Investigating the Relationship between Soil Gas Radon and Soil Permeability by Using Artificial Neural Networks

<p>Uncertainty Analysis of Artificial Neural Network (ANN) And Support Vector Machine (SVM) Models in Predicting Monthly River Flow (Case Study: Ghezelozan River)</p>

As monitoring of spray drift during application can be expensive, time-consuming, and labor-intensive, drift predicting models may provide a practical complement. Several mechanistic models have been developed as drift prediction tool for various types of application equipment. Nevertheless, mechanistic models are quite often intricate and complex with a large number of input parameters required. Quite often, the detailed data needed for such models are not readily available. In this study, two advanced machine learning models (artificial neural network (ANN) and support vector regression (SVR)) were developed for pesticide drift prediction and compared with three conventional regression-based models: multiple linear regression (MLR), generalized linear model (GLM), and generalized nonlinear least squares (GNLS). The models were evaluated in fivefold cross-validation and by external validation using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute bias (MAB). From regression-based models, GLM and GNLS models performed very well when evaluated by cross-validation with R2 = 0.96 and 0.95 and RMSE = 0.70 and 0.82 respectively, while MLR performed less with R2 of 0.65 and RMSE of 2.25. Simultaneously, ANN and SVR models performed very well with R2 = 0.98 and 0.97 and RMSE = 0.58 and 0.71 respectively. Overall, ANN model performed best compared to the other four models followed by SVR. A comparison was also made between the high-performing model, ANN, and two previously published empirical models. The ANN model outperformed the two previously published empirical models and can be used to predict pesticide drift. Therefore, the ANN model is a potentially promising new approach for predicting ground drift that merits further study. In conclusion, our work demonstrated that the new approach, ANN and SVR-based models, for pesticide drift modeling has better predictive power than conventional regression models. Their ability to model complex relationships is a clear benefit in pesticide drift modeling where the variability in pesticide drift is often affected by a number of variables and the relationships between drift and predictors are very complicated. We believe such insights will pave better way for the application of machine learning towards spray drift modeling.

The competent and efficient utilization of feedstocks is highly essential in the transesterification of biodiesel from sardine fish oil. The identification of optimal reaction parameters is of high importance to maximize the yield of biodiesel produced from sardine fish oil at low cost. Application of ultrasonic energy-assisted biodiesel production from sardine fish oil catalyzed by KOH catalyst has been studied under different conditions. Response surface methodology (RSM) based on central composite rotatable design (CCRD) was employed to optimize the three important process parameters: methanol/oil molar ratio (X1), KOH catalyst concentration (X2), and reaction time (X3) for transesterification of sardine fish oil using ultrasonic energy. Artificial neural network (ANN) models with two feed-forward back-propagation neural network architecture, multilayer perceptron networks and radial basis function networks have been developed to obtain a good correlation between the input variables responsible for the input reaction parameters and the output parameter yield of fatty acid methyl ester (FAME) from sardine fish oil to biodiesel. The developed ANN models were trained and tested with the experimental data obtained from the RSM–CCRD method. The developed ANN models’ performances were compared with experimental data and were statistically compared by the coefficient of determination (R2), root-mean-square error, and mean absolute error. From the statistical analysis, it was found that the estimated yield of FAME from both RSM and ANN models was able to predict the FAME yield, and the results showed that the ANN model is much more accurate in the prediction of FAME yield as compared to the RSM model.

In this study, an artificial neural network (ANN) model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010). Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin). The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm). Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 (~98%) was obtained with root mean square error (RMSE) of 0.852 MJ/m2 , mean square error (MSE) of 0.725 MJ/m2 , mean absolute bias error (MABE) 10.659MJ/m2 , and mean absolute percentage error (MAPE) of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions.

Prediction of wind drift and evaporation losses from sprinkler irrigation using neural network and multiple regression techniques

Prediction of performance parameters in friction stir processing using ANN and multiple regression models

