Random Forest Classification Model Research Articles

Autism Spectrum Disorder Prediction in Children Using Machine Learning

Life symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.

MOBFinder: a tool for mobilization typing of plasmid metagenomic fragments based on a language model.

Mobilization typing (MOB) is a classification scheme for plasmid genomes based on their relaxase gene. The host ranges of plasmids of different MOB categories are diverse, and MOB is crucial for investigating plasmid mobilization, especially the transmission of resistance genes and virulence factors. However, MOB typing of plasmid metagenomic data is challenging due to the highly fragmented characteristics of metagenomic contigs. We developed MOBFinder, an 11-class classifier, for categorizing plasmid fragments into 10 MOB types and a nonmobilizable category. We first performed MOB typing to classify complete plasmid genomes according to relaxase information and then constructed an artificial benchmark dataset of plasmid metagenomic fragments (PMFs) from those complete plasmid genomes whose MOB types are well annotated. Next, based on natural language models, we used word vectors to characterize the PMFs. Several random forest classification models were trained and integrated to predict fragments of different lengths. Evaluating the tool using the benchmark dataset, we found that MOBFinder outperforms previous tools such as MOBscan and MOB-suite, with an overall accuracy approximately 59% higher than that of MOB-suite. Moreover, the balanced accuracy, harmonic mean, and F1-score reached up to 99% for some MOB types. When applied to a cohort of patients with type 2 diabetes (T2D), MOBFinder offered insights suggesting that the MOBF type plasmid, which is widely present in Escherichia and Klebsiella, and the MOBQ type plasmid might accelerate antibiotic resistance transmission in patients with T2D. To the best of our knowledge, MOBFinder is the first tool for MOB typing of PMFs. The tool is freely available at https://github.com/FengTaoSMU/MOBFinder.

Transferability analysis of user groups in travel behaviour surveys using a random forest classification model

There are many different travel behavior surveys with a wide range of sample sizes and contents, whose data sets are often difficult to merge. This contribution aims to evaluate the possibilities of transferring user typologies from one travel survey to another using a random forest classification model. By incorporating the parameters of usage frequencies, socio-demographics and mobility resources, the classification achieved a mean F1 score of 0.93. In conclusion, it can be shown that the proposed method is suitable for transferring mobility types developed on the basis of a specialized data set to other surveys using mobility behavior parameters.

Detecting Forest Fires in Southwest China From Remote Sensing Nighttime Lights Using the Random Forest Classification Model

Detecting Forest Fires in Southwest China From Remote Sensing Nighttime Lights Using the Random Forest Classification Model

Structure-activity relationship study of anti-wear additives in rapeseed oil based on machine learning and logistic regression.

Anti-wear performance is a crucial quality of lubricants, and it is important to conduct research into the structure-activity relationship of anti-wear additives in bio-based lubricants. These lubricants are eco-friendly and energy-efficient. A literature review resulted in the construction of a dataset comprising 779 anti-wear properties of 79 anti-wear additives in rapeseed oil, at various loadings and additive levels. The anti-wear additives were classified into six groups, including phosphoric acid, formate esters, borate esters, thiazoles, triazine derivatives, and thiophene. Logistic regression analysis revealed that the quantity and kind of anti-wear agents had significant effects on the anti-wear properties of rapeseed oil, with phosphoric acid being the most effective and thiophene being the least effective. To identify the specific structural data that affect the anti-wear capabilities of additives in bio-based lubricants of rapeseed oil, a random forest classification model was developed. The results showed a 0.964 accuracy (ACC) and a 0.931 Matthews Correlation Coefficient (MCC) on the test set. The ranking of importance and characterization of MACCS descriptors in the model confirms that anti-wear additives with chemical structures containing P, O, N, S and heterocyclic groups, along with more than two methyl groups, improve the anti-wear performance of rapeseed oil. The application of data analysis and machine learning to investigate the classifications and structural characteristics of anti-wear additives in rapeseed oil provides data references and guiding principles for designing anti-wear additives in bio-based lubricants.

