Development of PLS–path model for understanding the role of precursors on ground level ozone concentration in Gulfport, Mississippi, USA

AbstractGround-level ozone (GLO) is produced by a complex chain of atmospheric chemical reactions that depend on precursor emissions from natural and anthropogenic sources. GLO concentration in a particular location is also governed by local weather and climatic factors. In this work an attempt was made to explore a Partial Least Squares Path Modeling (PLS–PM) approach to quantify the interrelationship between local conditions (weather parameters and primary air pollution) and GLO concentrations. PLS path modeling algorithm was introduced and applied to GLO concentration analyses at Gulfport, Mississippi, USA. In the present analysis, three latent variables were selected: PRC (photochemical reaction catalyst), MP (meteorological factor), and OPP (other primary air pollutants). The three latent variables included 14 indicators for the analysis; with PRC having two (extraterrestrial radiation on horizontal surface, and extraterrestrial radiation normal to the sun), MP having nine (temperature, dew point, relative humidity, pressure, visibility, maximum wind speed, average wind speed, precipitation, and wind direction) and OPP having three (NO2, PM2.5, and SO2) parameters. The resulting model revealed that PRC had significant direct impact on GLO concentration but very small overall effect. This is because PRC had significant indirect negative impact on GLO via MP. Thus, when both direct and indirect effects were taken into account, PRC emerged as having the weakest effect on GLO. The third variable (OPP) also had a positive impact on GLO concentration

Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R-squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization

Application of transfer learning of deep CNN model for classification of time-series satellite images to assess the long-term impacts of coal mining activities on land-use patterns

The study aims to analyse the long-term impacts of mining activities in Jharia coalfield (JCF) on land-use (LU) patterns using transfer learning of the deep convolutional neural network (Deep CNN) model. A new database was prepared by extracting 10,000 image samples of 6 × 6 size for five LU types (barren land, built-up area, coal mining region, vegetation and waterbody) from Landsat data to train and validate the model. The satellite data from 1987 to 2021 at an interval of two years was used for change analysis. The study results revealed that the model offers 95 and 88% accuracy on the training and the validation dataset. The results indicate that barren land, coal mining region, and waterbody have been decreased from 237.30 sq. km. (=39.88%) to 171.25 sq. km (=28.78%), 118.77 sq. km. (=19.96%) to 68.73 sq. km (=11.55%), and 35.58 sq. km (=5.98%) to 18.68 sq. km (=3.14%) during 1987–2021, respectively. On the other hand, the built-up area and vegetation have been increased from 120.14 sq. km (=20.19%) to 233.02 sq. km (=39.16%) and 83.19 sq. km (=13.98%) to 103.36 sq. km (=17.37%) during 1987–2021. The time-series correlation results indicate that coal mining is the most sensitive LU type from 1987 to 2021, whereas barren land is least sensitive up to 2011, and thereafter vegetation is the least sensitive

Optimization Techniques and their Applications to Mine Systems

This book describes the fundamental and theoretical concepts of optimization algorithms in a systematic manner, along with their potential applications and implementation strategies in mining engineering. It explains basics of systems engineering, linear programming, and integer linear programming, transportation and assignment algorithms, network analysis, dynamic programming, queuing theory and their applications to mine systems. Reliability analysis of mine systems, inventory management in mines, and applications of non-linear optimization in mines are discussed as well. All the optimization algorithms are explained with suitable examples and numerical problems in each of the chapters. Features include: Integrates operations research, reliability, and novel computerized technologies in single volume, with a modern vision of continuous improvement of mining systems. Systematically reviews optimization methods and algorithms applied to mining systems including reliability analysis. Gives out software-based solutions such as MATLAB, AMPL, LINDO for the optimization problems. All discussed algorithms are supported by examples in each chapter. Includes case studies for performance improvement of the mine systems. This book is aimed primarily at professionals, graduate students, and researchers in mining engineering

Sensitivity analysis of fuzzy-analytic hierarchical process (FAHP) decision-making model in selection of underground metal mining method

This study aims to analyse the sensitivity in decision-making which results in the selection of the appropriate underground metal mining method using the fuzzy-analytical hierarchy process (FAHP) model. The proposed model considers sixteen criteria for the selection of the most appropriate mining method out of the seven. The model consists of three-layer viz. the first layer represents the criteria (factors which influence the mining method), the second layer represents the sub-criteria (categorisation of the factors) and the third layer represents the alternatives (mining methods). The priority of the different mining methods was determined based on global weights. The global weights of seven mining method were determined using a different fuzzification factor under different decision-making attitudes (optimistic, pessimistic and unbiased). The sensitivity of the decision-making results was analysed in order to understand the robustness of the model. Keywords: Decision-making, Mining methods, Fuzzy-AHP, Sensitivity analysi

Development of machine vision-based ore classification model using support vector machine (SVM) algorithm

© 2017, Saudi Society for Geosciences. All Right Reserved. The product of the mining industry (ore) is considered to be the raw material for the metal industry. The destination policy of the raw materials of iron mine is highly dependent on the class of iron ores. Thus, regular monitoring of iron ore class is the urgent need at the mine for accurately assigning the destination policy of raw materials. In most of the iron ore mines, decisions on ore class are made based on either visual inspection by the geologist or laboratory analyses of the ores. This process of ore class estimation is time consuming and also challenging for continuous monitoring. Thus, the present study attempts to develop an online vision-based technology for classification of iron ores. A laboratory-scale transportation system is designed using conveyor belt for online image acquisition. A multiclass support vector machine (SVM) model was developed to classify the iron ores. A total of 2200 images were captured for developing the ore classification model. A set of 18 features (9-histogram-based colour features in red, green and blue (RGB) colour space and 9-texture features based on intensity (I) component of hue, saturation and intensity (HSI) colour space) were extracted from each image. The performance of the SVM model was evaluated using four confusion matrix parameters (sensitivity, accuracy, misclassification and specificity). The SVM model performance was also compared with the other methods like K-nearest neighbour, classification discriminant, Naïve Bayes, classification tree and probabilistic neural network. It was observed that the SVM classification model performs better than the other classification methods

