Performance Of Support Vector Machine Model Research Articles

Enhancing Machine Learning Model Performance in Addressing Class Imbalance

This research aims to investigate methods for handling class imbalance in machine learning models, with a focus on the Support Vector Machine (SVM) algorithm. We apply oversampling (SMOTE) and undersampling techniques to a dataset with class imbalance and evaluate the performance of SVM using these methods. Experiments are conducted using data from Twitter social media regarding the 2024 general electionsThe findings indicate that incorporating SMOTE effectively enhances the performance of SVM models, particularly within the SVM Polynomial variant. However, the use of undersampling shows limited impact on improving SVM model performance. This study provides valuable insights for researchers and practitioners in choosing the appropriate strategy for handling class imbalance in machine learning models.

Early identification of Parkinson's disease with anxiety based on combined clinical and MRI features.

To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety. A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model. 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model. We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying.

The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate − 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is − 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.

Competing endogenous RNA network characterization of lymph node metastases in Leuran gastric cancer subtypes.

Gastric cancer is a kind of tumor with strong heterogeneity. Long noncoding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors. In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. The performance of SVM model was good in the intestinal and diffuse cohort, while the model in the whole cohort was relatively poor. Some important co-expression patterns between immune cells and ceRNAs network indicated significant correlation CD70 with dendritic cells and so on. Our research inferred competing endogenous RNA network of lymph node metastasis and built an excellent predicting model.

Classification of Coal Bursting Liability Based on Support Vector Machine and Imbalanced Sample Set

As an inherent property of the accumulation of elastic energy and the sudden instability failure of coal, coal bursting liability (CBL) is the basis of the research on the early warning and prevention of coal burst. To accurately classify the CBL level, the support-vector-machine (SVM) method was introduced in this paper, and the dynamic failure time (DT), elastic energy index (WET), impact energy index (KE) and uniaxial compressive strength (RC) were selected as the classification indexes. An imbalanced sample set, containing 95 groups of measured data of CBL, was established, and eight SVM classification models were constructed, based on different kernel functions and swarm-intelligence-optimization algorithms. Focusing on the problem of sample imbalance, the classification accuracy, A, F1-score and kappa coefficient were used to comprehensively evaluate the classification performance of SVM models, and the grey-wolf-optimizer SVM (GWO-SVM) model was selected as the best model in this paper, reaching the highest accuracy of 98.9%. The GWO-SVM was applied to identify the CBL level of the 4# coal seam in Xiaozhuang Coal Mine and the 1# coal seam in the Wanfeng Coal Mine. The results of the engineering application are consistent with those from the engineering field, and show that the proposed model is scientific and practical, and can be a new method for CBL classification.

An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method

Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset.

Machine Learning Based on Diffusion Kurtosis Imaging Histogram Parameters for Glioma Grading.

Glioma grading plays an important role in surgical resection. We investigated the ability of different feature reduction methods in support vector machine (SVM)-based diffusion kurtosis imaging (DKI) histogram parameters to distinguish glioma grades. A total of 161 glioma patients who underwent magnetic resonance imaging (MRI) from January 2017 to January 2020 were included retrospectively. The patients were divided into low-grade (n = 61) and high-grade (n = 100) groups. Parametric DKI maps were derived, and 45 features from the DKI maps were extracted semi-automatically for analysis. Three feature selection methods [principal component analysis (PCA), recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO)] were used to establish the glioma grading model with an SVM classifier. To evaluate the performance of SVM models, the receiver operating characteristic (ROC) curves of SVM models for distinguishing glioma grades were compared with those of conventional statistical methods. The conventional ROC analysis showed that mean diffusivity (MD) variance, MD skewness and mean kurtosis (MK) C50 could effectively distinguish glioma grades, particularly MD variance. The highest classification distinguishing AUC was found using LASSO at 0.904 ± 0.069. In comparison, classification AUC by PCA was 0.866 ± 0.061, and 0.899 ± 0.079 by RFE. The SVM-PCA model with the lowest AUC among the SVM models was significantly better than the conventional ROC analysis (z = 1.947, p = 0.013). These findings demonstrate the superiority of DKI histogram parameters by LASSO analysis and SVM for distinguishing glioma grades.

Characteristic wavelengths optimization improved the predictive performance of near-infrared spectroscopy models for determination of aflatoxin B1 in maize

A neoteric measure for quantitative assay of Aflatoxin B1 (AFB1) in maize based on an optimized feature model of near-infrared (NIR) spectroscopy was proposed in the work. A portable near-infrared spectroscopy system constructed by the group was employed to collect maize samples with varying degrees of mildew. The variable selection methods of interval variable iterative space shrinkage approach (IVISSA), iterative retained information variable (IRIV), and particle swarm optimization combined moving window (PSO-CMW) were introduced to perform feature selection on the pretreatment NIR spectra. The characteristic wavelength variables after screening were used to constitute support vector machine (SVM) and partial least squares (PLS) test model respectively to implement the measurement of AFB1 in maize, and the detection performance of the two types of models was compared. The results obtained showed that the overall performances of SVM models were higher than that of PLS models, and the SVM model based on the characteristic wavelength variables optimized by the PSO-CMW method had the most prominent generalization performance. The root mean square error of prediction (RMSEP) of the model was 3.5967 μg kg−1, the coefficient of determination (RP2) was 0.9707, and the relative prediction deviation (RPD) was 5.7538. The overall results demonstrate that the optimized features of NIR spectra can realize the on-site quick testing of the AFB1 in maize with high precision by constructing a nonlinear SVM detection model. This investigation provides an original approach for speedy quantitative detection of mycotoxins in cereals.

