Single Artificial Neural Network Model Research Articles

A Grain Size Profile Prediction Method Based on Combined Model of Extreme Gradient Boosting and Artificial Neural Network and Its Application in Sand Control Design

Summary The grain size distribution along the well depth is of great significance for the prediction of the physical properties and the staged sand control design of the unconsolidated or weakly consolidated sandstone reservoir. In this paper, a new method for predicting the formation median grain size profile based on the combination model of extreme gradient boosting (XGBoost) and artificial neural network (ANN) is proposed. The machine learning algorithm and weighted combination model are applied to the prediction and analysis of reservoir grain size. The prediction model is improved from two aspects: First, the feature engineering of the XGBoost-ANN model is constructed by using the data of multiple sampling points on the logging curve. Second, the prediction accuracy is improved by increasing the dimension of the prediction model, that is, the XGBoost and ANN single-prediction models are weighted by the error reciprocal method and a combined prediction model containing multidimensional information is established. The research results show that compared with the single-point mapping model, the prediction accuracy of the multipoint mapping model considering the vertical geological continuity of the reservoir is higher than that of the single-point prediction and the coefficient of determination in the testing set can be improved up to 14.5%. The influence of different weighting methods on prediction performance is studied, and the prediction performance of original XGBoost, ANN, and XGBoost-ANN combined models is compared. The combined prediction model has a higher prediction accuracy than the single XGBoost and ANN models with the same number of sampling points and the coefficient of determination can be improved by up to 16.5%. The prediction accuracy and generalization ability of the XGBoost-ANN combined model are evaluated comprehensively. The combined model is used to design layered sand control of a well in an adjacent block, and good results have been achieved in production practice. This study provides a new method with high accuracy and efficiency for the prediction of unconsolidated sand median grain size profile.

Evaluating the rapid chloride permeability of self-compacting concrete containing fly ash and silica fume exposed to different temperatures: An artificial intelligence framework

One of the major known challenges of improving durability of concrete structures is reducing the permeability of concrete to retard the transport of chloride ions. In this regard, the utilization of self-compacting concrete (SCC) incorporating supplementary cementitious materials such as fly ash and silica fume can aid to improve the resistance against chloride ion penetration. With the aim of evaluating the effectiveness of the SCCs, existing experimental methods, such as the rapid chloride permeability test (RCPT), demand time, financial resources, and skilled technicians. This study proposes three accurate and reliable predictive models through the combination of metaheuristic optimization techniques of particle swarm optimization (PSO), ant colony optimization (ACO) and biogeography-based optimization (BBO) with artificial neural network (ANN) in order to minimize the reliance on time-consuming and costly laboratory activities. To this end, 360 RCPT results on 60 SCC mixtures incorporating various percentages of fly ash and silica fume exposed to different temperatures during the RCPT were collected. The performance of the developed models was assessed by different statistical indicators. The results reveal that the proposed ANN-BBO model exhibits a higher accuracy than the single ANN, ANN-PSO and ANN-ACO models. It is also found that the input parameter of the sample temperature during testing is the most important variable in controlling the RCPT of SCC with a contribution of 25%. Comparing the performance of the best proposed model (ANN-BBO) with that of existing models in the literature reveals that the ANN-BBO model has a higher accuracy in predicting the chloride ion penetration resistance of SCCs.

Neural network based corrosion modeling of Stainless Steel 316L elbow using electric field mapping data

