Random Forest Machine Learning Algorithm Research Articles

Random Forest Ensemble Machine Learning Model for Early Detection and Prediction of Weight Category

The number of insurgents in our nation today is significantly rising each day, and the majority of those affected are living as Internally Displayed Persons (IDP) in various IDP camps. These people experience a variety of health problems as a result of numerous factors. Due to financial difficulties and a lack of accessibility to healthcare facilities and medical professionals, these health risk factors may occasionally go undetected for long periods. BMI excesses, such as those in the underweight, overweight, and obese categories, are linked to several health issues, including low birth weight, poor quality of life, diabetes mellitus, cardiovascular diseases, and higher mortality. In the context of this paper, identifying the health status of IDPs depends critically on human body weight. For people living in IDP camps, early detection of the weight categories like underweight, overweight, and obese people is crucial because if not, they will be an early death or other health complications. To reduce mortality rates and other health complications that may result from improper and lately identifying underweight, overweight, and obese members in IDP camps, the researcher collect Datasets from the IDP camps, trained, and developed a Random Forest ensemble model of supervised learning that will aids the medical practitioner in early detection and prediction of the weight category of IDPs. After hyper-parameter tuning and feature selection, the Random forest machine learning algorithms identify three significant parameters from the dataset's original 10 parameters to use as the model parameter. The highest accuracy obtained was 92% on the test dataset and 96% on the training dataset for the Random Forest (RF) Classifier using 3 features, while the accuracy of 83% was obtained on the test dataset and 87% on the training dataset for the RF Classifier using 10 features.

Prepayment and Default Risks of Mortgage-Backed Security Collateral Pools

Mortgage-backed securities (MBS) are structured financial products that are produced via securitization of mortgage loans. Due to the nature of securitization, all risks of mortgage loans are transferred from originators to MBS investors. Prepayment and default risks of mortgages lead to uncertainty in MBS cash flows and create a complex problem for valuation of these instruments. Therefore, estimating these mortgage termination risks has become the focus of valuation of MBS collateral pools. This study explores two questions by using a publicly open dataset provided by Fannie Mae. First, two machine learning algorithms (Random Forest and Multinomial Logit Regression) are used for classification to predict whether a mortgage loan is likely to be prepaid, defaulted or current. Afterwards, Competing Risks Cox Regression Analysis is performed to see determinants of when mortgage termination risks are likely to happen. It is found that not all mortgage borrowers behave optimally in their prepayment and default decisions. Therefore, in addition to refinancing incentive and negative equity which depend on variations in prevailing mortgage interest rates and housing prices, heterogeneity in mortgage borrowers’ behaviors and loan characteristics, and also local economic factors are significantly important in estimating mortgage termination risks. It is worth noting that prominence role of mortgage payment delinquencies in particularly predicting defaults emphasizes the essential need of monitoring payments by servicers to keep safety of MBS investors and financial markets.

Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery

The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows.

A machine learning approach to predicting plant available phosphorus that accounts for soil heterogeneity and regional variability

PurposeMehlich-3 extractable P, Al, Ca, and Fe combined with pH can be used to help explain soil chemical processes which regulate P retention, such as the role of Al, Ca, Fe, and pH levels in P fixation and buffering capacity. However, Mehlich-3 is not always the standard test used in agriculture. The objective of this study is to assess the most reliable conversion of Mehlich-3 Al, Ca, Fe, and P and pH into a commonly used soil P test, Morgan’s P, and specifically to predict values into decision support for fertiliser recommendations.MethodsA geochemical database of 5631 mineral soil samples which covered the northern area of Ireland was used to model soil test P and P indices using Mehlich-3 data.ResultsA random forest machine learning algorithm produced an R2 of 0.96 and accurately predicted soil P index from external validation in 90% of samples (with an error range of ± 1 mg L−1). The model accuracy was reduced when predicted Morgan’s P concentration was outside of the sampled area.ConclusionsIt is recommended that random forest is used to produce Mehlich-3 conversions, especially when data covers large spatial scales with large heterogeneity in soil types and regional variations. To implement conversion models into P testing regimes, it is recommended that representative soil types/geochemical attributes are present in the dataset. Furthermore, completion of a national scale geochemical survey is needed. This will enable accurate predictions of Morgan’s P concentration for a wider range of soils and geographical scale.