Performance enhancement of SVM model using discrete wavelet transform for daily streamflow forecasting

Streamflow modeling becomes a vital task in any hydrological study for an improved planning and management of water resources. Soft computing and machine learning techniques are becoming popular day by day for their predictive capability when limited input data are available. In the present study, Support Vector Machine (SVM) technique is applied to forecast 1-day, 3-day, and 5-day ahead streamflow using daily streamflow time-series of Khanapur, Cholachguda, and Navalgund gauging stations in Malaprabha sub-basin located in the Karnataka state of India. Furthermore, Discrete Wavelet Transform is used as a data pre-processing method to evaluate the performance enhancement of SVM model, for which four different mother wavelet functions are used and tested separately, namely, Haar, Daubechies, Coiflets, and Symlets. Models are evaluated using coefficient of determination (R2), root-mean-square error, and Nash–Sutcliffe efficiency. The study indicates that the performance of SVM model improves considerably when wavelet method is coupled. It is found that the R2 values for Khanapur station using SVM are 0.91, 0.66, and 0.46 for 1-day, 3-day, and 5-day lead-time forecasts, respectively. However, when wavelet method is coupled with SVM model, the R2 is improved to 0.99, 0.73, and 0.68 for 1-day, 3-day, and 5-day lead-time forecasts, respectively.

Estimating leaf chlorophyll content by laser-induced fluorescence technology at different viewing zenith angles.

Leaf chlorophyll content (LCC) is a key indicator of a plant's physiological status. Fast and non-destructive monitoring of chlorophyll content in plants through remote sensing is very important for accurate diagnosis and assessment of plant growth. Through the use of laser-induced fluorescence (LIF) technology, this study aims to compare the predictive ability of different single fluorescence characteristic and fluorescence characteristic combinations at various viewing zenith angles (VZAs) combined with multivariate analysis algorithms, such as principal component analysis (PCA) and support vector machine (SVM), for estimating the LCC of plants. The SVM models of LCC estimation were proposed, and fluorescence characteristics-fluorescence peak (FP), fluorescence ratio (FR), PCA, and first-derivative (FD) parameter-and fluorescence characteristic combinations (FP+FR, FP+FD, FR+FD, FP+FR+FD) were used as input variables for the models. Experimental results demonstrated that the effect of single fluorescence characteristics on the predictive performance of SVM models was: FR>FD>FP>PCA. Compared with other models, 0° SVM was the optimal model for estimating LCC by higher R2. The fluorescence spectra and FD spectra observed at 0° and 30° were superior to those observed at 15°, 45°, and 60°. Thus, appropriate VZA must also be considered, as it can improve the accuracy of LCC monitoring. In addition, compared with single fluorescence characteristic, the FP+FR+FD was the optimal combination of fluorescence characteristics to estimate the LCC for the SVM model by higher R2, indicating better predictive performance. The experimental results show that the combination of LIF technology and multivariate analysis can be effectively used for LCC monitoring and has broad development prospects.

A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE–GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and γ of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE–GWO and MDE–GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE–GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m2 and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m2 and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m2 and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.

Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same data sets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient, and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient, and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage and become an important part of drug safety assessment.

Estimation of permeability of soil using easy measured soil parameters: assessing the artificial intelligence-based models

ABSTRACT In this study, the support vector machine (SVM) technique was used to estimate the permeability of the soil. The performance of SVM model was equated with another type of artificial intelligence techniques such as Gaussian process (GP), random forest (RF) and multi-linear regression (MLR)-based models. While a GP, RF, and MLR model gives a good estimation performance, the SVM model outperforms them. Two kernel functions, Pearson VII and radial basis kernel function, were used in SVM and GP regression models. For this study, a data set contaning 95 observations collected from laboratory experiments. Out of 95, a total of 66 data sets were selected for preparing different algorithms whereas rest 29 data sets were selected to test the models. Input variables include percentage of sand (S), percentage of fly ash (Fa), specific gravity (G), time (T) and head (H), whereas coefficient of permeability (k) is considered as output. A comparison was also done with the various study, which shows poor relationship between the values of permeability. Sensitivity analysis concludes that the parameter, time and water head are the most effective parameters for the estimation of permeability of soil.

