Implementing artificial neural networks and support vector machines to predict lost circulation

Abbas, Ahmed K.; Al-haideri, Najim A.; Bashikh, Ali A.

doi:10.1016/j.ejpe.2019.06.006

Cited by 58 publications

(20 citation statements)

References 35 publications

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“…This method is based on local separation functions. “SVMs are one of the most effective mathematical approaches for both machine learning and data mining communities,” (Abbas et al., 2019). These functions interpose a separation hyperplane between a set of nearby data (local), with its separation flexibility depending on the type of function.…”

Section: Resultsmentioning

confidence: 99%

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Meza

Yepes

Rodrigo-Ilarri

et al. 2019

Heliyon

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This study presents an analysis of three models associated with artificial intelligence as tools to forecast the generation of urban solid waste in the city of Bogotá, in order to learn about this type of waste's behavior. The analysis was carried out in such a manner that different efficient alternatives are presented. In this paper, a possible decision-making strategy was explored and implemented to plan and design technologies for the stages of collection, transport and final disposal of waste in cities, while taking into account their particular characteristics. The first model used to analyze data was the decision tree which employed machine learning as a non-parametric algorithm that models data separation limitations based on the learning decision rules on the input characteristics of the model. Support vector machines were the second method implemented as a forecasting model. The primary advantage of support vector machines is their proper adjustment to data despite its variable nature or when faced with problems with a small amount of training data. Lastly, recurrent neural network models to forecast data were implemented, which yielded positive results. Their architectural design is useful in exploring temporal correlations among the same. Distribution by collection zone in the city, socio-economic stratification, population, and quantity of solid waste generated in a determined period of time were factors considered in the analysis of this forecast. The results found that support vector machines are the most appropriate model for this type of analysis.

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Section: Resultsmentioning

confidence: 99%

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Meza

Yepes

Rodrigo-Ilarri

et al. 2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…The support vector machine is one of the artificial neural network branches that can be applied for regression and classification purposes through a theory of statistical learning. This approach is also considered as an efficient tool in machine learning as well as data mining (Vapnik 2013;Abbas et al 2019). In this paper, the kernel function and…”

Section: Machine Learning Systems Methodologymentioning

confidence: 99%

Prediction of vitrinite reflectance values using machine learning techniques: a new approach

Sadeghtabaghi

Talebkeikhah

Rabbani

2020

J Petrol Explor Prod Technol

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Vitrinite reflectance (VR) is considered the most used maturity indicator of source rocks. Although vitrinite reflectance is an acceptable parameter for maturity and is widely used, it is sometimes difficult to measure. Furthermore, Rock-Eval pyrolysis is a current technique for geochemical investigations and evaluating source rock by their quality and quantity of organic matter, which provide low cost, quick, and valid information. Predicting vitrinite reflectance by using a quick and straightforward method like Rock-Eval pyrolysis results in determining accurate and reliable values of VR with consuming low cost and time. Previous studies used empirical equations for vitrinite reflectance prediction by the Tmax data, which was accompanied by poor results. Therefore, finding a way for precise vitrinite reflectance prediction by Rock-Eval data seems useful. For this aim, vitrinite reflectance values are predicted by 15 distinct machine learning models of the decision tree, random forest, support vector machine, group method of data handling, radial basis function, multilayer perceptron, adaptive neuro-fuzzy inference system, and multilayer perceptron and adaptive neuro-fuzzy inference system, which are coupled with evolutionary optimization methods such as grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and genetic algorithm, with four inputs of Rock-Eval pyrolysis parameters of Tmax, S1/TOC, HI, and depth for the first time. Statistical evaluations indicate that the decision tree is the most precise model for VR prediction, which can estimate vitrinite reflectance precisely. The comparison between the decision tree and previous proposed empirical equations indicates that the machine learning method performs much more accurately.

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“…prototipe masukan dipindahkan ke ruang fitur dimensi tinggi dengan menggunakan metode pemetaan nonlinear. Aturan ini memilih hyperplane pemisah yang optimal dengan jarak maksimum antara kelas yang dapat dipisahkan [14]. R Studio pada paket e1071 mampu membangun model SVM dari data latih yang ditetapkan seperti Tabel 1.…”

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Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province

Tarigan

Bayupati

Putri

2021

J. Teknol. dan Sist. Komput

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Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.

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Implementing artificial neural networks and support vector machines to predict lost circulation

Cited by 58 publications

References 35 publications

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks

Prediction of vitrinite reflectance values using machine learning techniques: a new approach

Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province

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