Stainless steel (SS) is widely employed in industrial applications that demand superior corrosion resistance. Modeling its corrosion behavior in common structural and various operational scenarios is beneficial to provide wall-thickness (WT) information, thus leading to a predictive asset integrity regime. In this spirit, an approach to model the corrosion behavior of SS 316L using artificial neural networks (ANNs) is developed, whereby saline water at different concentrations is flown through an elbow structure at different flow rates and salt concentrations. Voltage, current, and temperature data are recorded hourly using electric field mapping (EFM) pins installed on the elbow surface, which serve as training data for the ANNs. The performance of corrosion modeling is verified by comparing the predicted WT with actual measurements obtained from experimental tests. The results show the exceptional performance of the proposed single ANN model to predict WT. The error is calculated by comparing the estimated WT and actual measurement recorded, where the maximum error for each setting is range from 0.5363 to 0.7535%. RMSE and MAE values of each pin in every setting are also computed such that the maximum values of RMSE and MAE are 0.0271 and 0.0266, respectively. Moreover, a concise account of the observed scale formation is also reported. This comprehensive study contributes to a better understanding of SS 316L corrosion and offers valuable insights for developing efficient strategies to prevent corrosion in industrial environments. By accurately predicting WT loss using ANNs, this approach enables proactive maintenance planning, minimizing the risk of structural failures and ensuring the extended sustainability of industrial assets.

An Enhanced Feed-Forward Back Propagation Levenberg–Marquardt Algorithm for Suspended Sediment Yield Modeling

Rivers are dynamic geological agents on the earth which transport the weathered materials of the continent to the sea. Estimation of suspended sediment yield (SSY) is essential for management, planning, and designing in any river basin system. Estimation of SSY is critical due to its complex nonlinear processes, which are not captured by conventional regression methods. Rainfall, temperature, water discharge, SSY, rock type, relief, and catchment area data of 11 gauging stations were utilized to develop robust artificial intelligence (AI), similar to an artificial-neural-network (ANN)-based model for SSY prediction. The developed highly generalized global single ANN model using a large amount of data was applied at individual gauging stations for SSY prediction in the Mahanadi River basin, which is one of India’s largest peninsular rivers. It appeared that the proposed ANN model had the lowest root-mean-squared error (0.0089) and mean absolute error (0.0029) along with the highest coefficient of correlation (0.867) values among all comparative models (sediment rating curve and multiple linear regression). The ANN provided the best accuracy at Tikarapara among all stations. The ANN model was the most suitable substitute over other comparative models for SSY prediction. It was also noticed that the developed ANN model using the combined data of eleven stations performed better at Tikarapara than the other ANN which was developed using data from Tikarapara only. These approaches are suggested for SSY prediction in river basin systems due to their ease of implementation and better performance.

Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models

Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation.

Using Artificial Neural Network Application in Modeling the Mechanical Properties of Loading Position and Storage Duration of Pear Fruit

In the study, rupture energy values of Deveci and Abate Fetel pear fruits were predicted using artificial neural network (ANN). This research aimed to develop a simple, accurate, rapid, and economic model for harvest/post-harvest loss of efficiently predicting rupture energy values of Deveci and Abate Fetel pear fruits. The breaking energy of the pears was examined in terms of storage time and loading position. The experiments were carried out in two stages, with samples kept in cold storage immediately after harvest and 30 days later. Rupture energy values were estimated using four different single and multi-layer ANN models. Four different model results obtained using Levenberg–Marquardt, Scaled Conjugate Gradient, and resilient backpropagation training algorithms were compared with the calculated values. Statistical parameters such as R2, RMSE, MAE, and MSE were used to evaluate the performance of the methods. The best-performing model was obtained in network structure 5-1 that used three inputs: the highest R2 value (0.90) and the lowest square of the root error (0.018), and the MAE (0.093).

An ensemble approach to improve BPNN model precision for predicting compressive strength of high-performance concrete

The use of machine learning and deep learning models to predict the properties of concrete is an emerging field to investigate. The effectiveness of these model's predictions depends on several parameters such as data set quality and amount, model type, input and output correlations, and many more. The better the model accuracy, the broader its reach in real-world applications. Therefore, this work intends to develop a distinctive deep learning model based on an ensemble technique to forecast the compressive strength of high-performance concrete; consequently, predictions are more accurate and trustworthy. The Artificial Neural Network (ANN) model is applied in the study to construct an ensemble model. Furthermore, Particle Swarm Optimization (PSO) methodology is utilized to optimize the hyperparameters of the ensemble model. The ideal number of successively coupled ANN models and learning rate obtained through PSO are 3755 and 0.0951, respectively. The feature significance research has also been undertaken to capture the effect of components on the compressive strength of concrete. The present ensemble technique has increased the model accuracy by 41.04 % compared to the single ANN model. The Root Mean Squared Error (RMSE) value of the proposed ensemble model is 4.009, and the coefficient of determination (R2) value is determined to be 0.93. The feature importance study indicated that concrete age, cement content, and water content are the key factors that influence concrete strength. The suggested ensemble model may be applied to decline the laboratory dependence for finding concrete properties.

