Using Machine Learning for Explosion Yield Estimation

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.

본 연구는 Deep Neural Network(DNN)을 이용하여 광주-기아 챔피언스 필드의 일일 관중 수를 예측함으로써 이를 통해 구단과 관련기업의 마케팅 자료제공 및 구장 내 부대시설의 재고관리에 자료로 쓰임을 목적으로 수행 되었다. 본 연구 에서는 Artificial Neural Network(ANN)의 종류인 DNN 모델을 이용하였으며 DNN 모델의 과적합을 막기 위해 Dropout 과 Batch normalization 적용한 모델을 바탕으로 총 4종류를 설계하였다. 각각 10개의 DNN을 만들어 예측값의 Root Mean Square Error(RMSE)와 Mean Absolute Percentage Error(MAPE)의 평균값을 낸 모델과 예측값의 평균으로 RMSE와 MAPE를 평가한 Ensemble 모델을 만들었다. 모델의 학습 데이터는 2008년부터 2017년까지의 관중 수 데이터를 수집하여 수집된 데이터의 80%를 무작위로 선정하였으며, 나머지 20%는 테스트 데이터로 사용하였다. 총 100회의 데이터 선정, 모델구성 그리고 학습 및 예측을 한 결과 Ensemble 모델은 DNN 모델의 예측력이 가장 우수하게 나왔으며, 다중선 형회귀 모델 대비 RMSE는 15.17%, MAPE는 14.34% 높은 예측력을 보이고 있다.In this paper, we used the Deep Neural Network (DNN) to predict the number of daily spectators of Gwangju -KIA Champions Field in order to provide marketing data for the team and related businesses and for managing the inventories of the facilities in the stadium. In this study, the DNN model, which is based on an artificial neural network (ANN), was used, and four kinds of DNN model were designed along with dropout and batch normalization model to prevent overfitting. Each of four models consists of 10 DNNs, and we added extra models with ensemble model. Each model was evaluated by Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The learning data from the model randomly selected 80% of the collected data from 2008 to 2017, and the other 20% were used as test data. With the result of 100 data selection, model configuration, and learning and prediction, we concluded that the predictive power of the DNN model with ensemble model is the best, and RMSE and MAPE are 15.17% and 14.34% higher, correspondingly, than the prediction value of the multiple linear regression model.

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

The forecasting of evapotranspiration (ET) in some water-stressed regions remains a major challenge due to the lack of reliable and sufficient historical datasets. For efficient water balance, ET remains the major component and its proper forecasting and quantifying is of the utmost importance. This study utilises the 18-year (2001 to 2018) MODIS ET obtained from a drought-affected irrigation scheme in the Eastern Cape Province of South Africa. This study conducts a teleconnection evaluation between the satellite-derived evapotranspiration (ET) time series and other related remotely sensed parameters such as the Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and precipitation (P). This comparative analysis was performed by adopting the Mann–Kendall (MK) test, Sequential Mann–Kendall (SQ-MK) test, and Multiple Linear Regression methods. Additionally, the ET detailed time-series analysis with the Keiskamma River streamflow (SF) and monthly volumes of the Sandile Dam, which are water supply sources close to the study area, was performed using the Wavelet Analysis, Breaks for Additive Seasonal and Trend (BFAST), Theil–Sen statistic, and Correlation statistics. The MODIS-obtained ET was then forecasted using the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANNs) for a period of 5 years and four modelling performance evaluations such as the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Pearson Correlation Coefficient (R) were used to evaluate the model performances. The results of this study proved that ET could be forecasted using these two time-series modeling tools; however, the ARIMA modelling technique achieved lesser values according to the four statistical modelling techniques employed with the RMSE for the ARIMA = 37.58, over the ANN = 44.18; the MAE for the ARIMA = 32.37, over the ANN = 35.88; the MAPE for the ARIMA = 17.26, over the ANN = 24.26; and for the R ARIMA = 0.94 with the ANN = 0.86. These results are interesting as they give hope to water managers at the irrigation scheme and equally serve as a tool to effectively manage the irrigation scheme.

