A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model

Huijun, Duan; Hao, Shijun; Feng, Juan; Wang, Yi; Peng, Dong

doi:10.1515/geo-2020-0277

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…To e ectively guarantee the safe production of coal mines and reduce the occurrence of mine water disasters, it is an important prerequisite for the prevention and control of water disasters to accurately identify the types of mine water inrush sources [5,6]. At present, there are many methods used to distinguish mine water inrush sources, such as groundwater level dynamics [7], hydrochemical analysis [8], GIS theoretical analysis [9], and so on [10]. Among them, the hydrochemical analysis method is one of the most widely used method [11], but the hydrochemical method needs to obtain the hydrochemical parameters of the mine water source (such as pH value, ion concentration, and conductivity) in the harsh experimental environment.…”

Section: Introductionmentioning

confidence: 99%

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

Zhou

Dai

2022

Scientific Programming

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In the process of mine water inrush disaster prevention, accurate and rapid identification of water inrush source type is of great significance to coal mine safety production. However, traditional hydrochemical methods have shortcomings such as time-consuming and complex detection. Therefore, a new idea of identifying mine water inrush source by Raman spectroscopy is proposed. Goaf water, roof sandstone fissure water, Ordovician limestone water, Taiyuan limestone water, and surface water as well as their mixed water samples are selected as research objects, and Raman spectral data of different water samples are collected by the Raman spectroscopy system. To eliminate the influence of laser power fluctuation and spectrometer system noise in Raman spectrum acquisition, detrend correction (DC), multiplicative scatter correction (MSC), standard normal variate transformation (SNV), first derivative (FD), and mean centering (MC) were used to preprocess the raw Raman spectra. Due to the large dimension and long analysis time of Raman spectrum data, the marine predator algorithm (MPA) is used to screen the characteristic Raman shifts of the Raman spectrum of water samples, and the characteristic Raman shift information that can best characterize the mine water samples is obtained. Finally, to verify the feasibility of MPA screening the characteristic Raman shifts of Raman spectrum of mine water inrush source, the selected characteristic Raman displacement information is used as input to construct BP neural network (BP), k-nearest neighbor algorithm (KNN), support vector machine (SVM), and decision tree (DT) classification models, respectively. Experiments show that SNV has the best preprocessing effect on the raw Raman spectrum, which can effectively eliminate part of the noise in the Raman spectrum data and improve the accuracy of Raman spectrum identification. Using MPA, 226 characteristic Raman shifts can be screened from 2048 Raman data points, reducing the number of Raman shifts to 11.04%, and the modeling accuracy of characteristic Raman shift information screened by MPA is higher than that of full Raman data. In addition, the average analysis speed of BP, KNN, SVM, and DT water source identification models is 7.61 times faster than that of all Raman data. The results show that MPA is adopted to screen the characteristic Raman displacement of mine water source Raman spectrum, which can effectively reduce the redundancy of Raman spectral data and greatly improve the speed of Raman spectral analysis, which is of great significance to ensure the real-time detection of the mine water source.

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Section: Introductionmentioning

confidence: 99%

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

Zhou

Dai

2022

Scientific Programming

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Risk identification of coal spontaneous combustion in goaf based on variable weight grey target model

Yang

Zhang

et al. 2022

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Integrating Social, Economic, and Environmental Factors to Evaluate How Competitive Urban Landscapes Are for the Development of Sustainable Cities: Penang Island in Malaysia as a Case Study

Bagheri

Lonik

Jaafar

et al. 2022

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The competitiveness of urban landscapes is a subject of growing attention as cities strive to become the best places to support enterprises, economic development, and high-quality living while preserving environmental resources. In evaluations of urban competitiveness, financial factors are frequently emphasized. In this systematic study for the future sustainable urban landscape (SUL), we use the instance of Penang Island, Malaysia, to present an integrated methodology for finding, evaluating, and validating good indicators for developing an urban landscape index that balances economic, sociocultural, and environmental factors. First, we provide a framework for developing such an index by developing a Delphi Analytical Hierarchy Process (DAHP) model that aids in choosing the most important aspects and sub criteria affecting the sustainability of urban landscapes. Then, under the Sustainable Development Goals (SDG), we demonstrate how to use these data to assess the competitiveness of various zones on Penang Island, namely Bayan Lepas and George Town. According to the quantitative results of the DAHP model, economic criteria, which were given a weight of 0.595 and a corresponding inconsistency (CI) of 0.04, had a greater influence on Penang Island’s urban landscape than environmental criteria, which were given a weight of 0.276 and a CI of 0.02, and social and cultural criteria, which were given a weight of 0.128 and a CI of 0.08. In terms of economic criteria, Zone 1 has a weight of 0.360, which indicates that it has less sway than Zone 2, which has a weight of 0.636. In the discussion, we highlight the essential elements of the methodology to assess the competitiveness of urban environments.

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A prediction method for water enrichment in aquifer based on GIS and coupled AHP–entropy model

Cited by 3 publications

References 22 publications

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

Study on Characteristic Raman Shift Screening Method Based on MPA for Raman Spectrum of Mine Water Inrush Source

Risk identification of coal spontaneous combustion in goaf based on variable weight grey target model

Integrating Social, Economic, and Environmental Factors to Evaluate How Competitive Urban Landscapes Are for the Development of Sustainable Cities: Penang Island in Malaysia as a Case Study

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