Prediction of tram track gauge deviation using artificial neural network and support vector regression

ABSTRACTAlong with increase in tram traffic, gradual increase in gauge deviation is inevitable. Excessive track gauge is considered as an effective parameter in aggravating the quality of ride as well as the traffic safety of tram services. To ensure a smooth ride, reduce the risk of the train derailment and lower cost of maintenance, preventive maintenance operations are necessary which demand accurate track degradation prediction modelling. In this study, the Melbourne tram network is considered as a case study. Two types of rail tracks including straight segments and curved segments are studied. In order to develop models for gauge degradation prediction, two machine learning models including artificial neural network (ANN) and support vector machine (SVM) are applied. Two indexes including the coefficient of determination (R2) and mean squared error (MSE) are used to evaluate the performance of the proposed models. According to the results, both ANN and SVM models provide acceptable and somewhat similar outcomes. However, the performance of ANN models in predicting gauge deviation of straight segments is slightly better than SVM models. On the contrary, the performance of SVM models in predicting gauge deviation of curved segments is slightly better than ANN models.

An Effective ABC‐SVM Approach for Surface Roughness Prediction in Manufacturing Processes

It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data‐driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε‐insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well‐known evolutionary and swarm‐based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC‐SVM are better than DE‐SVM, GA‐SVM, and PSO‐SVM except three indicator values of DE‐SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.

Real-time rear-end crash potential prediction on freeways

This study develops new real-time freeway rear-end crash potential predictors using support vector machine (SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron (MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.

Prediction of egg storage time and yolk index based on electronic nose combined with chemometric methods

Egg storage time and yolk index, two descriptors of egg freshness, were evaluated by an electronic nose combined with chemometric methods. To obtain more useful information from collected data, the wavelet energy was extracted as feature signal by the wavelet transform method for qualitative and quantitative analysis. For qualitative analysis, linear discriminant analysis (LDA) was applied to evaluate the feature signals, and the result indicated that these feature signals had good classification performance with the first two scores explaining 82.50% of total variance. Moreover, probabilistic neural network (PNN) was performed to classify eggs with different storage times, and 92.86% of samples in testing set were classified correctly. For quantitative analysis, back propagation neural networks (BPNN) and support vector machine (SVM) were applied to build prediction models of yolk index, indicating that SVM models (R2 = 0.9641 in training set and R2 = 0.8339 in testing set) were better than BPNN (R2 = 0.8629 in training set and R2 = 0.7863 in testing set). To further improve the performance of SVM models, independent component analysis (ICA) and local linear embedding (LLE) were used to reduce dimension of feature data, and the results showed that ICA-SVM model had satisfying prediction performance (R2 > 0.97).

Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction

High complexity of water distribution systems’ (WDS) dynamics has convinced researchers to look for more sophisticated statistical approaches in urban water demand forecasting. Given the huge threat to major water reserves and ongoing draughts, water authorities are concerned with long term analysis of water demand to deal with uncertain future of this dynamic infrastructure. Researchers have tried a wide range of modelling techniques to propose an accurate model. However, applications of machine learning techniques are yet to be explored in detail. This research proposes a support vector machine (SVM) model, using polynomial kernel function to forecast monthly water demand of City of Kelowna (CKD), Canada. The prime objective of this research is to assess the use of phase space reconstruction prior to design of models’ input variables combinations. Results of this study proved optimum lag time of the input variables can significantly improve the performance of SVM models.

Application of machine-learning models for diagnosing health hazard of nitrate toxicity in shallow aquifers

There is a growing concern about health hazards linked to nitrate (NO3) toxicity in groundwater due to overuse of nitrogen fertilizers in rice production systems of northern Iran. Simple-cost-effective methods for quick and reliable prediction of NO3 contamination in groundwater of such agricultural systems can ensure sustainable rural development. Using 10-year time series data, the capability of adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) models as well as six geostatistical models was assessed for predicting NO3 concentration in groundwater and its noncarcinogenic health risk. The dataset comprised 9360 water samples representing 26 different wells monitored for 10 years. The best predictions were found by SVM models which decreased prediction errors by 42–73 % compared with other models. However, using well locations and sampling date as input parameters led to the best performance of SVM model for predicting NO3 with RMSE = 4.75–8.19 mg l−1 and MBE = 3.3–5.2 mg l−1. ANFIS models ranked next with RMSE = 8.19–25.1 mg l−1 and MBE = 5.2–13.2 mg l−1 while geostatistical models led to the worst results. The created raster maps with SVM models showed that NO3 concentration in 38–97 % of the study area usually exceeded the human-affected limit of 13 mg l−1 during different seasons. Generally, risk probability went beyond 90 % except for winter when groundwater quality was safe from nitrate viewpoint. Noncarcinogenic risk exceeded the unity in about 1.13 and 6.82 % of the study area in spring and summer, respectively, indicating that long-term use of groundwater poses a significant health risk to local resident. Based on the results, SVM models were suitable tools to identify nitrate-polluted regions in the study area. Also, paddy fields were the principal source of nitrate contamination of groundwater mainly due to unmanaged agricultural activities emphasizing the importance of proper management of paddy fields since a considerable land in the world is devoted to rice cultivation.