A Novel Approach for Blast-Induced Fly Rock Prediction Based on Particle Swarm Optimization and Artificial Neural Network

Kumar, Navdeep; Mishra, Balmukund; Bali, Vikram

doi:10.1007/978-981-10-8198-9_3

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…In order to investigate the causes, as well as the distance of fly-rock, we collected the basic parameters of 210 blasting events, including explosive charge per delay, powder factor, stemming, spacing, burden, and fly-rock distance, abbreviated as W, PF, ST, S, B, and FR, respectively. These parameters were also used for estimating FR by many previous scientists (e.g., [10,[42][43][44]). Subsequently, the experimental datasets were analyzed and visualized.…”

Section: Dataset Usedmentioning

confidence: 99%

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

et al. 2019

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Fly-rock induced by blasting is an undesirable phenomenon in quarries. It can be dangerous for humans, equipment, and buildings. To minimize its undesirable hazards, we proposed a state-of-the-art technology of fly-rock prediction based on artificial neural network (ANN) models and their robust combination, called EANNs model (ensemble of ANN models); 210 fly-rock events were recorded to develop and test the ANN and EANNs models. Of thi sample, 80% of the whole dataset was assigned to develop the models, the remaining 20% was assigned to confirm the models developed. Accordingly, five ANN models were designed and developed using the training dataset (i.e., 80% of the whole original data) first; then, their predictions on the training dataset were ensembled to generate a new training dataset. Subsequently, another ANN model was developed based on the new set of training data (i.e., EANNs model). Its performance was evaluated through a variety of performance indices, such as MAE (mean absolute error), MAPE (mean absolute percentage error), RMSE (root-mean-square error), R2 (correlation coefficient), and VAF (variance accounted for). A promising result was found for the proposed EANNs model in predicting blast-induced fly-rock with a MAE = 2.777, MAPE = 0.017, RMSE = 4.346, R2 = 0.986, and VAF = 98.446%. To confirm the performance of the proposed EANNs model, another ANN model with the same structure was developed and tested on the training and testing datasets. The findings also indicated that the proposed EANNs model yielded better performance than those of the ANN model with the same structure.

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Section: Dataset Usedmentioning

confidence: 99%

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

et al. 2019

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“…Bali et.al. [16] discussed the use of optimization technique for Rock Predication by using Artificial Neural Networks. Bali et al [17] mentioned the use of optimizing technique using goal programming approach.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

Gangwar¹,

Bali²,

Kumar³

2018

ICST Transactions on Scalable Information Systems

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The objective of this work is to present a comprehensive exploration of deep learning based wind forecasting model. The forecasting of speed of wind is called as the wind speed forecasting/prediction. It is basically done to achieve the better sustainability for power generation and production. The availability of wind energy in ample amount makes it quite comfortable to be utilized for various functionalities. In this research work the main aim is to forecast speed using LSTM including certain parameters and then comparative analysis is done using SVM. Both are machine learning approaches but have different functionalities in comparison to each other. This comparison is done to obtain the better technique which can be further applied on larger datasets to design a better, accurate, efficient forecasting model for speed of wind. The survey and implementation of both the techniques gave a clear idea about the utilisation of long short term memory for the better and enhanced wind speed forecasting. The forecasting is based on various atmospheric variables, and the data set is taken from the kaggle datsets which have numerous attributes but we have considered few of them only for the prediction purpose.

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“…Hussain et al (2018) used J48, PART, Random Forest and Bayes classifiers to predict students" end semester grades on a data set of 300 students from different colleges and found that Random Forest classification algorithm gives the best results based on accuracy and classifier errors [1]. Bali et al (2018) proposed the use of optimization technique for Optimal Component Selection and "Rock Predication by using Artificial Neural Networks" [2][9] [11]. Mittal et al (2018) designed a model for predicting diabetes using Naïve Bayes, K-NN and Support Vector Machine (SVM) and concluded that the SVM classifier outperforms the rest of the two classifiers [3].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for Classifying Students Based on Performance using Machine Learning Techniques

2019

ijrte

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In today’s competitive world of educational organizations, the universities and colleges are using various data mining tools and techniques to improve the students’ performance. Now a days, when the number of drop out students is increasing every year, if we get to know the probability of a student whether he/she will be able to cope up easily with the course, it is possible to take some preventive actions beforehand. In other words, if we get to know that a student will clear his papers in the course or he will have reappear in papers, a teacher/parent can focus more on such students. The data set of students has been taken from the UCI Machine Learning repository where a sample of 131 students have been provided with twenty-two attributes. The results of six classification algorithms have been compared in order to predict the most appropriate model for classifying whether a student will have a reappear in a course or not.

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A Novel Approach for Blast-Induced Fly Rock Prediction Based on Particle Swarm Optimization and Artificial Neural Network

Cited by 14 publications

References 8 publications

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model

Comparative Analysis of Wind Speed Forecasting Using LSTM and SVM

Prediction Model for Classifying Students Based on Performance using Machine Learning Techniques

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