Gold mine dam levels and energy consumption classification using artificial intelligence methods

This paper presents development of an artificial intelligence (AI)‐based model, genetic expression programming (GEP) to predict long‐term streamflow using large‐scale climate drivers as predictors. GEP is chosen over artificial neural networks (ANNs) model, as ANN is a black‐box model, whereas GEP is able to explain the developed forecast models with mathematical expressions. As a case study, 12 streamflow measuring stations were selected from four different regions of New South Wales (NSW) in eastern Australia. A number of climate indices, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO) and ENSO Modoki index (EMI), were selected as candidate predictors based on the findings of some preliminary studies. Higher predictabilities of the GEP‐based models are evident from the Pearson correlation (r) values ranging between 0.57 and 0.97, which are mostly about twice the values achieved by multiple linear regression (MLR) models in the preliminary study. Performances of the developed models were assessed using standard statistical measures such as root relative squared error (RRSE), relative absolute error (RAE), root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and Pearson correlation (r) values. The developed models are able to predict spring streamflow up to 5 months in advance with significantly high correlation values.

The purpose of this article is to evaluate regression’s potential and soft computing-based models such as linear regression, random forest, random tree, and artificial neural network for concrete mixed with marble powder waste for the accurate prediction of the 28-day compressive strength. A total of 49 experimental observations were collected from previously published research articles and randomly divided into two parts: the training dataset, which accounted for 70% of the total dataset, and the testing dataset, which accounted for 30% of the total dataset and was used to validate the model. To assess each model’s performance, evaluation has been done using five quantitative measures such as coefficient of correlation, root mean square error, mean absolute error, relative absolute error, root relative squared error, and Taylor diagram (graphical representation) was drawn for comparing the performance of developed models. Results conclude that M6-ANN had performed better than other applied models.

Food production in India is largely dependent on cereal crops including rice, wheat and various pulses. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Variability in seasonal climate conditions can have detrimental effect, with incidents of drought reducing production. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. This paper presents the review on use of such machine learning technique for Indian rice cropping areas. This paper discusses the experimental results obtained by applying SMO classifier using the WEKA tool on the dataset of 27 districts of Maharashtra state, India. The dataset considered for the rice crop yield prediction was sourced from publicly available Indian Government records. The parameters considered for the study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.

Early diagnosis of breast cancer is essential to save lives of patients. Usually, medical datasets include a large variety of data that can lead to confusion during diagnosis. The Knowledge Discovery on Database (KDD) process helps to improve efficiency. It requires elimination of inappropriate and repeated data from the dataset before final diagnosis. This can be done using any of the feature selection algorithms available in data mining. Feature selection is considered as a vital step to increase the classification accuracy. This paper proposes a Modified Bat Algorithm (MBA) for feature selection to eliminate irrelevant features from an original dataset. The Bat algorithm was modified using simple random sampling to select the random instances from the dataset. Ranking was with the global best features to recognize the predominant features available in the dataset. The selected features are used to train a Random Forest (RF) classification algorithm. The MBA feature selection algorithm enhanced the classification accuracy of RF in identifying the occurrence of breast cancer. The Wisconsin Diagnosis Breast Cancer Dataset (WDBC) was used for estimating the performance analysis of the proposed MBA feature selection algorithm. The proposed algorithm achieved better performance in terms of Kappa statistic, Mathew’s Correlation Coefficient, Precision, F-measure, Recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE) and Root Relative Squared Error (RRSE).

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions.

Geopolymer concrete is a new invention of the concrete industry. It could be the future of all construction fields due to its performance against severe conditions, and strength. It is a perfect alternative to conventional concrete. It is more sustainable, ecological, durable, and economic than conventional concrete. In the present era, machine learning techniques are also the future of all research and development industries. These techniques predict the results based on their previous data. In the construction industry, the find the results or value are very difficult, time consumable, and laborious. These techniques make them very easier to predict the strength of mix design without making samples and destructive tests. The aim of this study is to predict the compressive strength of flyash-based geopolymer concrete by using deep learning and random forest algorithm and comparing them with different errors and coefficient correlation. After the simulation of data, it is proved that the random forest algorithm is the most suitable technique for the prediction of compressive strength. After the developing a model, the various errors were found for accuracy. The mean absolute error, root mean square error, relative absolute error, and root relative squared error are 1.63%, 2.68%, 30.28%, and 37.47%, respectively for the deep learning predicted compressive strength. The errors provide the proof of model accuracy to predict the compressive strength on the basis of ingredients proportions.KeywordsGreen concreteGeopolymer concreteSustainableMachine learningRandom forest algorithm

This study aimed to forecast the daily reference evapotranspiration (ETo) using a gene-expression programming (GEP) algorithm with limited public weather forecast information over Gaoyou station, located in Jiangsu province, China. To calibrate and validate the gene-expression code, important meteorological data and weather forecast information were collected from the local meteorological station and public weather media, respectively. The GEP algebraic formulation was successfully constructed based only on daily minimum and maximum air temperature using the true FAO56 Penman-Monteith (PM) set as reference values. The performance of the models was then assessed using the correlation coefficient (R), root mean squared error (RMSE), root relative squared error (RRSE) and mean absolute error (MAE). The study demonstrated that GEP is able to calibrate ETo (all errors ≤0.990 mm/day, R = 0.832–0.866) and forecast the daily ETo with good accuracy (RMSE = 1.207 mm/day, MAE = 0.902 mm/day, RRSE = 0.629 mm/day, R = 0.777). The model accuracies slightly decreased over a 7-day forecast lead-time. These results suggest that the GEP algorithm can be considered as a deployable tool for ETo forecast to anticipate decision on short-term irrigation schedule in the study zone.

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3).

Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material’s resistance to deformation. In this paper, artificial intelligence (AI)-based models—especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms—are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R2), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (ρ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R2, MAE, RSE, RMSE, RRMSE, and ρ equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest.

Multi hours ahead prediction of surface ozone gas concentration: Robust artificial intelligence approach

Nowadays , nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to model both linear and nonlinear structures in time series, they are not able to handling both structures equally well. The autoregressive integrated moving average (ARIMA) model and two ANN models namely, Radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) methods were applied to Hyderabad airport traffic data. The data obtained for 15 years from 2002–2003 to 2016–2017 about domestic and international passenger of International Airport of Hyderabad, India. In this research paper, we compared the performances of ARIMA, RBFNN and ERNN were based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results showed that RBFNN obtained the smallest MAE, MAPE and RMSE in both the modeling and forecasting processes. The performances of the three models ranked in ascending order were: ARIMA, ERNN and the RBFNN model. Keywords: T ime series, forecasting, artificial neural networks, ARIMA models, radial basis function neural networks, and Elman recurrent neural networks Cite this Article R. Ramakrishna, Berhe Aregay, Tewodros Gebregergs. The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks. Research & Reviews: Journal of Statistics . 2018; 7(3): 75–90p.

Shear wave velocity (Vs) is considered a key soil parameter in the field of earthquake engineering. The time-averaged shear wave velocity in the upper 30 m (Vs30) layer of soil is used to classify seismic site class. In-situ Vs test is sometimes unsuitable to the project’s need due to financial reasons, noisy environment on site or simply the lack of expertise. This paper attempts to develop a global prediction model for Vs using Standard Penetration Resistance (Nspt), depth (z) and soil type (st) as the independent parameters. Two approaches to modelling would be taken; a multi-linear regression (MLR) model and an ensemble (EN-PSO) model. The EN-PSO model attempts to improve upon the accuracy of the MLR model prediction ability using the ensemble learning method. A dataset was compiled from literatures for this paper. 5 Base models were developed: MLR, Random Forest (RFR), Support Vector Machine (SVR), Artificial Neural Network (ANN) and k-Nearest Neighbor (KNN) which are combined into an ensemble model named EN-PSO. The weights for EN-SPO was then calculated using Particle Swarm Optimization (PSO). The performance of each models were then compared and it was shown that EN-PSO was the best in terms of: MAE (Mean Absolute Error) = 22.085, MAPE (Mean Absolute Percentage Error) = 9.1 %, RMSE (Root Mean Square Error) = 31.741 and R2 Coefficient of Determination) = 0.895. In addition, it was also shown that the EN-PSO model was able to improve upon the performance of the MLR model, which the most accurate among the Base models. Comparisons were also made between EN-PSO and other suggested Universal Vs correlations and EN-PSO was shown to outperform the other correlation based on prediction using a modified Test set. Three new empirical correlations as alternative for the EN-PSO model was also presented.

In this article, a comparison study of three artificial intelligence (AI) techniques for energy consumption estimation are presented. The models considered are: multilayer perceptron (MLP); radial basis function (RBF) and support vector machine (SVM). The energy consumption is modeled as a function of activity, structural and intensity changes. The models are applied to Canadian industrial manufacturing data from 1990 to 2000. Comparisons were based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Square Error (RRSE) as well as Simulation Time. The best results were obtained for the Multilayer Perceptron.

Analyzing the hidden information from the images are helpful to identify the various causes. In general processing of images includes Pre-processing, segmentation, Feature extraction and Classification. Significance of classifier is essential since results are always based on the classifier. The ultimate aim is to explain how WEKA tool is used for rheumatoid arthritis and investigate the performance of the various classifiers for huge data. In our method we are distinctively give attention to the classification methods like ADTree, Best First Decision tree(BF), Decision stump, J48Pruned tree, J48 Graft Pruned tree, Least Absolute Deviation regression trees (LAD), Logistic Model Tree(LMT), Naïve-Bayes (NB), Random tree, Random forest tree, CART Decision tree. The features like Area, perimeter, circularity, integrated density, Median, Skewness, Raw integrated density, and Roundness and solidity are obtained from the Lymphocytes images and formed the data set. Different classifier is applied for RA facet for Validation. RA facet contains 108 rows and 10 columns. Using classifier to find out the various values like Relative Absolute Error, and Kappa Statistic, Mean Absolute Error, Root Mean Squared Error and Root Relative Squared Error. From those values compare with all the methods ADT classifier is suggested for use in huge data.

Friction Stir Welding (FSW) is a popular metal joining technique due to its solid-state deformation mechanism and high joining strength. The process is controlled by adjusting the process parameters, including rotational speed, translational speed, and axial force. However, the lack of proper settings and optimization of these parameters can result in a defective weld joint and unexpected failure. The traditional trial-and-error-based experimental and computational approaches for finding the optimal combination of parameters are expensive, time-consuming, and intricate. Machine Learning (ML), a data analysis technique, can be a useful alternative to optimize the process parameters in the FSW process. The tool rotational speed, translational speed, and axial force in FSW can be categorized as the input parameters or feature variables in the ML dataset. On the other hand, a performance parameter, such as the ultimate tensile strength of the welded joint, can be set as the output parameter or the target variable in the dataset. This study presents an ML modeling technique to predict the ultimate tensile strength for an FSW welded joint. A dataset is developed by collecting data for the three features and a target variable from experimental procedures. The skill of the trained ML model is estimated using the k-fold cross-validation procedure. The ML modeling findings for several supervised algorithms are evaluated based on the mean absolute error, root means squared error, relative absolute error, and root relative squared error. The sensitivity analysis is conducted by finding the global correlation coefficient. The ML model is validated by comparing its results with the experimental data and showing a good agreement between the ML model prediction and experimental results.