Stock Price Co-movement Prediction Based on Stock Market Technique Indicators

In the research of financial market analysis, the correlation research between different companies’ stock prices has important theoretical value and practical significance, but there is still a lack of unified standards for the construction and selection of technical indicators. In this paper, a comprehensive review of technique indicator for stock price correlation is designed and the stock price correlation research is carried out. Based on the calculation method of indicators and the meaning of indicators, this paper designs a stock price correlation indicator system. Various related indicators are extracted from the actual market data, combined with the time series clustering model K-Shape and random forest classification model. This paper tries to describe the synchronous correlation of stock prices among listed companies and further analyzes the mechanism leading to these correlations. By using the correlation indicator classification system proposed in this paper, the synchronous correlation relationship of stock prices of different companies is analyzed, and the synchronous correlation relationship of stock prices of corresponding companies in the future can be predicted, and the synchronous correlation relationship between listed companies can be analyzed and explained in practice to a certain extent.

Precision in Progress: Leveraging Data Mining Technique to Empower Career Path Selection for Incoming Senior High School Students

This research investigated the application of data mining, specifically the Random Forest classification model, to optimize career path selection for incoming Senior High School students in the Philippines. Given the diverse tracks and strands within the SHS program, the traditional decision-making process relies on anecdotal advice, limited exposure, and personal perceptions, often resulting in sub optimal choices. Focused on addressing the complexities introduced by the K-12 educational reform, the study analyzed the data of 1,020 students from three public schools including the Sibsib National High School. The Random Forest model achieved high accuracy (91.2%) and precision (72.6%), with critical attributes identified as Career Prospects, Personal Interests or Skills, and the Monthly Salary Bracket of Parents. While the model excelled overall, there is room for improvement in predicting certain academic tracks, particularly Humanities and Social Sciences (HUMSS). The study recommends refining the model, emphasizing enhancements for specific tracks and continual updates to accommodate evolving student data patterns.

Building a Binary Classification Machine-Learning Model: A Guide to Predicting Participation in a Lyme Disease Program at a Medical Institute.

The field of data analysis, preparation, and machine learning is rapidly expanding, offering numerous libraries and resources for exploration. Researchers gain knowledge through various channels, but few resources provide a comprehensive framework for building machine-learning models. We present a step-by-step framework for constructing a robust Random Forest classification model to fill this gap. Using the trained model, we predict if individuals visiting Sanoviv Medical Institute between 2020 and 2023 participated in the Lyme disease program based on age, symptoms, blood count, and chemistry results. While not exhaustive, the methods in each step provide a valuable starting point for researchers, promoting an understanding of the fundamental approach to model creation. The framework encourages researchers to explore beyond the outlined techniques, fostering innovation and experimentation.

Clustering analysis for classifying fake real estate listings.

With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

Tuberculosis (TB) is the second most deadly infectious disease globally, posing a significant burden in Brazil and its Amazonian region. This study focused on the "riverine municipalities" and hypothesizes the presence of TB clusters in the area. Wealso aimed to train a machine learning model to differentiate municipalities classified as hot spots vs. non-hot spots using disease surveillance variables as predictors. Data regarding the incidence of TB from 2019 to 2022 in the riverine town was collected from the Brazilian Health Ministry Informatics Department. Moran's I was used to assess global spatial autocorrelation, while the Getis-Ord GI* method was employed to detect high and low-incidence clusters. A Random Forest machine-learning model was trained using surveillance variables related to TB cases to predict hot spots among non-hot spot municipalities. Our analysis revealed distinct geographical clusters with high and low TB incidence following a west-to-east distribution pattern. The Random Forest Classification model utilizes six surveillance variables to predict hot vs. non-hot spots. The machine learning model achieved an Area Under the Receiver Operator Curve (AUC-ROC) of 0.81. Municipalities with higher percentages of recurrent cases, deaths due to TB, antibiotic regimen changes, percentage of new cases, and cases with smoking history were the best predictors of hot spots. This prediction method can be leveraged to identify the municipalities at the highest risk of being hot spots for the disease, aiding policymakers with an evidenced-based tool to direct resource allocation for disease control in the riverine municipalities.