Effect on the performance of a support vector machine based machine vision system with dry and wet ore sample images in classification and grade prediction

The aim of the present study is to analysing the effect of water absorption on iron ore samples in the performances of SVM-based machine vision system. Two types of SVM-based machine vision system (classification and regression) were designed and developed, and performances were compared with dry and wet ore sample images. The images of the ore samples were captured in both the conditions (wet and dry) to examine the proposed model performance. A total of 280 image features were extracted and optimised using sequential forward floating selection (SFFS) algorithm for model development. The iron ore samples were collected from an Indian iron ore mine (Guamine), and image capturing system was fabricated in the laboratory for executing the proposed study. The results indicated that a different set of optimised features obtained for dry and wet sample images in both the models (classification and regression). Furthermore, the performance of both the models with dry sample images was found to be relatively better than the wet sample images

Development of machine vision-based system for Iron ore grade prediction using gaussian process regression (GPR)

India is one of the major iron ore producing country and requires quality monitoring of iron ore. An attempt has made to develop a vision-based system for continuous iron ore grade prediction during transportation of ores through conveyors. A Gaussian process regression (GPR) algorithm was used to develop the model. To design the system, a pilot conveyor belt setup was fabricated to replicate the mine conveyor system and consists of image capturing system to capture images during transportation of ores. The images were processed and GPR was calibrated using the grade values of 26-iron ore samples. A set of 18 features (9-colors and 9-textures) were extracted from each of the 26-captured images for model development. The performance results revealed that the predicted grade has closely agreement with the actual grade of the ores. The correlation coefficient (R2) between the observed and predicted grades was found to be 0.9569

Vegetation activity enhanced in India during the COVID-19 lockdowns: evidence from satellite data

The Severe Acute Respiratory Syndrome-COronaVIrus Diseases 2019 (SARS-COVID-19) has sternly affected the entire world in terms of human health, loss of lives, and huge economic losses. However, pandemic-triggered lockdown (LD) events (as a preventive measure) have compelled to stop or reduce major economic activities, exerting positive impacts on the terrestrial environment. We deployed a variety of satellite products (i.e., normalized difference vegetation index (NDVI), solar-induced chlorophyll fluorescence (SIF), and aerosol optical depth (AOD)) along with gridded climatic dataset (temperature (TEMP), precipitation (PREC), and net radiation (NR)) to quantify the changes in vegetation activity (greenness and productivity) during the LD period over the Indian biogeographic provinces (BGPs) as compared to the average conditions over the previous three years (2017-2019). The analysis of the NDVI and SIF data revealed that vegetation greenness and productivity significantly enhanced during LD periods (by up to 37 to 55%, respectively). The influence of climatic drivers (PREC, TEMP, and NR) on vegetation activity was also investigated. We found that the enhancement in the vegetation activity (over BGPs) during the LD period was not entirely driven by the climatic parameters, and was therefore inferred to be also influenced by the LD events. Moreover, vegetation activity around the mining clusters were largely improved during the LD period (by up to 78%) over the coal mining, followed by iron ore mining (up to 63%), and stone mining (up to 41%) clusters) regions. In a nutshell, it can be deliberated that COVID-triggered preventive measures (i.e., country-level LD, travel bans, industry ban, curtail in mining capacity, among others) likely enhanced vegetation health and productivity. Thereby, regulatory measures can be seen as a viable option for improving the terrestrial environmental conditions in the context of climate change in the near future

Quantifying the impacts of opencast mining on vegetation dynamics over eastern India using the long-term Landsat-series satellite dataset

Enhanced spatio-temporal and up-to-date information on vegetation dynamics at various spatial scales are imperative in understanding the human, biosphere, and atmosphere interactions. Thus, the present study attempts to derive the vegetation greenness trends with the medium spatial resolution (30 m) satellite data at the regional scale with the support of Google Earth Engine (GEE) cloud platform. The long-term Landsat series satellite dataset was employed to characterize vegetation greenness trends using the Mann-Kendall test over the mining-dominated regions of Eastern India (Jharkhand and Odisha states) for two study periods, viz. earlier (1988–2004) and later (2000−2020). The key findings revealed that ∼1285 km 2 (2.97%) and 1688 km 2 (3.91%) areas over Jharkhand state and ∼ 5213 km 2 (5.68%) and 2940 km 2 (3.20%) areas over Odisha state showed the negative vegetation greenness trend (indicative of decreasing vegetation activity) during 1988–2004 and 2000–2020, respectively. It was observed that the major anthropogenic activities, particularly opencast mining, are the major factor for vegetation degradation in Jharkhand and Odisha states, contributing to ∼3–5.7% vegetation degradation during the study periods. The negative vegetation greenness trend patches were mainly observed in mining sites, settlement encroachments, construction sites, roadways, logging sites, etc. The drastic rise in the intensity of mining activities in the last two decades (2000–2020) has led to massive vegetation destruction compared to the earlier period (1988–2004). Furthermore, the key climatic parameters (i.e., precipitation, temperature, downward radiation, and soil moisture) have less control over the long-term vegetation greenness trends in the mining-dominated regions (∼ 27%) in contrast to forest regions (∼ 47%). The findings of the study shall be helpful to the policy-makers, stakeholders, environmentalists, and government bodies to formulate and implement various sustainable development programs in the mining-dominated regions to ensure ecological conservation and enhance ecological services. </p