scholarly journals Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment

2020 ◽  
Vol 12 (22) ◽  
pp. 9338
Author(s):  
Anna Kopeć ◽  
Paweł Trybała ◽  
Dariusz Głąbicki ◽  
Anna Buczyńska ◽  
Karolina Owczarz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Coal Mining ◽  
Geographically Weighted Regression ◽  
Machine Learning Algorithms ◽  
Weighted Regression ◽  
Hard Coal ◽  
Mining Operations ◽  
Hard Coal Mining ◽  
The Impact

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.

scholarly journals Spatial Prediction of COVID-19 in China Based on Machine Learning Algorithms and Geographically Weighted Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qi Shao ◽  
Yongming Xu ◽  
Hanyi Wu
Keyword(s):  
Machine Learning ◽  
Geographically Weighted Regression ◽  
Learning Algorithms ◽  
Mainland China ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Weighted Regression ◽  
Least Squares Regression ◽  
Independent Variables ◽  
Number Of Patients

COVID-19 has swept through the world since December 2019 and caused a large number of patients and deaths. Spatial prediction on the spread of the epidemic is greatly important for disease control and management. In this study, we predicted the cumulative confirmed cases (CCCs) from Jan 17 to Mar 1, 2020, in mainland China at the city level, using machine learning algorithms, geographically weighted regression (GWR), and partial least squares regression (PLSR) based on population flow, geolocation, meteorological, and socioeconomic variables. The validation results showed that machine learning algorithms and GWR achieved good performances. These models could not effectively predict CCCs in Wuhan, the first city that reported COVID-19 cases in China, but performed well in other cities. Random Forest (RF) outperformed other methods with a CV ‐ R 2 of 0.84. In this model, the population flow from Wuhan to other cities (WP) was the most important feature and the other features also made considerable contributions to the prediction accuracy. Compared with RF, GWR showed a slightly worse performance ( CV ‐ R 2 = 0.81 ) but required fewer spatial independent variables. This study explored the spatial prediction of the epidemic based on multisource spatial independent variables, providing references for the estimation of CCCs in the regions lacking accurate and timely.

scholarly journals Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

2021 ◽  
Author(s):  
Kamil Faisal
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
City Planning ◽  
Urban Morphology ◽  
New Method ◽  
Economic Indicators ◽  
Weighted Regression ◽  
The Impact ◽  
The Relationship

Population growth around the world may cause an adverse impact on the environment and the human life. Thus, modeling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. To evaluate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the UEQ in two major cities in Ontario, Canada. The main objectives of this research are: 1) to examine the relationship of multiple UEQ parameters derived from remote sensing, GIS and socio-economic data; 2) to evaluate some of the existing methods (e.g. linear regression, GIS overlay and Principal Component Analysis (PCA)) for assessing and integrating multiple UEQ parameters; 3) to propose a new method to weight urban and environmental parameters obtained from different data sources; 4) to develop a new method to validate the UEQ results with respect to three socio-economic indicators. Remote sensing, GIS and census data were first obtained to calculate various environmental, urban parameters and socio-economic indicators. The derived parameters and indicators were tested to emphasize their relationship to UEQ. Three geographically-Weighted Regression (GWR) techniques were used to integrate all these environmental, urban parameters and socio-economic indicators. Three key indicators including family income, the level of education and land value were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the three indicators. The findings showed that the GWR with spatial lag model represents an improved precision and accuracy up to 20% with respect to GIS overlay and PCA techniques. The final outcomes of the research can help the authorities and decision makers to understand the empirical relationships among regional science, urban morphology, real estate economics and economic geography.

scholarly journals The Impact of Variability of Selected Geological and Mining Parameters on the Value and Risks of Projects in the Hard Coal Mining Industry

2017 ◽  
Vol 62 (3) ◽  
pp. 545-564
Author(s):  
Michał Kopacz
Keyword(s):  
Coal Mining ◽  
Probabilistic Approach ◽  
Mining Industry ◽  
Hard Coal ◽  
Nonparametric Bootstrap ◽  
Coal Mining Industry ◽  
Empirical Copula ◽  
Percentage Points ◽  
Hard Coal Mining ◽  
The Impact

AbstractThe paper attempts to assess the impact of variability of selected geological (deposit) parameters on the value and risks of projects in the hard coal mining industry. The study was based on simulated discounted cash flow analysis, while the results were verified for three existing bituminous coal seams.The Monte Carlo simulation was based on nonparametric bootstrap method, while correlations between individual deposit parameters were replicated with use of an empirical copula. The calculations take into account the uncertainty towards the parameters of empirical distributions of the deposit variables. The Net Present Value (NPV) and the Internal Rate of Return (IRR) were selected as the main measures of value and risk, respectively.The impact of volatility and correlation of deposit parameters were analyzed in two aspects, by identifying the overall effect of the correlated variability of the parameters and the indywidual impact of the correlation on the NPV and IRR. For this purpose, a differential approach, allowing determining the value of the possible errors in calculation of these measures in numerical terms, has been used.Based on the study it can be concluded that the mean value of the overall effect of the variability does not exceed 11.8% of NPV and 2.4 percentage points of IRR. Neglecting the correlations results in overestimating the NPV and the IRR by up to 4.4%, and 0.4 percentage point respectively. It should be noted, however, that the differences in NPV and IRR values can vary significantly, while their interpretation depends on the likelihood of implementation.Generalizing the obtained results, based on the average values, the maximum value of the risk premium in the given calculation conditions of the „X“ deposit, and the correspondingly large datasets (greater than 2500), should not be higher than 2.4 percentage points. The impact of the analyzed geological parameters on the NPV and IRR depends primarily on their co-existence, which can be measured by the strength of correlation. In the analyzed case, the correlations result in limiting the range of variation of the geological parameters and economics results (the empirical copula reduces the NPV and IRR in probabilistic approach). However, this is due to the adjustment of the calculation under conditions similar to those prevailing in the deposit.

