Dynamic Monitoring of Surface Water Bodies and Their Influencing Factors in the Yellow River Basin

Zhao, Zikun; Li, Huanwei; Song, Xiaoyan; Sun, Wenyi

doi:10.3390/rs15215157

Cited by 4 publications

(4 citation statements)

References 67 publications

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“…This cloud-based platform has been widely used for geo-big data applications and planetary-scale monitoring [30,36,37], and its capabilities for big data processing have been demonstrated in studies on crop extraction, global forest cover change, and land change detection [38][39][40]. Similar studies focusing on the detection and monitoring of surface water have also been conducted, demonstrating excellent performance [41][42][43][44][45]. The image filter successfully identified images within the specified time ranges, geological boundaries, and cloud cover conditions.…”

Section: Lake Area Extractionmentioning

confidence: 95%

Dynamic Monitoring and Change Analysis of Lake Area on the Inner-Mongolian Plateau over the Past 22 Years

Xing,

Gan,

Yan

et al. 2024

Remote Sensing

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Lakes are essential components of the terrestrial water cycle. Their size and quantity reflect natural climate change and anthropogenic activities in time. Lakes on the Inner-Mongolian Plateau (IMP) have experienced significant changes in recent decades, but the current situation remains elusive. In this study, we conducted multi-decadal intensive monitoring of lake area and performed comprehensive variation analysis on the IMP. The study involved pre-processing, lake area extraction, post-processing, and lake area analysis procedures using multi-source satellite images. The results reveal the detailed variation in the lake from various aspects. The temporal analysis indicates that the lake area has undergone two distinct periods of decline followed by subsequent increase, and the variation pattern from January to December was diverse. The spatial analysis suggests that the lake area expanded slightly in the western and southern parts, while the area shrank in the other two. Variations in lakes of different sizes exhibited spatial heterogeneity. To some extent, effective actions have led to lake rehabilitation, but it has not yet reached or surpassed the level of 2000. This study provides a substantial data basis and technological guidance for conducting lacustrine surveys. The results can play important roles in fostering further analyses of the water cycle and the carbon cycle.

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Section: Lake Area Extractionmentioning

confidence: 95%

Dynamic Monitoring and Change Analysis of Lake Area on the Inner-Mongolian Plateau over the Past 22 Years

Xing,

Gan,

Yan

et al. 2024

Remote Sensing

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“…The Yellow River Basin, a vital water resource region in China, plays a pivotal role in supporting the livelihoods and economic activities of its populace [1][2][3][4][5]. Recent rapid economic growth and urbanization in China have intensified water pollution in the Yellow River, posing significant risks to the ecological environment and the sustainability of the basin's water resources, thereby impacting resident lives [6,7].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

Wu,

Blancaflor,

Ren

et al. 2024

Int. J. Onl. Eng.

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In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.

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“…The Yellow River, as one of China's mother rivers, is an important region for water resources supply and ecological environment support in our country [1]. However, with the acceleration of industrialization and urbanization processes, the water quality issues in the Yellow River basin are becoming increasingly prominent, bringing serious impacts on the local ecological environment and people's lives [2].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Water Quality Prediction in the Yellow River Basin: Application of the SSA-RBF Model

Wu,

Blancaflor

2024

E3S Web Conf.

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Watershed water quality monitoring is of great significance in water environment protection and management, and this paper proposes a water quality prediction model based on RBF neural network. Aiming at the parameter optimisation of RBF water quality prediction model, we propose to apply the sparrow search algorithm to optimise the parameters of the RBF model, aiming to improve the global search ability and convergence speed of the model. The characteristics of water quality parameters in the Shaanxi section of the Yellow River Basin were analysed using the model. Comparison with the RBF prediction algorithm is made, and the SSA-RBF prediction algorithm can significantly improve the prediction performance of the RBF model.

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Dynamic Monitoring of Surface Water Bodies and Their Influencing Factors in the Yellow River Basin

Cited by 4 publications

References 67 publications

Dynamic Monitoring and Change Analysis of Lake Area on the Inner-Mongolian Plateau over the Past 22 Years

Dynamic Monitoring and Change Analysis of Lake Area on the Inner-Mongolian Plateau over the Past 22 Years

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

Enhanced Water Quality Prediction in the Yellow River Basin: Application of the SSA-RBF Model

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