soil and Tillage Research

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A Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

Wu,

Wen,

Zhu

2023

Preprint

View full text Add to dashboard Cite

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns is crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5 related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components - feature selection, clustering, and integrated prediction, the model first employs the non-dominated sorting Genetic Algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the Adaboost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants.

show abstract

Trends and Changes in Hydrologic Cycle in the Huanghuaihai River Basin from 1956 to 2018

Cited by 3 publications

References 46 publications

An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

An Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

Biochar application does not improve crop growth and yield in a semi-humid region in the HuangHuaiHai Plain of China: A 7-year consecutive field experiment

A Ensemble Model for PM2.5 Concentration Prediction Based on Feature Selection and Two-Layer Clustering Algorithm

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