Lithology classification is crucial for efficient and sustainable resource exploration in the oil and gas industry. Missing values in well-log data, such as Gamma Ray (GR), Neutron Porosity (NPHI), Bulk Density (RHOB), Deep Resistivity (RS), Delta Time Compressional (DTCO), Delta Time Shear (DTSM), and Resistivity Deep (RD), significantly affect machine learning classification accuracy. This study applied three algorithms, extreme gradient boosting (XGBoost), K-nearest neighbours (KNN), and the artificial neural network (ANN), to handle missing values in well-log datasets, particularly datasets with extreme missing data (30 %). Results indicated that XGBoost was the most efficient and accurate, especially for RHOB, NPHI, DTCO, and DTSM, with the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The ANN also performed effectively, particularly on the GR, RS, and RD features, after the use of preprocessing techniques such as isolation forest and bias correction. However, the ANN can suffer from overfitting and requires large datasets for optimal performance. In contrast, KNN struggled with missing-not-at-random (MNAR) data due to its reliance on the k parameter and distance metric, making it less effective in mapping missing data relationships.•Missing values in well-log data can hinder lithology classification accuracy for efficient resource exploration in the oil and gas industry.•This research aims to address the problem of missing values in well-log datasets by applying machine learning algorithms such as XGBoost, ANN, and KNN to enhance classification performance.•XGBoost demonstrated superior performance in handling extreme missing data (30 %) in well-log datasets. ANN was effective but prone to overfitting for small datasets, while KNN struggled with missing-not-at-random (MNAR) data due to limitations in its distance-based approach.

The right design and the high efficiency of solar energy systems require accurate information on the availability of solar radiation. Due to the cost of purchase and maintenance of the radiometers, these data are not readily available. Therefore, there is a need to develop alternative ways of generating such data. Artificial neural networks (ANNs) are excellent and effective tools for learning, pinpointing or generalising data regularities, as they have the ability to model nonlinear functions; they can also cope with complex ‘noisy’ data. The main objective of this paper is to show how to reach an optimal model of ANNs for applying in prediction of solar radiation. The measured data of the year 2007 in Ghardaia city (Algeria) are used to demonstrate the optimisation methodology. The performance evaluation and the comparison of results of ANN models with measured data are made on the basis of mean absolute percentage error (MAPE). It is found that MAPE in the ANN optimal model reaches 1.17 %. Also, this model yields a root mean square error (RMSE) of 14.06 % and an MBE of 0.12. The accuracy of the outputs exceeded 97 % and reached up 99.29 %. Results obtained indicate that the optimisation strategy satisfies practical requirements. It can successfully be generalised for any location in the world and be used in other fields than solar radiation estimation.

Developing novel models using neural networks and fuzzy systems for the prediction of strength of rocks from key geomechanical properties

Prediction models of starch content in fresh cassava roots for a tapioca starch manufacturer in Thailand

Agricultural modernization is crucial for the economic development of agriculture. Technique for order preference by similarity to an ideal solution (Topsis) and artificial neural network (ANN) were employed to evaluate and predict agricultural modernization level. A three-layer ANN models were developed to predict agricultural modernization level. The models were compared using the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The 5-4-1 ANN model with standardization transformation was the best model. Topsis and ANN are useful tools to evaluate agricultural modernization level and to provide policy proposals for more efficient decision-making for the local government.

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.

Artificial neural network (ANN) works as a very effective tool in both classification and regression problem. The main advantage lies in the fact that it can draw fine distinctions, patterns, or hidden information of data explicitly without complex mathematical considerations. This study aimed to use the neural network on regression problem to achieve a better performance with a lower computational cost. After constructing the initial architecture, we added randomized weights and biases to the model. The performance of the model is evaluated on a well-known benchmark regression dataset named Auto MPG. The datasets were split among train, validation, and testing in a ratio of 50%-30%-20%. After subsequent tuning of hyperparameters, the refined architecture was able to perform predicting regression variable with a great efficiency and less computational time. The metrics of accuracy were taken as mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage of error (MAPE). Comparative study with the others state-of-the-art metaheuristic algorithms reveals the effectiveness of the proposed ANN. Evaluating the experimental results, it was obvious that, most often ANN did lead the other approaches with all the metrics of accuracy with a least computational time. It is also seen that, in some cases, though ANN revealed a very competitive result with other approaches, however the reduction in computational cost overcame the drawbacks and put neural network in leading.

Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on many factors and their interrelationships, which are very nonlinear and complex. The traditional approaches are unable to solve these complex nonlear processes of SSY. Thus, the development of a reliable and accurate model for estimating the SSY is essential. The goal of this research was to develop a single hybrid artificial intelligence model, which is a hybridization of the artificial neural network (ANN) and genetic algorithm (GA) (ANN-GA) for the estimation of SSY in the Mahanadi River (MR), India, by combining data from 11-gauge stations into a single hybrid generalized model and applying it to every gauging station for estimating the SSY. All parameters of the ANN model were optimized automatically and simultaneously using GA to estimate the SSY. The proposed model was developed considering the temporal monthly hydro-climatic data, such as temperature (T), rainfall (RF), water discharge (Q) and SSY and spatial data, including the rock type (RT), catchment area (CA) and relief (R), of all 11 gauging stations in the MR. The performances of the conventional sediment rating curve (SRC), ANN and multiple linear regression (MLR) were compared with the hybrid ANN-GA model. It was noticed that the ANN-GA model provided with greatest coefficient of correlation (0.8710) and lowest root mean square error (0.0088) values among all comparative SRC, ANN and MLR. Thus, the proposed ANN-GA is most appropriate model compared to other examined models for estimating SSY in the MR Basin, India, particularly at the Tikarapara measuring station. If no measures of SSY are available in the MR, then the modelling approach could be used to estimate SSY at ungauged or gauge stations in the MR Basin.

Air is the most vital constituent for the sustenance of life on earth. Air pollution is the major problem we have been facing. It is important to address this issue to lead a healthy life. Forecasting of air quality will contribute to a healthy society. In this paper, artificial neural network (ANN) predictors trained with conjugate gradient descent have been implemented to forecast air quality index (AQI) in a particular area of interest. Several neural network models such as multilayer perceptron (MLP), Elman, radial basis function and NARX were applied. In these neural network models, four major pollutant concentrations including NO2, CO, O3 and PM10 for the year 2014 to 2016 in Delhi (India) were used to train each predictor. It can be concluded that, among all these models, radial basis function exhibited more accuracy in terms of measures of quality with mean absolute error (MAE) = 7.33, mean absolute percent error (MAPE) = 4.05%, correlation coefficient (R) = 0.993, root mean square error (RMSE) = 9.69 and index of agreement (IA) = 0.99.

Solar radiation data play an important role in solar energy study. These data are not available for location of interest due to lack of meteorological stations. Therefore, it is all important to forecast solar radiation for a site using different climatic variables. Methods in practice like neural networks with back propagation algorithm as training function, firefly algorithms and time series models suffer with slow convergence rates, high computational times and lack of recognizing the non-linear series respectively. Hence, to overcome these drawbacks, the present work proposes three novel approaches for forecasting the daily global solar radiation (DGSR) of 10 Indian cities. Simple Artificial Neural Network (ANN), ANN with forward unity gain and ANN with Regression Networks are considered in estimating the DGSR. The data set consisting of the minimum temperature , maximum temperature , average temperature , wind speed , relative humidity , precipitation , extraterrestrial radiation and sunshine hours are considered as inputs to the proposed approach. Statistical indicators like coefficient of determination , root mean square error , mean bias error and mean absolute percentage error are evaluated to determine the efficiencies of the proposed approaches. Results show that forecasting of DGSR is superior as compared to other approaches.

The increasing demand for electrical energy in Indonesia encourages the availability of a reliable, efficient and stable power distribution system, especially in maintaining voltage quality during the transmission to distribution process. One of the main challenges in the distribution system is the occurrence of voltage drops which if not controlled can reduce energy efficiency and damage electrical equipment. In overcoming these problems, this research applies the Artificial Neural Network (ANN) method based on the backpropagation algorithm as an alternative approach in analyzing and predicting voltage drops in power distribution networks. Simulations were conducted using MATLAB R2024b software with input variables in the form of voltage, power factor, conductor length and diameter, cable type resistance, reactance, and load current. Meanwhile, the output variable analyzed is voltage drop. The simulation results show that the ANN model has a high predictive ability indicated by the Mean Square Error (MSE) value of 0.012743, Root Mean Square Error (RMSE) of 0.11289, Mean Absolute Error (MAE) of 0.033586, and Mean Absolute Percentage Error (MAPE) of 6.4292 %. This low error value indicates that the ANN is able to recognize complex patterns between input variables and provide adaptive responses to various variations in distribution system conditions so that the application of ANN using the backpropagation algorithm proves effective and efficient as a tool in supporting the technical decision-making process.