Australian rainfall is highly variable in nature and largely influenced by the several large scale remote climate drivers. Several past studies tried to establish the relationships between climate predictors (El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM)) and rainfalls over Australia. However, the relationship between climate predictors and South Australian rainfall is still unclear. Most of the past studies in this region have been carried out based on the individual and concurrent relationship of climate drivers with rainfall. Moreover, the combined relationship considering lagged-time effects of multiple climate predictors has not previously been attempted in South Australia. This paper presents the application of linear Multiple Regression (MR) analysis and non-linear Artificial Neural Networks (ANN) modelling to forecast long-term seasonal rainfall in South Australia using the potential climate predictors. A rainfall station in South Australia was chosen as case study to broadly explore this present hypothesis. The use of combined lagged ENSO-IOD-SAM climate input sets for calibrating and validating the ANN and MR Models was proposed to investigate the effect of past values of these major climate modes on long-term spring rainfall. The ANN model was developed in the form of multilayer perceptron using Levenberg-Marquardt algorithm. Early stopping techniques were used to analyze the improvement in the network's generalization ability. Both the MR and ANN modelling were assessed statistically using root mean square error (RMSE), Pearson correlation (R) and Willmott index of agreement (d). Finally the superiority of rainfall predictability methods was established by comparing the both linear and non-linear techniques. The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed poor generalization ability than non-linear ANN models. This study found that predicting spring rainfall using combined lagged ENSO-DMI-SAM climate indices with ANN can achieve better correlation as compared to multiple regressions. The study discovered that lagged DMI-SAM combined climate model has more effect on spring rainfall predictability than other combinations of climate model. It was observed that ANN modelling is able to provide higher correlations using the lagged-indices to forecast spring rainfall in compared to linear methods. Using the combination of DMI-SAM dual climate indices in an ANN model increased the model correlation up to 87%, 76% and 37% for the three combined climate predictor's models in forecasting South Australian spring rainfall. Whereas, those rainfall predictability was 52%, 49% and 18% respectively in case of linear MR modelling. The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggested the potentials of non-linear artificial intelligence techniques (ANN) over linear MR models for rainfall forecasting using large scale climate modes. This method can be used for other parts of the world where a relationship exists between rainfall and large scale climate modes which could not be established by linear methods.

The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions

• We modelled rainfall using multiple regression and Artificial Neural Networks for Victoria, Australia. • We examined combined lagged months of ENSO and IOD as potential predictors of spring rainfall. • Spring rainfall was predicted three years in advance with the developed models using ENSO–IOD lagged months. • ANN models proved to be more accurate than multiple regression models in terms of long term predictions. In this study, the application of Artificial Neural Networks (ANN) and Multiple regression analysis (MR) to forecast long-term seasonal spring rainfall in Victoria, Australia was investigated using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential predictors. The use of dual (combined lagged ENSO–IOD) input sets for calibrating and validating ANN and MR Models is proposed to investigate the simultaneous effect of past values of these two major climate modes on long-term spring rainfall prediction. The MR models that did not violate the limits of statistical significance and multicollinearity were selected for future spring rainfall forecast. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm. Both MR and ANN modelling were assessed statistically using mean square error (MSE), mean absolute error (MAE), Pearson correlation ( r ) and Willmott index of agreement ( d ). The developed MR and ANN models were tested on out-of-sample test sets; the MR models showed very poor generalisation ability for east Victoria with correlation coefficients of −0.99 to −0.90 compared to ANN with correlation coefficients of 0.42–0.93; ANN models also showed better generalisation ability for central and west Victoria with correlation coefficients of 0.68–0.85 and 0.58–0.97 respectively. The ability of multiple regression models to forecast out-of-sample sets is compatible with ANN for Daylesford in central Victoria and Kaniva in west Victoria ( r = 0.92 and 0.67 respectively). The errors of the testing sets for ANN models are generally lower compared to multiple regression models. The statistical analysis suggest the potential of ANN over MR models for rainfall forecasting using large scale climate modes.

Solvent selection and Controlling of operating parameters play a crucial role in batch cooling crystallization process. Choosing a best solvent for crystallization process involves more experimentation and time. To overcome this problem, an Artificial Neural Network (ANN) model technique is used to predict the carbamazepine form Ⅲ solubility by considering the thermodynamic properties of different solvents i.e. critical temperature, critical pressure, temperature, molecular weight, and acentric factor. The ANN model was trained and evaluated for solubility at various input data sets using experimental solubility data available in the literature. The ANN model with 20 hidden neurons has given the R2 value of 0.9943 which shows that the developed ANN model can be used for the selection of best solvent for batch crystallization process. Further, to determine the optimal cooling profile of batch cooling crystallization process, a multi-objective optimization problem is formulated by considering objectives as minimizing the coefficient of variation (CV) and maximizing the Number mean size (NMS) of crystals subjected to population balance equations using “method of moments” technique. Two types of temperature strategies i.e., piece-wise constant and piece-wise linear are developed and solved using NSGA-Ⅱ dynamic optimization procedure. The optimal NMS value attained through piece-wise linear strategy was 197.1 µm. This value has been increased by 28.3 µm from the nominal case (without optimization) and the coefficient of variation has decreased from 0.951 to 0.76. Further, optimal NMS value attained through piece-wise constant strategy was 205 µm. The value has been increased by 36.2 µm and the coefficient of variation has decreased from 0.951 to 0.73. This proves that the crystal attributes can be improved by optimal cooling temperature profile obtained by multi-objective optimization framework. For implementing the optimal cooling profile an advanced model-based control, i.e., Generic Model Control (GMC) was developed. It was observed that the GMC controller has the good tracking profile with no offset with/without disturbances and small value of root mean square error (RMSE) of 0.0016 using piece-wise constant as set point temperature. Using piece-wise linear as set point temperature, the RMSE value was 0.0018. In particular, it is advantageous to operate the batch cooling crystallization process with piece-wise linear strategy for set point trajectory tracking problems.