2000–2020年海南岛天然橡胶人工林分布变化数据集

Natural rubber plantations are one of the primary forest ecosystems on Hainan Island and serve as a burgeoning focal point for ecosystem services in the Tropics. Therefore, it is crucial for regional ecological environment and economic development to accurately monitor and map the spatial and temporal pattern of rubber plantations. Employing Landsat TM/OLI image data acquired through the Google Earth Engine (GEE) cloud platform spanning from 1998 to 2022 in Hainan Island, coupled with information from the Third National Land Survey Report, we obtained a dataset of distribution changes of natural rubber plantations in Hainan Island from 2000 to 2020, by using a random forest classification model. The dataset demonstrates a relatively high accuracy, with an overall accuracy of 96.93%; Specifics include a producer's accuracy of 89.10% and a user's accuracy of 94.72%. The rubber plantation area in Hainan Island has exhibited a growing trend, increasing from 3,661.65 km2 in 2000 to 7,422.02 km2 in 2020. This dataset can provide fundamental support for monitoring, managing, and decision-making concerning rubber plantation ecosystems.

Integration of Unmanned Aerial Vehicle Imagery and Machine Learning Technology to Map the Distribution of Conifer and Broadleaf Canopy Cover in Uneven-Aged Mixed Forests

Uneven-aged mixed forests have been recognized as important contributors to biodiversity conservation, ecological stability, carbon sequestration, the provisioning of ecosystem services, and sustainable timber production. Recently, numerous studies have demonstrated the applicability of integrating remote sensing datasets with machine learning for forest management purposes, such as forest type classification and the identification of individual trees. However, studies focusing on the integration of unmanned aerial vehicle (UAV) datasets with machine learning for mapping of tree species groups in uneven-aged mixed forests remain limited. Thus, this study explored the feasibility of integrating UAV imagery with semantic segmentation-based machine learning classification algorithms to describe conifer and broadleaf species canopies in uneven-aged mixed forests. The study was conducted in two sub-compartments of the University of Tokyo Hokkaido Forest in northern Japan. We analyzed UAV images using the semantic-segmentation based U-Net and random forest (RF) classification models. The results indicate that the integration of UAV imagery with the U-Net model generated reliable conifer and broadleaf canopy cover classification maps in both sub-compartments, while the RF model often failed to distinguish conifer crowns. Moreover, our findings demonstrate the potential of this method to detect dominant tree species groups in uneven-aged mixed forests.

Protecting Genetic Genealogical Databases from Identical-by-State Probing Attacks: A Machine Learning-Based Approach

Identical-by-state (IBS) probing is a way of attacking a public genealogy database to discover the identities of people who share specific qualities. The attacker creates an IBS-inert DNA sequence (IBS-inert-DNA-sequence) and combines it with a sequence containing the trait of interest. The final sequence matches people with genomic areas similar to the trait. To prevent attacks, it was hypothesized that the design of IBS-inert-DNA-sequences is based on the principle that they are susceptible to detection by skilled machine learning systems, because the attacker purposely creates an IBS-inert-DNA-sequence which is structurally dissimilar to real DNA sequences. The dataset consisted of real DNA (from the UCI Machine Learning Repository's splice junction gene sequences dataset) and computer-generated sequences. Eighteen non-identical Random Forest classifier models (RF-models) were created to determine the best configurations for discriminating between real and computer-generated sequences. The findings revealed that an optimized RF-model combined with k-mer and n-gram values of 2 each, resulted in the most performant model, with accuracy, sensitivity, specificity, false positive rate, Matthew’s correlation coefficient, and area under the receiver operating characteristic curve values of 88.3%, 84.8%, 91.8%, 8.2%, 0.768 and 0.958, respectively. A decline in performance was linked to an increase in k-mer size.

Alzheimer’s disease‐driven microbiome dysbiosis is a strong predictor of cognitive performance in humans