High accuracy prediction of Cu(II) cations removal from synthetic aqueous solutions with ANNs using normal and modified biosorbent

ABSTRACT This study aims to accurately predict Cu(II) removal efficiencies from synthetic aqueous solutions with Artificial Neural Networks (ANNs) using normal and modified Abies bornmulleriana cone. The predicted values of Cu(II) removal efficiencies for normal and modified Abies bornmulleriana cone were determined using a single ANNs model structure. In the present investigation, the neural network’s input parameters are biosorption time, biosorbate concentration, biosorbent amount, pH, particle size, agitation rate, and temperature. The removal efficiencies of the Cu (II) ions were selected as the experimental responses. The optimal network design is (7:7-9-2:2). Sensitivity analysis was employed to estimate the relative contribution of the input parameters to the ANN’s performance. The most critical parameter affecting the removal efficiencies of the Cu(II) was defined as biosorption time. The ANNs model for the normal and the modified Abies bornmulleriana cone had determination coefficients of (0.95, 0.98), standard deviation ratios of (0.22, 0.14), root mean square errors of (27.89, 26.02), and mean absolute errors of (2.21, 2.06), respectively. The outcomes from the network signify that the ANNs model could predict high accuracy Cu (II) removal efficiencies for both biosorbents.

Prediction of ignition delay times of Jet A-1/hydrogen fuel mixture using machine learning

To control the global warming trends, carbon footprint of all the human activities needs to be restricted, including the aviation industry. Mixing hydrogen with commercial kerosene jet fuels appears as a promising alternative fuel to reduce the carbon dioxide emissions of aviation engines. The addition of hydrogen can significantly impact the auto-ignition process of aviation fuels, which is a key ingredient of engine reliability. However, accurate calculations or measurements of ignition delay times (IDTs) over a wide range of pressures, temperatures and fuel blending ratios are complicated and time-consuming. To achieve real-time prediction of ignition delay time for hydrogen-blended jet fuels under various operating conditions, machine learning methods are introduced to build a data-driven proxy model in this work. First, the ignition delay times of Jet A-1/hydrogen fuel mixture are simulated using the well-known HyChem combustion reaction mechanism under different pressures, temperatures, equivalence ratios and blending molar ratios of hydrogen. After some validation against experimental results, an artificial neural network (ANN) model is trained using the database of ignition delay times. Furthermore, a sub-ANN is nested to the original ANN model as an improvement on certain local conditions. The results show that the single original ANN model leads to a large local relative error for IDT <103μs, generally referring to high temperatures and pressures conditions. With the help of the nested sub-ANN approach, the improved model achieves a significantly better accuracy for very fast ignition. Compared with other machine learning models such as random forest, the nested sub-ANN model is more efficient to predict the ignition delay times of Jet A-1/hydrogen fuel mixture, still mitigating computational cost. The sensitivity analysis shows how the nested sub-ANN model is less sensitive to the uncertainties of the input parameters than the original ANN model.

Capability assessment of conventional and data-driven models for prediction of suspended sediment load.

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6-3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6-3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312g/l, 65.15 and - 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.

