scholarly journals Integration of grey analysis with artificial neural network for classification of slope failure

2021 ◽  
Vol 325 ◽  
pp. 01008
Author(s):  
Ashanira Mat Deris ◽  
Badariah Solemon ◽  
Rohayu Che Omar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Grey Relational Analysis ◽  
Slope Failure ◽  
Reference Sequence ◽  
Ann Model ◽  
Relational Analysis ◽  
Artificial Neural ◽  
Grey Relational

With the advent of technology and the introduction of computational intelligent methods, the prediction of slope failure using the machine learning (ML) approach is rapidly growing for the past few decades. This study employs an “artificial neural network” (ANN) to predict the slope failures based on historical circular slope cases. Using the feed-forward back-propagation algorithm with a multilayer perceptron network, ANN is a powerful ML method capable of predicting the complex model of slope cases. However, the prediction result of ANN can be improved by integrating the statistical analysis method, namely grey relational analysis (GRA), to the ANN model. GRA is capable of identifying the influencing factors of the input data based on the correlation level of the reference sequence and comparability sequence of the dataset. This statistical machine learning model can analyze the slope data and eliminate the unnecessary data samples to improve the prediction performance. Grey relational analysis-artificial neural network (GRANN) prediction model was developed based on six slope factors: unit weight, friction angle, cohesion, pore pressure ratio, slope height, and slope angle, with the factor of safety (FOS) as the output factor. The prediction results were analyzed based on accuracy percentage and receiver operating characteristic (ROC) values. It shows that the GRANN model has outperformed the ANN model by giving 99% accuracy and 0.999 ROC value, compared with 91% and 0.929.

A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction

2019 ◽  
Vol 142 (5) ◽  
Author(s):  
Byeongho Yu ◽  
Dongsu Kim ◽  
Heejin Cho ◽  
Pedro Mago
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Nonlinear System ◽  
Prediction Model ◽  
Thermal Load ◽  
Ann Model ◽  
Load Prediction ◽  
Artificial Neural ◽  
Nonlinear Autoregressive

Abstract Thermal load prediction is a key part of energy system management and control in buildings, and its accuracy plays a critical role to improve building energy performance and efficiency. Regarding thermal load prediction, various types of prediction model have been considered and studied, such as physics-based, statistical, and machine learning models. Physical models can be accurate but require extended lead time for model development. Statistical models are relatively simple to develop and require less computation time, but they may not provide accurate results for complex energy systems with intricate nonlinear dynamic behaviors. This study proposes an artificial neural network (ANN) model, one of the prevalent machine learning methods to predict building thermal load, combining with the concept of nonlinear autoregressive with exogenous inputs (NARX). NARX-ANN prediction model is distinguished from typical ANN models because the NARX concept can address nonlinear system behaviors effectively based on its recurrent architectures and time indexing features. To examine the suitability and validity of NARX-ANN model for building thermal load prediction, a case study is carried out using the field data of an academic campus building at Mississippi State University (MSU). Results show that the proposed NARX-ANN model can provide an accurate and robust prediction performance and effectively address nonlinear system behaviors in the prediction.

A hybrid grey based artificial neural network and C&R tree for project portfolio selection

2017 ◽  
Vol 24 (3) ◽  
pp. 651-665 ◽  
Author(s):  
Farshad Faezy Razi ◽  
Seyed Hooman Shariat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Tree ◽  
Portfolio Selection ◽  
Grey Relational Analysis ◽  
Project Portfolio ◽  
Content Type ◽  
Relational Analysis ◽  
Project Portfolio Selection ◽  
Grey Relational