Ferroptosis-related genes LPCAT3 and PGD are potential diagnostic biomarkers for osteoarthritis

BackgroundOsteoarthritis (OA) is the most common chronic joint disease and how ferroptosis contributes to OA has garnered much attention recently. Bioinformatics promoted the discovery of ferroptosis-related biomarkers for OA. But since OA is a whole-joint disease, sensitive biomarkers for OA are still limited. We herein focused on subchondral bone, a joint component often-ignored by existing bioinformatic reports, to identify ferroptosis-related diagnostic biomarkers for OA.MethodMicroarray datasets GSE51588 and GSE55457 were downloaded from Gene Expression Omnibus database. Ferroptosis-related differential expression genes (Ferr-DEGs) between OA and normal samples were identified and their functional enrichment was analyzed. Common genes for OA diagnosis were selected from Ferr-DEGs using the combination of SVM-RFE, LASSO regression, and RandomForest machine learning algorithms. Common genes' diagnostic value was verified by receiver operating characteristic (ROC) curve and their association with immune infiltration was analyzed by CIBERSORT. Finally, candidate gene’s expression was verified in chondrocytes from OA patients and in an in vitro IL-1β-induced OA model, by RT-PCR.ResultsTwo ferroptosis-related genes, LPCAT3 and PGD, were identified as OA diagnostic biomarkers and confirmed by ROC diagnostic test. The association of LPCAT3 and PGD with the infiltration of several types of immune cells was identified. The decreased expression of LPCAT3 and PGD was both confirmed in OA chondrocytes and IL-1β-induced OA condition.ConclusionsWe identified ferroptosis-related genes LPCAT3 and PGD as potential diagnostic biomarkers for OA, which may offer insight into the role of ferroptosis in OA and provides useful information for the diagnosis and treatment of OA.

Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean

Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen’s K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most ‘hand-drawn’ knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model.

Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study.

The rapid increase of single-person households in South Korea is leading to an increase in the incidence of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases, due to lifestyle changes. It is necessary to analyze the complex effects of metabolic syndrome risk factors in South Korean single-person households, which differ from one household to another, considering the diversity of single-person households. This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning techniques and categorically characterize the risk factors through latent class analysis (LCA). This cross-sectional study included 10-year secondary data obtained from the National Health and Nutrition Examination Survey (2009-2018). We selected 1371 participants belonging to single-person households. Data were analyzed using SPSS (version 25.0; IBM Corp), Mplus (version 8.0; Muthen & Muthen), and Python (version 3.0; Plone & Python). We applied 4 machine learning algorithms (logistic regression, decision tree, random forest, and extreme gradient boost) to identify important factors and then applied LCA to categorize the risk groups of metabolic syndromes in single-person households. Through LCA, participants were classified into 4 groups (group 1: intense physical activity in early adulthood, group 2: hypertension among middle-aged female respondents, group 3: smoking and drinking among middle-aged male respondents, and group 4: obesity and abdominal obesity among middle-aged respondents). In addition, age, BMI, obesity, subjective body shape recognition, alcohol consumption, smoking, binge drinking frequency, and job type were investigated as common factors that affect metabolic syndrome in single-person households through machine learning techniques. Group 4 was the most susceptible and at-risk group for metabolic syndrome (odds ratio 17.67, 95% CI 14.5-25.3; P<.001), and obesity and abdominal obesity were the most influential risk factors for metabolic syndrome. This study identified risk groups and factors affecting metabolic syndrome in single-person households through machine learning techniques and LCA. Through these findings, customized interventions for each generational risk factor for metabolic syndrome can be implemented, leading to the prevention of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases. In conclusion, this study contributes to the prevention of metabolic syndrome in single-person households by providing new insights and priority groups for the development of customized interventions using classification.

