Groundwater level prediction using a SOM-aided stepwise cluster inference model

Han, Jing‐Cheng; Huang, Yuefei; Li, Zhong; Zhao, Chunhong; Cheng, Guanhui; Huang, Pengfei

doi:10.1016/j.jenvman.2016.07.069

Cited by 41 publications

(13 citation statements)

References 36 publications

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“…The nutriaents mainly came from domestic sewage, industrial wastewater, agriculture fertilizer, and marine culture in the Pearl River estuary (Huang et al, 2003). Nonpoint source pollution (with nitrate and related compounds) due to agriculture is a major issue affecting groundwater (Han et al, 2016). The focus of controlling water pollution is controlling pollution sources and reducing the discharge of wastewater to streams and shallow aquifers (Han et al, 2016).…”

Section: Nutrientsmentioning

confidence: 99%

“…Nonpoint source pollution (with nitrate and related compounds) due to agriculture is a major issue affecting groundwater (Han et al, 2016). The focus of controlling water pollution is controlling pollution sources and reducing the discharge of wastewater to streams and shallow aquifers (Han et al, 2016). The eutrophication had an influence on transportation and transformation of contaminants in the aquatic environment, which including potential key factors, such as biomass dissolving functions, a staying period in waters, sediment embedding, and structures of food net (Huang et al, 2003).…”

Section: Nutrientsmentioning

confidence: 99%

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Environmental Pollution in Pearl River Delta, China: Status and Potential Effects

Wang¹,

Hao²

2020

J ENVIRON INFORM LET

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Rapid economic growth in the Pearl River Delta (PRD) region, south China, has resulted in severe pollution of the natural eco-environment since the reform and opening up. Of various sources for eco-environmental deterioration, the negative impact of environmental pollutants is a global concern. Large amounts of research on environmental pollution in the PRD have been accumulated, which allows us to conduct a fairly comprehensive assessment of the environment state of the PRD. This review examines environmental pollutions (e.g., heavy metals, organics) mainly in water, air, and soil. The general information of these pollutions on curr ent levels, possible causes, and potential effects in PRD were reviewed. The study found that heavy metals had an increasing tr end in PRD in recent decades, especially for Cr. The sediments in coastal wetlands were significantly contaminated by Cd, Zn, and Ni. The levels o f DDTs in various environmental media are of great concern. The discharge of industrial effluents and domestic sewage seemed to cause nutrient and heavy metal pollution in environmental media. Atmospheric emissions of gaseous and particulate pollutants have caused profound environmental and health implications. Growing public concern over the potential accumulation of heavy metals in soils could be owing to rapid urban and industrial development.

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Section: Nutrientsmentioning

confidence: 99%

Section: Nutrientsmentioning

confidence: 99%

Environmental Pollution in Pearl River Delta, China: Status and Potential Effects

Wang¹,

Hao²

2020

J ENVIRON INFORM LET

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“…Nevertheless, besides its calculation complexity, the performance of the SCA is sensitive to its inputs and internal parameters; the difference within leaf clusters of a SCA tree is usually not well described. The SCA has been applied to various water resources and environmental management problems, such as urban air quality prediction (Huang, 1992), lung cancer diagnosis (Ren et al, 1997), waste treatment process simulation (Sun et al, 2009;Sun et al, 2011), groundwater bioremediation optimization (Huang et al, 2006;Qin et al, 2007;He et al, 2008b;Wang et al, 2012;Zhao et al, 2017), open water forecasting (Fan et al, 2015;Li et al, 2015;Han et al, 2016;Zhuang et al, 2016b;Cheng et al, 2016;Fan et al, 2017;) and climate model downscaling (Wang et al, 2013;Zhuang et al, 2016a;Zhai et al, 2019). However, few applications of SCA to river ice forecasting are reported.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting

Sun¹,

Shi²,

Huang³

et al. 2019

J ENVIRON INFORM LET

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Frequently occurring ice jams often cause concern in northern regions. Breakup timing is directly related to emergency responses preparation and thus its early accurate forecasting is beneficial to ice-related flooding management. The stepwise cluster analysis (SCA) is a non-parameter regression method, which generates a classification tree in the sense of probability through cutting or merging operations according to certain statistic criteria. To enhance SCA's predictive performance, a SCA ensemble (SCAE) method is developed and applied to forecasting of annual river ice breakup dates (BDs). In detail, the SCA is employed as a base model at the lower level while the simple average method is selected as combining models at the upper level. The SCA base models are selected according to different performance selection criteria and searched for further combination. A site on a representative river prone to river ice flooding in Alberta, Canada is selected to demonstrate the effectiveness of the proposed SCAE. The results mainly show that: the SCA base models with multiple combinations of inputs and internal parameters are able to predict the BDs with good performances (the highest average of correlation coefficients for training can be 0.958); the optimal SCA base model has three inputs, which indicates that the temperatures before breakup and just after freeze-up as well as the maximum of water flow in March are relatively important indicators of BD. The optimal SCAE, including base models from different performance selection criteria, has the lowest average of root mean squared error, which improves upon the optimal SCA base model by 25.3%. It indicates the different model selection criteria do improve the diversity and thus further help to improve the performance of ensemble models. This first application of the SCAE to river ice forecasting highlights the possibility of using the ensemble learning paradigm to enhance the SCA. The potential applications of the SCAE to other forecasting problems are expected.

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“…They validated the results using field measurements and found that the equation can predict the water level variations, with good precision. Han et al (2016) implemented a groundwater level modelling framework through the coupling of two spatial and temporal clustering techniques. In addition, their procedure used selforganizing map technique to identify spatially homogeneous clusters of groundwater level piezometers.…”

Section: Introductionmentioning

confidence: 99%

Assessment of groundwater level variations using multivariate statistical methods

et al. 2019

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Fluctuation of groundwater level induces changes in pore-water pressure of soil. However, this variation is not considered for underground constructions. This article explores the application of a statistical method to evaluate the groundwater level variation in geotechnical designs. The methodology included: (i) data collection, (ii) statistic formulation, and (iii) statistic data analysis. We collected information from the technical studies of the project “Metro de Bogotá”, and selected four boreholes spanning 160 m, approximately, where the 1° de Mayo metro station will be built, in the south of the city. We used groundwater level readings reported by different piezometers for 30 days and data variance was assessed using a multivariate statistical method: analysis of repeated measures profiles. Results present a procedure to estimate the groundwater level fluctuation during a short monitoring period. We concluded that the analysis of repeated measures profiles allows estimating the groundwater level variation under a significance level 1-a.

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Groundwater level prediction using a SOM-aided stepwise cluster inference model

Cited by 41 publications

References 36 publications

Environmental Pollution in Pearl River Delta, China: Status and Potential Effects

Environmental Pollution in Pearl River Delta, China: Status and Potential Effects

Ensemble Learning Enhanced Stepwise Cluster Analysis for River Ice Breakup Date Forecasting

Assessment of groundwater level variations using multivariate statistical methods

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