Development of hybrid machine learning-based carbonation models with weighting function

Carbonation of concrete has significant influence on the service life of constructions and great effort has been made to establish an accurate and efficient model of carbonation that incorporates both internal and external factors. We present a hybrid machine learning (ML) approach that combined two single ML models: artificial neural network (ANN) and support vector machine (SVM). A literature survey generated a database containing 532 records of accelerated carbonation depth measurements for concrete mixtures inclusive of fly-ash blends. Six inputs comprising cement content, fly-ash replacement level, water–binder ratio (w/b), CO2 concentration, relative humidity and exposure time were selected for modeling, justified by grey relational analysis. The four ML models had excellent accuracy in predicting the carbonation depth of concrete, with the correlation coefficient ranging from 0.9788 to 0.9946, but the two hybrid ML models achieved superior performance to the single ANN and SVM models with characteristic higher correlation coefficients, lower mean value for the absolute error and lower standard deviation for its distribution. In addition, compared with other commonly known empirical carbonation models, the hybrid ML models showed more accurate carbonation depth prediction with smaller root mean square error. Furthermore, the weightings of the contributions of six selected factors to carbonation depth disclosed that CO2 concentration, w/b and binder content had higher relative importance to carbonation depth and should be given greater weighting in future carbonation model development.

Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine

Prediction of financial time series such as stock and stock indexes has remained the main focus of researchers because of its composite nature and instability in almost all of the developing and advanced countries. The main objective of this research work is to predict the direction movement of the daily stock prices index using the artificial neural network (ANN) and support vector machine (SVM). The datasets utilized in this study are the KSE-100 index of the Pakistan stock exchange, Korea composite stock price index (KOSPI), Nikkei 225 index of the Tokyo stock exchange, and Shenzhen stock exchange (SZSE) composite index for the last ten years that is from 2011 to 2020. To build the architect of a single layer ANN and SVM model with linear, radial basis function (RBF), and polynomial kernels, different technical indicators derived from the daily stock trading, such as closing, opening, daily high, and daily low prices and used as input layers. Since both the ANN and SVM models were used as classifiers; therefore, accuracy and F-score were used as performance metrics calculated from the confusion matrix. It can be concluded from the results that ANN performs better than SVM model in terms of accuracy and F-score to predict the direction movement of the KSE-100 index, KOSPI index, Nikkei 225 index, and SZSE composite index daily closing price movement.

Determination of a Renewable Energy System Capacity Factor Using Machining Learning Algorithms

Across the world, machining learning (ML) algorithms, such as support vector machines (SVR) and artificial neural networks (ANN), are among scientific tools for the fourth industrial revolution, or Industry 4.0, campaign. These algorithms have wide engineering applications, but their potentials in energy management are still evolving. Hence, this study investigates the performance of SVR and ANN algorithms as predictive models for wind turbines capacity factor (CF) estimation. Five independent parameters-wind speed, power density, turbulence intensity, installed capacity, and wind shear - were used as input parameters for this estimation problem. Polynomial, radial basis function (RBF), and linear kernels were used to train an SVR model that estimates CF, while Adams was used to optimize the performance of a backpropagation ANN model. These models’ applicability was evaluated using data sets from eight locations. This study used correlation coefficient used to compared the model performance. The RBF trained SVR model performed better than the other kernels during model training, but the linear kernel trained SVR model performed better than the other kernels during testing model. When these kernels performances were compared with a single hidden layer ANN model, the ANN model results were better than these kernels results.

Predicting Estimated Blood Loss and Transfusions in Gynecologic Surgery Using Artificial Neural Networks

This chapter explores valuating the efficacy of using artificial neural networks (ANNs) for predicting the estimated blood loss (EBL) and also transfusion requirements of myomectomy patients. All 146 myomectomy surgeries performed over a 6-year period from a single site are captured. Records were removed for various reasons, leaving 96 cases. Backpropagation and radial basis function ANN models were developed to predict EBL and perioperative transfusion needs along with a regression model. The single hidden layer backpropagation ANN models performed the best for both prediction problems. EBL was predicted on average within 127.33 ml of measured blood loss, and transfusions were predicted with 71.4% sensitivity and 85.4% specificity. A combined ANN ensemble model using the output of the EBL ANN as an input variable to the transfusion prediction ANN was developed and resulted in 100% sensitivity and 62.9% specificity. The preoperative identification of large EBL or transfusion need can assist caregivers in better planning for possible post-operative morbidity and mortality.