Enhanced Understanding of Key Soil Properties in Northern Xinjiang Using Water-Heat-Spectral Datasets Based on Bioclimatic Guidelines

Current digital soil mapping of soil properties (soil organic carbon, SOC; electrical conductivity, EC; and pH) is mainly based on transfer learning, which is inadequate in terms of accuracy for the northern plain area of Xinjiang. To address this issue, establishing a new model is urgently required that can improve our understanding of the soil properties in this region. To this end, based on the global bioclimatic variables and surface dry–wet and wet–dry transitions, The study developed a spectral–water–heat database (SWHD). The study then incorporated this database and background data into machine learning algorithms (XGBoost, LightGBM, and random forest) to establish models applicable to the study area and draw spatial changes in the key soil properties. Our findings revealed that the organic carbon content was the highest in grasslands, whereas shrublands had high soil salinity. The pH value indicated overall alkalinity in the study area. Additionally, the SWHD-based predictions outperformed the mean or maximum value datasets, with LightGBM showing superior performance among all models. Furthermore, the validation accuracy obtained through our optimal algorithm was significantly higher than that obtained by other products, such as Harmonized World Soil Database (HWSD) and SoilGrid250, likely because of the limitations of these datasets, which may represent historical soil properties rather than current variations in the soil properties in the region. The study also observed that the mean SOC and EC values significantly decreased compared to the historical data, while the decrease in pH was smaller but not significant. Structural equation modeling and variable importance analysis revealed that the variables with the greatest influence on modeling SOC, EC, and pH were BIO10, DTW2021_406-426_B3 (Surface reflectance acquired in spring), and land use type. Our improved model developed based on the SWHD dataset offers important scientific evidence and decision support for land use management and provides a solid foundation for future research in this field.

A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth.

Preterm birth (PB) is a leading cause of perinatal morbidity and mortality. PB prediction is performed by measuring cervical length, with a detection rate of around 70%. Although it is known that a cytokine-mediated inflammatory process is involved in the pathophysiology of PB, none screening method implemented in clinical practice includes cytokine levels as a predictor variable. Here, we quantified cytokines in cervical-vaginal mucus of pregnant women (18-23.6 weeks of gestation) with high or low risk for PB determined by cervical length, also collecting relevant obstetric information. IL-2, IL-6, IFN-γ, IL-4, and IL-10 were significantly higher in the high-risk group, while IL-1ra was lower. Two different models for PB prediction were created using the Random Forest machine-learning algorithm: a full model with 12 clinical variables and cytokine values and the adjusted model, including the most relevant variables-maternal age, IL-2, and cervical length- (detection rate 66 vs. 87%, false positive rate 12 vs. 3.33%, false negative rate 28 vs. 6.66%, and area under the curve 0.722 vs. 0.875, respectively). The adjusted model that incorporate cytokines showed a detection rate eight points higher than the gold standard calculator, which may allow us to identify the risk PB risk more accurately and implement strategies for preventive interventions.

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (&gt;0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach

Dissolved organic matter (DOM) plays important roles not only in maintaining the productivity and functioning of aquatic ecosystems but also in the global carbon cycle, although the sources and biogeochemical functions of terrestrially derived DOM have not been fully elucidated, particularly in the tropics and subtropics. This study aimed to evaluate the factors influencing spatiotemporal variability in (i) the concentration and composition of DOM, including dissolved organic carbon (DOC), ultraviolet absorption coefficient at 254-nm wavelength (a254), and components identified by fluorescence excitation–emission matrix coupled with parallel factor analysis (EEM-PARAFAC), and (ii) the concentration of dissolved iron (DFe) across freshwater systems (rivers, forested streams, and dam reservoirs) on a tropical island (Ishigaki Island, Japan) based on the results of water quality monitoring at 2-month intervals over a 2-year period. Random forests (RF) machine learning algorithm was employed, with the catchment characteristics (land use, soil type) and water temperature as the predictor variables for DOM and the composition of DOM (EEM-PARAFAC components) and hydrochemistry (water temperature, pH, and concentrations of divalent cations) as the predictor variables for DFe. The RF models for DOC, a254, and three humic-like components exhibited excellent predictive performance, indicating that these DOM properties are not only seasonally variable but also strongly influenced by the compositions of land uses and soil types in the upstream watershed. Poorly drained riparian lowland soil (Gleyic Fluvisols) was identified as the most important catchment parameter that positively influences these DOM variables. The RF model also explained a large portion of the variation in DFe, while terrestrial humic-like components were the most important parameters, emphasizing their significance as organic ligands for iron. These results improve our understanding of the impacts of terrestrial DOM and iron loadings on tropical and subtropical coastal ecosystems as well as on regional and global carbon budgets.