scholarly journals Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

2021 ◽  
Author(s):  
Kamil Faisal
Keyword(s):  
Remote Sensing ◽  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
City Planning ◽  
Urban Morphology ◽  
New Method ◽  
Economic Indicators ◽  
Weighted Regression ◽  
The Impact ◽  
The Relationship

Population growth around the world may cause an adverse impact on the environment and the human life. Thus, modeling the Urban Environmental Quality (UEQ) becomes indispensable for a better city planning and an efficient urban sprawl control. To evaluate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the UEQ in two major cities in Ontario, Canada. The main objectives of this research are: 1) to examine the relationship of multiple UEQ parameters derived from remote sensing, GIS and socio-economic data; 2) to evaluate some of the existing methods (e.g. linear regression, GIS overlay and Principal Component Analysis (PCA)) for assessing and integrating multiple UEQ parameters; 3) to propose a new method to weight urban and environmental parameters obtained from different data sources; 4) to develop a new method to validate the UEQ results with respect to three socio-economic indicators. Remote sensing, GIS and census data were first obtained to calculate various environmental, urban parameters and socio-economic indicators. The derived parameters and indicators were tested to emphasize their relationship to UEQ. Three geographically-Weighted Regression (GWR) techniques were used to integrate all these environmental, urban parameters and socio-economic indicators. Three key indicators including family income, the level of education and land value were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the three indicators. The findings showed that the GWR with spatial lag model represents an improved precision and accuracy up to 20% with respect to GIS overlay and PCA techniques. The final outcomes of the research can help the authorities and decision makers to understand the empirical relationships among regional science, urban morphology, real estate economics and economic geography.

scholarly journals Comparison of Climate Reanalysis and Remote-Sensing Data for Predicting Olive Phenology through Machine-Learning Methods

2021 ◽  
Vol 13 (6) ◽  
pp. 1224
Author(s):  
Izar Azpiroz ◽  
Noelia Oses ◽  
Marco Quartulli ◽  
Igor G. Olaizola ◽  
Diego Guidotti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Prediction Model ◽  
Olive Tree ◽  
Temperature Data ◽  
Machine Learning Algorithms ◽  
Environmental Data ◽  
Climate Data ◽  
Tree Phenology ◽  
The Impact

Machine-learning algorithms used for modelling olive-tree phenology generally and largely rely on temperature data. In this study, we developed a prediction model on the basis of climate data and geophysical information. Remote measurements of weather conditions, terrain slope, and surface spectral reflectance were considered for this purpose. The accuracy of the temperature data worsened when replacing weather-station measurements with remote-sensing records, though the addition of more complete environmental data resulted in an efficient prediction model of olive-tree phenology. Filtering and embedded feature-selection techniques were employed to analyze the impact of variables on olive-tree phenology prediction, facilitating the inclusion of measurable information in decision support frameworks for the sustainable management of olive-tree systems.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Chances and Threats of Hard Coal Mining Development in Poland – The Results of Experts Research/Szanse I Zagrożenia Dla Rozwoju Górnictwa Węgla Kamiennego W Polsce – Wyniki Badań Eksperckich

2014 ◽  
Vol 59 (2) ◽  
pp. 395-411 ◽  
Author(s):  
Józef Dubiński ◽  
Marian Turek
Keyword(s):  
Coal Mining ◽  
Present Situation ◽  
Hard Coal ◽  
Coal Production ◽  
Actual Situation ◽  
Mining Sector ◽  
Energy Carriers ◽  
Hard Coal Mining ◽  
Adverse Influence ◽  
Other World

Abstract The actual situation of hard coal mining in Poland has been presented. In particular, these factors, which have impact on the competiveness of mining sector were highlighted and need of its improving has been stressed. Outlining present situation of hard coal mining an attention was paid to its specific threats. The primary analytical material is based on the results of questionnaire conducted among 92 specialists and experts from the mining sector. The questions were related to chances and threats for development of hard coal mining in Poland. The factors determining them were grouped in such domains as economy, technology, geology, social and law aspects. Moreover, the special attention was paid to the problem of increasing and high costs of coal production which constitute significant threat for the financial and economic situation of the mining enterprises. Also the adverse influence of these high cost on the competitiveness of Polish hard coal with other world producers and with other energy carriers was emphasized. The conclusions summarize the achieved results of analysis.

Radiological evaluation of mine water discharges from hard coal mining in the Fossa Eugeniana Area, Germany

Kerntechnik ◽  
2008 ◽  
Vol 73 (3) ◽  
pp. 101-107
Author(s):  
C. Wanke ◽  
S. Ritzel ◽  
R. Sachse ◽  
R. Michel
Keyword(s):  
Coal Mining ◽  
Mine Water ◽  
Hard Coal ◽  
Radiological Evaluation ◽  
Hard Coal Mining
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