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks.

Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.

Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting

Drought prediction is an important subject, particularly in drought-hydrology, and has a key role in risk management, drought readiness and alleviation. Hydrological time series data consists of nonlinear features and various time scales. With this view in mind, this study has combined the strengths of the Wavelet transformation, Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to test a new method of a hybrid model for their ability to accurately predict future droughts. A 30-year rainfall data from the year 1986 to 2016 for Malaysia’s Langat River Basin was analyzed. Meteorological drought indices (DI) such as the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP) were used to compute historical drought events. At first, each of these computed drought time series went through a process of decomposition to be divided as low frequency and high-frequency sub-series by discrete wavelet transform (DWT). Secondly, the high and low-frequency sub-series were passed through the predictive model of ANN and ARIMA techniques, respectively. Lastly, the predicted sub-series were used to reconstruct and develop a final drought prediction model. It was found that the Wavelet-ARIMA-ANN (which named as W-2A) model outperformed the single ANN and wavelet-ANN predictive models. The ANN model developed by SPI achieved an overall correlation co-efficient R-value of 0.423, but the wavelet-based ANN model decreased in the R-value to 0.415. Finally, two different models, which were established using drought indices SPI and SIAP, and discrete wavelet transformation-based hybrid ANN-ARIMA (W-2A), have achieved improved R values of 0.914 and 0.934 respectively.

Coupling wavelet transform and artificial neural network for forecasting estuarine salinity

Accurate and reliable predictions of estuarine salinity can lead to more effective water resource management and can alleviate the adverse impacts of saltwater intrusion. Due to the nonlinear and nonstationary features in time series of estuarine salinity, this study is conducted to develop a hybrid model coupling the techniques of wavelet transform (WT) and artificial neural network (ANN) for forecasting estuarine salinity in the Pearl River Estuary, China. Two wavelet-based forecasting frameworks, the direct forecast (DF) framework (i.e., only explanatory variables are decomposed, leading to direct forecasting of the target variables) and multicomponent forecast (MF) framework (i.e., both explanatory and target variables are decomposed, and each target component is forecasted separately), were used to construct the WT-ANN models. The results reveal the superiority of hybrid WT-ANN models in estuarine salinity forecasting over traditional multiple linear regression (MLR) models and single ANN models, indicated by the Nash–Sutcliffe efficiency (NSE), correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE) and Kling–Gupta efficiency (KGE). Between the two wavelet-based forecasting paradigms, the MF framework outperformed the DF framework for better capturing the mutual dependence (i.e., mutual information, MI) between input and output variables. Additionally, the superiority of WT-ANN models in comparison with MLR and ANN models is increasingly prominent when the forecast lead time is extended from 1 to 3 days.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam.

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Prediction of perioperative transfusions using an artificial neural network.

BackgroundAccurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations.MethodsOver 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients.ResultsVarious ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70–80% specificity with a corresponding 75–62% sensitivity.ConclusionANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.

Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation

The present work aimed to examine the feasibility of using artificial neural network (ANN) based models to obtain accurate estimates of nitrate loads in river basins, which is an important parameter for water quality management. Both Single ANN (SANN) and Ensemble ANN (EANN) models were used to obtain the load estimations for five river basins in the Midwest United States. These basins included the Cuyahoga, Raisin, Sandusky, Muskingum, and Vermilion basins in Michigan and Ohio. Further, canonical correlation analysis (CCA) was applied to the ANN models to improve the performance. The k-fold cross-validation method was then utilized to evaluate the proposed models based on two statistical indices, namely, the rRMSE and rBAIS, and the estimates were compared for four different k values (k = 3, 5, 7, and 10). According to the results, the EANN model seemed to produce better load estimations than the SANN model, and the CCA based EANN model tended to produce the best estimates among all of the proposed models in this study. The box plot data for the rRMSE index were also investigated, and the plot results indicated that increasing values of k tended to generate better estimates. Thus, the use of k = 10 is recommended for load estimations since this value was associated with better performances and less biased estimates.