Prediction of geomechanical parameters using soft computing and multiple regression approach

Singh, Rajesh; Umrao, Ravi Kumar; Ahmad, M.; Ansari, M. K.; Sharma, Laxmi Kant; Singh, T. N.

doi:10.1016/j.measurement.2016.12.023

Cited by 101 publications

(25 citation statements)

References 39 publications

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“…In engineering sciences, the use of ANNs (as a branch of artificial intelligence) has been highlighted by many investigators [25][26][27][28][29][30][31]. Such networks are good tools for forecasting issues, however, they have several limitations such as low learning speed and falling into local minima [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

153

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Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R 2 ). The obtained results showed that although all predictive models are able to approximate flyrock, PSO-ANN predictive model can perform better compared to others. Based on R 2 , values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. These results show higher efficiency of the PSO-ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

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Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

153

View full text Add to dashboard Cite

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“…If R = 0, it can be said that there is no existence of any kind of correlation relationship between the two variables .

R = \frac{\sum_{i = 1}^{n} X_{i} Y_{i} - n \overset{X}{true} \overset{Y}{true}}{\sum_{i = 1}^{n} ((), X_{i}^{2} - n {\overset{false¯}{X}}^{2}) ((), Y_{i}^{2} - n {\overset{false¯}{Y}}^{2})}

where, X and Y are random variables, n represents the number of variables,

\overset{X}{true}

and

\overset{Y}{true}

are arithmetic means of the variables. The multiple regression model is given by Equation .

y = β_{0} + β_{1} X_{1} + β_{2} X_{2} + β_{3} X_{3} + \dots + β_{n} X_{n} + ɛ

where, β 0 is the intercept and ∈ is the error value. Equation represents the sum of linear parameters.…”

Section: Methodsmentioning

confidence: 99%

“…where, X and Y are random variables, n represents the number of variables, X and Y are arithmetic means of the variables. The multiple regression model is given by Equation 2 [25].…”

Section: Mathematical Backgroundmentioning

confidence: 99%

Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool

Babatunde

Abbasoğlu

2018

Env Prog and Sustain Energy

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The objective of this study is to develop a model that can predict the specific yield of PV plant and statistically examine the significance of independent variables during the process. The methods presented will help investors make quick and reliable decisions about installing PV plant at a particular site. To determine the best‐fit model for energy prediction, data from all variables were collected and a relevance check was conducted by examining the relationship between independent variables and dependent variable. The forecasting method makes use of Multiple Linear Regression model. Seven models were developed and two models with the best accuracy are proposed in this article. Variance Inflation Factor, which assesses potential multicollinearity among independent variables, was used to optimize the models that have similar characteristics. The first proposed model has coefficient of multiple determination of goodness of fit (R2) of 96.8%. The second model has R2 of 96%. The models were applied to data series selected from same site; prediction accuracies of 97% and 95% were obtained, respectively. They were also validated with Middle East Technical University North Cyprus Campus PV plant, yielding prediction accuracies of PV specific yield of 92% and 90%, respectively. It could be concluded that the results presented in this study will guide the energy experts about the reliability of the methods used to analyze the technical feasibility of medium or large scale PV projects. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13098, 2019

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“…As discussed earlier, the LSSM maps were produced using six different MFs. The performance assessments of the trained land subsidence models were examined with two evaluating statistical parameters, namely, the root-mean-square error (RMSE) (11) and the coefficient of determination (R 2 ) (12), as suggested by various researchers (Bui et al 2012;Folorunsho et al 2012;Chai and Draxler 2014;Singh et al 2017). …”

Section: Obtaining Land Subsidence Indexes For Mappingmentioning

confidence: 99%

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

et al. 2018

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In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while onethird of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.

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Prediction of geomechanical parameters using soft computing and multiple regression approach

Cited by 101 publications

References 39 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Predictive analysis of photovoltaic plants specific yield with the implementation of multiple linear regression tool

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

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