Volume 15 (2024)
Volume 14 (2023)
Volume 13 (2022)
Volume 12 (2021)
Volume 11 (2020)
Volume 10 (2019)
Volume 9 (2018)
Volume 8 (2017)
Volume 7 (2016)
Volume 6 (2015)
Volume 5 (2014)
Volume 4 (2013)
Volume 3 (2012)
Volume 2 (2011)
Volume 1 (2010)
Analysis of concentration of ambient particulate matter in the surrounding area of an opencast coal mine using machine learning techniques

Podicheti Ravi Kiran; Ramchandar Karra

Articles in Press, Accepted Manuscript, Available Online from 02 February 2024


  Opencast coal mines play a crucial role in meeting the energy demands of a country. However, the operations will result in deterioration of ambient air quality, particularly due to particulate emissions. The dispersion of particulate matter will vary based on the mining parameters and local meteorological ...  Read More

Ground Water Quality Analysis using Machine Learning Techniques: a Critical Appraisal

Naman Chandel; Sushindra Kumar Gupta; Anand Kumar Ravi

Volume 15, Issue 2 , April 2024, , Pages 419-426


  Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can ...  Read More

A Promising Automatic System for studying of Coal Mine Surfaces using Sentinel-2 Data to Assess a Classification on a Pixel-based Pattern

Ajay Kumar

Volume 15, Issue 1 , January 2024, , Pages 41-54


  Land use (LU) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. However, accurately classifying pixels according to their land use poses a significant difficulty. Despite advancements in feature extraction techniques, the ...  Read More

Rate of Penetration Prediction in Drilling Operation in Oil and Gas Wells by K-nearest Neighbors and Multi-layer Perceptron Algorithms

Yahia ElSayed Khamis; Shady Galal El-Rammah; Adel M Salem

Volume 14, Issue 3 , July 2023, , Pages 755-770


  The rate of penetration plays a key role in maximizing drilling efficiency, so it is essential for the drilling process optimization and management. Traditional mathematical models have been used with some success to predict the rate of penetration in drilling. Due to the high complexity and non-linear ...  Read More

Artificial Neural Network Modeling as an Approach to Limestone Blast Production Rate Prediction: a Comparison of PI-BANN and MVR Models

Blessing Olamide Taiwo; Gebretsadik Angesom; Yewuhalashet Fissha; Yemane Kide; Enming Li; Kiross Haile; Oluwaseun Augustine Oni

Volume 14, Issue 2 , April 2023, , Pages 375-388


  Rock blast production rate (BPR) is one of the most crucial factors in the evaluation of mine project's performance. In order to improve the production of a limestone mine, the blast design parameters and image analysis results are used in this work to evaluate the BPR. Additionally, the effect of rock ...  Read More

Application of Machine Learning Techniques to Predict Haul Truck Fuel Consumption in Open-Pit Mines

S. Alamdari; M.H. Basiri; A. Mousavi; A. Soofastaei

Volume 13, Issue 1 , January 2022, , Pages 69-85


  The haul trucks consume a significant energy source in open-pit mines, where diesel fuel is widely used as the main energy source. Improving the haul truck fuel consumption can considerably decrease the operating cost of mining, and more importantly, reduce the pollutants and greenhouse gas emissions. ...  Read More

A Comparative Study on Machine Learning Algorithms for Geochemical Prediction Using Sentinel-2 Reflectance Spectroscopy

Muhammad Ahsan M.; T. Celik; B. Genc

Volume 12, Issue 4 , October 2021, , Pages 987-1001


  The distribution of stream sediments is usually considered as an important and very useful tool for the early-stage exploration of mineralization at the regional scale. The collection of stream samples is not only time-consuming but also very costly. However, the advancements in space remote sensing ...  Read More

A Comparative Study of SVM and RF Methods for Classification of Alteration Zones Using Remotely Sensed Data

N. Mahvash Mohammadi; A. Hezarkhani

Volume 11, Issue 1 , January 2020, , Pages 49-61


  Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, ...  Read More