Purpose The purpose of this paper is twofold: the selection of project portfolios through hybrid artificial neural network algorithms, feature selection based on grey relational analysis, decision tree and regression; and the identification of the features affecting project portfolio selection using the artificial neural network algorithm, decision tree and regression. The authors also aim to classify the available options using the decision tree algorithm. Design/methodology/approach In order to achieve the research goals, a project-oriented organization was selected and studied. In all, 49 project management indicators were chosen from A Guide to the Project Management Body of Knowledge (PMBOK Guide), and the most important indicators were identified using a feature selection algorithm and decision tree. After the extraction of rules, decision rule-based multi-criteria decision making matrices were produced. Each matrix was ranked through grey relational analysis, similarity to ideal solution method and multi-criteria optimization. Finally, a model for choosing the best ranking method was designed and implemented using the genetic algorithm. To analyze the responses, stability of the classes was investigated. Findings The results showed that projects ranked based on neural network weights by the grey relational analysis method prove to be better options for the selection of a project portfolio. The process of identification of the features affecting project portfolio selection resulted in the following factors: scope management, project charter, project management plan, stakeholders and risk. Originality/value This study presents the most effective features affecting project portfolio selection which is highly impressive in organizational decision making and must be considered seriously. Deploying sensitivity analysis, which is an innovation in such studies, played a constructive role in examining the accuracy and reliability of the proposed models, and it can be firmly argued that the results have had an important role in validating the findings of this study.

scholarly journals Analysis and Prediction of the Thiourea Gold Leaching Process Using Grey Relational Analysis and Artificial Neural Networks

Minerals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 811
Author(s):  
Rui Xu ◽  
Xiaolong Nan ◽  
Feiyu Meng ◽  
Qian Li ◽  
Xuling Chen ◽  
...  
Keyword(s):  
Grey Relational Analysis ◽  
Iron Concentration ◽  
Gold Recovery ◽  
Ann Model ◽  
Gold Leaching ◽  
Relational Analysis ◽  
Leaching Process ◽  
Influencing Parameters ◽  
Artificial Neural ◽  
Grey Relational

The thiourea (TU) leaching of gold from refractory ores can be considered an alternative to cyanidation. However, the high reagent consumption causes an increase in cost, which seriously limits its use. In order to effectively reduce the TU consumption, it is necessary to analyze the influencing parameters of gold recovery and TU consumption and apply them to the prediction of the TU leaching process. This paper investigated six potential influencing parameters and used grey relational analysis (GRA) to analyze the relational degree between each parameter and gold recovery and TU consumption. Then, the artificial neural network (ANN) model was established to simultaneously predict the gold recovery and TU consumption in the TU gold leaching process. The results of the GRA indicated that the leaching time, initial pH, temperature, TU dosage, stirring speed, and ferric iron concentration were all well related to the gold recovery and TU consumption. Therefore, the incorporation of these parameters can significantly improve the ANN model validation. The predictive results noted that the prediction accuracy of gold recovery varied from 94.46% to 98.06%, and the TU consumption varied from 95.15% to 99.20%. Thus, the predicted values corresponded closely to the experimental results, which suggested that the ANN model can accurately reflect the relationship between the operational conditions and the gold recovery and TU consumption. This prediction method can be used as an auxiliary decision-making tool in the TU gold leaching process, and it has broad engineering application prospects in engineering.

scholarly journals Artificial Neural Network and Machine Learning Based Methods for Population Estimation of Rohingya Refugees: Comparing Data-Driven and Satellite Image-Driven Approaches

2019 ◽  
Vol 06 (04) ◽  
pp. 439-455 ◽  
Author(s):  
Nahian Ahmed ◽  
Nazmul Alam Diptu ◽  
M. Sakil Khan Shadhin ◽  
M. Abrar Fahim Jaki ◽  
M. Ferdous Hasan ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Satellite Image ◽  
Population Estimation ◽  
Data Driven ◽  
Gradient Boosting ◽  
Ann Model ◽  
Data Driven Approach ◽  
Artificial Neural

Manual field-based population census data collection method is slow and expensive, especially for refugee management situations where more frequent censuses are necessary. This study aims to explore the approaches of population estimation of Rohingya migrants using remote sensing and machine learning. Two different approaches of population estimation viz., (i) data-driven approach and (ii) satellite image-driven approach have been explored. A total of 11 machine learning models including Artificial Neural Network (ANN) are applied for both approaches. It is found that, in situations where the surface population distribution is unknown, a smaller satellite image grid cell length is required. For data-driven approach, ANN model is placed fourth, Linear Regression model performed the worst and Gradient Boosting model performed the best. For satellite image-driven approach, ANN model performed the best while Ada Boost model has the worst performance. Gradient Boosting model can be considered as a suitable model to be applied for both the approaches.

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