AbstractBackgroundThe association between Alzheimer’s Disease (AD) and dysbiosis of the gastrointestinal (GI) microbiome has been demonstrated in several studies. Here, we aimed to expand on our previous findings that in nursing home residents the microbiome and clinical variables are predictive of AD compared to no dementia (ND) by expanding our study population to include those residing in the general community. We sought to identity microbiota characteristics that stratify AD from ND individuals across environments. Further, in our community residing population, we assessed the longitudinal microbiome’s ability to predict cognitive performance (ADAS‐Cog‐13).MethodOlder individuals residing in Massachusetts nursing homes (n = 79) or in the general community (n = 71) were enrolled in our study. At each visit, fecal samples and clinical variables were collected. For those residing in the community, ADAS‐Cog‐13 testing was done. Metagenomic profiling was performed on longitudinal fecal samples. Random forest classification models were used to identify associations between residency, AD diagnosis and microbial features and metabolic pathways. Mixed‐effect random forest regression models were created to identify metagenome features informative of cognitive performance.ResultAmong AD and ND individuals residing in both the NH and community, microbial abundance and basic clinical variables were informative of AD status (F1‐score = 0.827±0.03), with Clostrida, Blautia and Neglecta species associating with AD diagnosis. In specifically the community residing individuals, increase in Bacteroidetes (F1‐score = 0.906 ± 0.03) and pathways (F1‐score = 0.885±0.03) involved in amino acid catabolism and nucleotide synthesis was informative of AD diagnosis. Additionally, mixed‐effect random forest machine learning models determined that variability in the abundance of these bacteria and pathways accurately predicts the cognitive performance (ADAS‐Cog‐13) in community residing individuals.ConclusionTaken together our study describes that the AD microbiome from both nursing home and community‐dwelling older adults is depleted in health‐associated Clostridia and that the abundance of these bacteria and their metabolites not only optimally discriminates older human according to disease status, but it also accurately reflects their cognitive ability.

Predictive Modeling Leveraging Electronic Health Records Identifies Early Predictors of Alzheimer’s Disease

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disorder of aging that is difficult to diagnose. Electronic health records (EHR) are a valuable source of longitudinal data for disease phenotyping and prediction. Knowledge graphs, such as SPOKE (Scalable Precision Medicine Open Knowledge Engine), help derive biological meaning from phenotypic data. Here we utilize machine learning on EHR to predict and better understand AD.MethodAD patients and controls were identified from the UCSF EHR. The index time was identified as the first dementia diagnosis or drug record among the AD patients, and 1 year before the last visit among controls. Controls matched on demographics and visit‐related factors were identified at five time points relative to the index time (‐7years, ‐5years, ‐3years, ‐1year, and ‐1day) at a 1:8 ratio. Random forest (RF) classification models for AD were trained utilizing clinical data (conditions, drugs, abnormal measurements) and evaluated with bootstrapped AUROCs on a 30% held‐out set. Top features mapped to phecodes were ranked based on average impurity decrease. Networks were created from SPOKE knowledge graph by identifying shortest paths between top 25 model features and AD, and associations were validated with genetic colocalization analysis on shared variants in the UK Biobank.ResultFrom the UCSF EHR, 749 AD patients and 250,545 Controls were identified with clinical concepts 7 years prior to each individual’s index time. RF trained on AD and matched control individuals’ data performed with AUROC of 0.58 (‐7years) to 0.77 (‐1day). Top phecode features from models include those that are important earlier (osteoarthritis, allergic rhinitis), progress in importance closer to index time (dizziness, vitamin D deficiency, memory loss), and persist in importance throughout time (hyperlipidemia) (Figure 1). Knowledge networks highlight shared biological relationships between top features (e.g., hyperlipidemia, osteoporosis), including identification of relevant genes (e.g., APOE, PSEN 1/2, INS, AKT1, HFE) (Figure 2). Colocalization analysis of LDL cholesterol and AD confirms a commonly associated SNP in APOE.ConclusionEHR together with knowledge networks provide an opportunity to identify early clinical predictors of AD diagnosis, with utility for early intervention, hypothesis generation and biological insight.

Predicting the Distribution of Mangrove Forests through Random Forest Algorithm by Combining Spectral Features and DEM Data

Using satellite data and machine learning-based classification methods for mangrove forest identification has gained popularity due to their effectiveness in producing high accuracy. Therefore, this research developed a random forest classification model using various dataset combinations representing spectral and topographical aspects to identify mangrove forests in Segara Anakan, Cilacap. We used the Sentinel-2 imagery acquired in 2022. Two types of digital elevation model (DEM) data were utilized, namely the National Digital Elevation Model (DEMNAS) and Multi-Error-Removed Improved-Terrain DEM (MERIT DEM). Another critical dataset used is the Normalized Difference Moisture Index (NDMI), derived from the ratio between near-infrared (NIR) and shortwave-infrared (SWIR) bands. The classification method used is the random forest algorithm on six different dataset combinations, including IMAGE, IMAGE+NDMI, IMAGE+DEMNAS, IMAGE+MERIT DEM, IMAGE+NDMI+DEMNAS, and IMAGE+NDMI+ MERIT DEM. The results showed that the combination of Image+DEMNAS and Image+NDMI+DEMNAS datasets was able to identify the mangrove forests more optimally. Incorporating DEM data alongside IMAGE and NDMI datasets resulted in a remarkable level of accuracy in mangrove forest mapping, exceeding 90%. DEM data has a very important role in increasing the classification accuracy of mangrove forests using the random forest algorithm.