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.

Flood-Prone Zones of Meandering Rivers: Machine Learning Approach and Considering the Role of Morphology (Kashkan River, Western Iran)

We utilized the random forest (RF) machine learning algorithm, along with nine topographical/morphological factors, namely aspect, slope, geomorphons, plan curvature, profile curvature, terrain roughness index, surface texture, topographic wetness index (TWI), and elevation. Our objective was to identify flood-prone areas along the meandering Kashkan River and investigate the role of topography in riverbank inundation. To validate the flood susceptibility map generated by the random forest algorithm, we employed Sentinel-1 GRDH SAR imagery from the March 2019 flooding event in the Kashkan river. The SNAP software and the OTSU thresholding method were utilized to extract the flooded/inundated areas from the SAR imagery. The results showed that the random forest model accurately pinpointed areas with a “very high” and “high” risk of flooding. Through analysis of the cross-sections and SAR-based flood maps, we discovered that the topographical confinement of the meander played a crucial role in the extent of inundation along the meandering path. Moreover, the findings indicated that the inner banks along the Kashkan river were more prone to flooding compared to the outer banks.

Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0–0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

Accurate and Efficient Surrogate Model-Assisted Optimal Design of Flux Reversal Permanent Magnet Arc Motor

An accurate and efficient surrogate model plays a significant role in the optimal design of electrical motors. This paper presents an accurate and efficient surrogate modeling method for a flux reversal permanent magnet arc motor (FR-PMAM) based on machine learning algorithm. Specifically, the motor topology and the optimized design of the FR-PMAM are firstly illustrated. Then, the PM magnetic motive force-permeance model is developed to select structural parameters of FR-PMAM. After that, a finite-element analysis (FEA) model of FR-PMAM is calculated to obtain sample data. A machine learning algorithm random forest is introduced into the field of motor optimal design for the first time to evaluate the contribution of each parameter to the design objectives. Based on the sample data, an efficient machine learning algorithm CatBoost is innovatively employed to establish the accurate and efficient surrogate model of the FR-PMAM that can capture the complex function relationship between output objectives and input parameters. Subsequently, an improved genetic algorithm with the elitist reservation strategy is adopted to obtain the optimal combination of the structural parameters of FR-PMAM. Finally, a prototype of the FR-PMAM is manufactured. The FEA and experimental results validate the effectiveness of the investigated surrogate modeling method.

Marine Habitat Mapping using Multibeam Echosounder Survey and Underwater Video Observations: A Case Study from Tioman Marine Park

In recent years, there has been an increasing trend of utilizing high-resolution multibeam echosounder (MBES) datasets and supervised classification via machine learning to create marine habitat maps. The purpose of current study was threefold: (1) to extract bathymetric and backscatter derivatives from a multibeam dataset, (2) to measure the correlation between bathymetric and backscatter derivatives, and (3) to generate a marine habitat map using the Random Forest (RF). Tioman Marine Park (TMP), which is situated Southeast China Sea. MBES surveyed area are encompassed an area of 406 km² and served as the location for the study. Based on results and analysis, fourteen (14) derivative were derived from bathymetry map and backscatter mosaic. The second step involved integrating variables and a total of 152 of habitat ground-truth data were used, derived from underwater imageries, and sediment samples, into an RF model to generate a map of the marine habitat. Based on marine habitat map, six habitat classes including sand, rock, gravel and sand, coral rubble, coral and rock, and coral were classified. The distribution of coral habitat was found to be correlated with the depth of the bathymetry in the shallow water region. Therefore, the study has reached the conclusion that the integration between MBES derivatives, ground-truth data, and RF machine learning algorithm is an effective in classifying the distribution of marine habitats, specifically the coral habitat.

