Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Yang, Wenjie; Zhao, Yue; Wang, Dong; Wu, Huihui; Lin, Aoxiang; He, Li

doi:10.3390/ijerph17082942

Cited by 213 publications

(96 citation statements)

References 48 publications

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“…is thesis is based on the analysis and summary of academic research on arid and semiarid mining ecosystems, ecological environment remote sensing monitoring, quantitative ecological evaluation model, ecological restoration of mining areas, and so on [18][19][20][21][22]. It introduces optical remote sensing and radar remote sensing as monitoring means to study the mining ecosystem and evaluate the ecological environment monitoring results of mining areas [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas

et al. 2021

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Along with the accelerated shift of coal mining to the ecologically fragile west, the contradiction between coal resource development and ecological protection in the western arid and semiarid coal mining areas is rapidly intensifying. Based on the above background, this thesis takes the coal mining area in the arid and semiarid regions as an example; applies the theories of ecology, coal mining subsidence, geodesy, and ecological restoration; uses remote sensing in synthetic aperture radar (SAR), geographic information system (GIS), and mathematical modelling to reveal the ecological evolution law of the mining area; measures the ecological damage of the mining area; and then proposes a reasonable ecological restoration strategy. The surface deformation monitoring study in the study area shows that on the whole, some areas in the study area have different degrees of surface subsidence disasters, and the maximum surface subsidence value exceeds 800 mm. From the distribution of surface subsidence in the study area, surface subsidence disasters mainly occur in the eastern and central mountainous areas rich in coal resources, as well as in the mining areas west of the Yellow River, and the subsidence basins are distributed in a series of irregular concentric ovals. In terms of the scale of surface subsidence in the study area, a total of 230.03 km2 of land in the study area showed surface subsidence hazards during the monitoring period, accounting for 13.78% of the total area of the study area, of which the area of severe subsidence was 44.98 km2 (2.69%). The area of more serious subsidence area is 101.33 km2 (6.07%), and the area affected by subsidence is 83.72 km2 (5.01%).

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Section: Introductionmentioning

confidence: 99%

[Retracted] Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas

et al. 2021

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“…With recent advances in computational intelligence, many scholars have replaced traditional methods with new generated machine learning [6][7][8][9][10][11], deep learning [12][13][14][15][16][17], decision making [18,19], and artificial intelligence-based tools [20][21][22]. These novel approximation techniques are well employed in various engineering fields such as in evaluating environmental concerns [19,[23][24][25][26][27][28][29][30][31], implications for natural environmental management [32][33][34][35][36][37][38][39], water resources management [28,[40][41][42][43][44], natural gas consumption [45][46][47][48], energy efficiency [49][50]…”

Section: Background Of Artificial Intelligencementioning

confidence: 99%

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Moayedi

Mosavi

2021

Energies

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A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

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“…It is well established that the cost and use of energy affect human lives every day. In this sense, many issues arise from the content of energy consumption such as acid rain, dependency on depleting supplies of fossil fuels, greenhouse gas emissions [8][9][10][11][12][13][14][15][16][17], climate change [18][19][20][21], as well as environmental concerns that come along with energy power supply [22][23][24][25][26][27][28][29][30][31][32][33][34][35]. In recent years, various techniques have been used for the optimal design of the HVAC system [36][37][38][39][40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

Moayedi¹,

Mosavi²

2021

Preprint

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Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings' energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this work is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS-ANN). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA), are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model's optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90 % correlation) can adequately optimize the ANN. In this regard, this tool's prediction error declined by nearly 23, 18, and 36 % by applying the GOA, FA, and SFS techniques. Also, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.

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Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin’anjiang River in Huangshan, China

Cited by 213 publications

References 48 publications

[Retracted] Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas

[Retracted] Mine Geological Environment Monitoring and Risk Assessment in Arid and Semiarid Areas

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

Suggesting a Stochastic Fractal Search Paradigm in Combination With Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

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