Automatic Crop Classification Based on Optimized Spectral and Textural Indexes Considering Spatial Heterogeneity

Crop recognition with high accuracy at a large scale is hampered by the spatial heterogeneity of crop growth characteristics under the complex influence of environmental conditions. With the aim to automatically realize large-scale crop classification with high accuracy, this study proposes an automatic crop classification strategy considering spatial heterogeneity (ACCSH) by combining the geographic detector technique, random forest average accuracy model, and random forest classification model. In ACCSH, spectral and textural indexes that can quantify crop growth characteristics and environmental variables with potential driving effects are first calculated. Next, an adaptive spatial heterogeneity mining method based on the geographic detector technique is proposed to mine spatial homogeneous zones adaptively with significant differentiation of crop growth characteristics. Subsequently, in view of the differences in crop growth characteristics and key classification indexes between spatial homogeneous zones, correlation analysis, and random forest average accuracy are combined to optimize classification indexes independently within each zone. Finally, random forest is used to classify the target crop in each spatial homogeneous zone separately. The proposed ACCSH is applied to automatically recognize crop types, specifically wheat and corn, in northern France. Results show that kappa coefficients of wheat and corn using ACCSH are 15% and 26% higher than those of classifications at the global scale, respectively. In addition, the index optimization strategy in ACCSH shows apparent superiority. Kappa coefficients of wheat and corn are 5–18% and 9–42% higher than those of classifications based on non-optimized indexes, respectively. In general, ACCSH can automatically realize crop classification with a high precision that suggests its reliability.

Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study.

Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.

A Knowledge Graph Method towards Power System Fault Diagnosis and Classification

As the scale and complexity of electrical grids continue to expand, the necessity for robust fault detection techniques becomes increasingly urgent. This paper seeks to address the limitations in traditional fault detection approaches, such as the dependence on human experience, low efficiency, and a lack of logical relationships. In response, this study presents a cascaded model that leverages the Random Forest classifier in combination with knowledge reasoning. The proposed method exhibits a high efficiency and accuracy in identifying six basic fault types. This approach not only simplifies fault detection and handling processes but also improves their interpretability. The paper begins by constructing a power fault simulation model, which is based on the IEEE 14-bus system. Subsequently, a Random Forest classification model is developed and compared with other commonly used models such as Support Vector Machines (SVMs), k-Nearest Neighbor (KNN), and Naïve Bayes, using metrics such as the F1-score, accuracy, and confusion matrices. Our results reveal that the Random Forest classifier outperforms the other models, particularly in small-sample datasets, with an accuracy of 90%. Then, we apply knowledge mining technology to create a comprehensive knowledge graph of power faults. At last, we use the transE model for knowledge reasoning to enhance the interpretability to assist decision making and to validate its reliability.

Predicting nitrate exposure from groundwater wells using machine learning and meteorological conditions

AbstractPrivate groundwater wells can be unmonitored sources of contaminated water that can harm human health. Developing models that predict exposure could allow residents to take action to reduce risk. Machine learning models have been successful in predicting nitrate contamination using geospatial information such as proximity to nitrate sources, but previous models have not considered meteorological factors that change temporally. In this study, we test random forest (regression and classification) and linear regression models to predict nitrate contamination using rainfall, temperature, and readily available soil parameters. We trained and tested models for (1) all of North Carolina, (2) each geographic region in North Carolina, (3) a three‐county region with a high density of animal agriculture, and (4) a three‐county region with a low density of animal agriculture. All regression models had poor predictive performance (R2 < 0.09). The random forest classification model for the coastal plain showed fair agreement (Cohen's κ = 0.23) when trying to predict whether contamination occurred. All other classification models had slight or poor predictive performance. Our results show that temporal changes in rainfall and temperature, or in combination with soil data, are not enough to predict nitrate contamination in most areas of North Carolina. The low level of contamination (<25%) measured during the study could have contributed to the poor performance of the models.