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

A single hidden layer Artificial Neural Network (ANN) model was developed to estimate a machinery energy ratio (MER) indicator, used to characterize and assess mechanization status of potato farms in Iran with a view point of energy expenditure in farm machinery. A wide range of variables of farming activities were examined. Initially, 90 attributes were used as input variables to predict desired MER output. Using regression analysis, 13 inputs were finally selected to model MER. Performance of developed ANN model was evaluated with various statistical measures including the coefficient of determination (R2), mean absolute percentage error (MAPE), mean squared error (MSE) and mean absolute error (MAE). The optimum ANN model had a 13 - 4 - 1 configuration. The values of the optimum model’s outputs correlated well, with R2 of 0.98. Value of MAPE calculated as 0.0001 for best ANN model, which indicate superiority of this model over other prediction models. Sensitivity analyses were also conducted to investigate the effects of each input item on the output value. Since the ANN model can predict this mechanization indicator for a target farming system in Hamadan province of Iran, it could be a good estimator for appraising mechanization of other regional farms. Also it overcomes some of the limitations of using simple data available from local databases as inputs that may contain errors. Key words: Potato, agricultural mechanization, machinery energy ratio, Artificial Neural Network.

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., sodium adsorption ratio (SAR), percent sodium (%Na) residual, Kelly’s ratio (KR), and residual sodium carbonate (RSC) in the groundwater of the Pratapgarh district of Southern Rajasthan, India. This study focuses on verifying the suitability of water for irrigational purpose, wherein more groundwater decline coupled with water quality problems compared to the other areas are observed. The southern part of the Rajasthan State is more populated as compared to the rest of the parts. The southern part of the Rajasthan is more populated as compared to the rest of the Rajasthan, which leads to the industrialization, urbanization, and evolutionary changes in the agricultural production in the southern region. Therefore, it is necessary to propose innovative methods for analyzing and predicting the water quality (WQ) for agricultural use. The study aims to develop an optimized artificial neural network (ANN) model to predict the sodium hazardness of groundwater for irrigation purposes. The ANN model was developed using ‘nntool’ in MATLAB software. The ANN model was trained and validated for ten years (2010–2020) of water quality data. An L-M 3-layer back propagation technique was adopted in ANN architecture to develop a reliable and accurate model for predicting the suitability of groundwater for irrigation. Furthermore, statistical performance indicators, such as RMSE, IA, R, and MBE, were used to check the consistency of ANN prediction results. The developed ANN model, i.e., ANN4 (3-12-1), ANN4 (4-15-1), ANN1 (4-5-1), and ANN4 (3-12-1), were found best suited for SAR, %Na, RSC, and KR water quality indicators for the Pratapgarh district. The performance analysis of the developed model (3-12-1) led to a correlation coefficient = 1, IA = 1, RMS = 0.14, and MBE = 0.0050. Hence, the proposed model provides a satisfactory match to the empirically generated datasets in the observed wells. This development of water quality modeling using an ANN model may help to useful for the planning of sustainable management and groundwater resources with crop suitability plans as per water quality.

Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries

This article compares two modeling approaches for optimal operation of a turbo chiller installed in an office building: (1) a machine learning model developed with artificial neural network (ANN) and (2) a hybrid machine learning model developed with the ANN model and available physical knowledge of the chiller. Before developing the ANN model of the chiller, the authors used Gaussian mixture model in order to check the validity of measured data. Then, the hybrid model was developed by combining the ANN model and physics-based regression equations from the EnergyPlus engineering reference. It was found that both the ANN and hybrid ANN model are satisfactory to predict the chiller’s power consumption: mean bias error (MBE) = −2.63%, coefficient of variation of the root mean square error (CVRMSE) = 8.05% by the ANN model; MBE = −3.99%, CVRMSE = 11.98% by the hybrid ANN model. However, the hybrid model requires fewer inputs (four inputs) than the ANN model (eight inputs). The energy savings of both models are similar coefficient of performance (COP) = 4.32 by the optimal operation of the ANN model; COP = 4.44 by the optimal operation of the hybrid ANN model. In addition, the hybrid ANN model can be applied where the ANN model is unable to provide accurate predictions.