Prediction of compressional sonic log in the western (Tano) sedimentary basin of Ghana, West Africa using supervised machine learning algorithms

Sonic logs are essential for determining important reservoir properties such as porosity, permeability, lithology, and elastic properties, among others, and yet may be missing in some well logging suites due to high acquisition costs, borehole washout, tool damage, poor tool calibration, or faulty logging instruments. This study aims at predicting the compressional sonic log from commonly acquired logs (gamma ray, resistivity, density, and neutron-porosity) in the Tano basin of Ghana using Support Vector Machines (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) Machine Learning (ML) algorithms and comparing the performances of the algorithms. The algorithms were trained with 70% of the data from two wells and tested using the remaining 30% of the data from the wells after cross-validation. Subsequently, they were applied to the data from a third well to predict the sonic log in the well. The performances of the algorithms were assessed with five statistical tools: coefficient of determination (R2), adjusted R2, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). All three algorithms successfully predicted the compressional sonic log (DT). XGBoost demonstrated the highest prediction accuracy, with R2 of 0.9068 and the least errors. RF exhibited the next highest accuracy, with R2 being 0.85478, while SVM had R2 of 0.66591. Therefore, the ensemble algorithms (XGBoost and RF) proved to be more accurate than the non-ensemble algorithm (SVM) in this study. The outcome of the study will accelerate and enhance the understanding of oil and gas fields with few or no compressional sonic logs. To the best of the authors’ knowledge, this is the first study to have predicted the compressional sonic log from well data (logs) in a Ghanaian sedimentary basin using machine learning algorithms, and only a few such studies have been conducted in the whole West African sub-region.

Tile-Based Random Forest Analysis for Analyte Discovery in Balanced and Unbalanced GC × GC-TOFMS Data Sets.

In this study, we introduce a new nontargeted tile-based supervised analysis method that combines the four-grid tiling scheme previously established for the Fisher ratio (F-ratio) analysis (FRA) with the estimation of tile hit importance using the machine learning (ML) algorithm Random Forest (RF). This approach is termed tile-based RF analysis. As opposed to the standard tile-based F-ratio analysis, the RF approach can be extended to the analysis of unbalanced data sets, i.e., different numbers of samples per class. Tile-based RF computes out-of-bag (oob) tile hit importance estimates for every summed chromatographic signal within each tile on a per-mass channel basis (m/z). These estimates are then used to rank tile hits in a descending order of importance. In the present investigation, the RF approach was applied for a two-class comparison of stool samples collected from omnivore (O) subjects and stored using two different storage conditions: liquid (Liq) and lyophilized (Lyo). Two final hit lists were generated using balanced (8 vs Eight comparison) and unbalanced (8 vs Nine comparison) data sets and compared to the hit list generated by the standard F-ratio analysis. Similar class-distinguishing analytes (p < 0.01) were discovered by both methods. However, while the FRA discovered a more comprehensive hit list (65 hits), the RF approach strictly discovered hits (31 hits for the balanced data set comparison and 29 hits for the unbalanced data set comparison) with concentration ratios, [OLiq]/[OLyo], greater than 2 (or less than 0.5). This difference is attributed to the more stringent feature selection process used by the RF algorithm. Moreover, our findings suggest that the RF approach is a promising method for identifying class-distinguishing analytes in settings characterized by both high between-class variance and high within-class variance, making it an advantageous method in the study of complex biological matrices.

MArVD2: a machine learning enhanced tool to discriminate between archaeal and bacterial viruses in viral datasets

Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats.

Crop Diversification Assessment in Tank Ayacut Areas of Lower Palar Sub-Basin of Chengalpattu District, Tamil Nadu, India Using Geo-Spatial Techniques

For the assessment of crop diversification in the major tank Ayacut area of the Lower Palar sub-basin in Chengalpattu district of Tamil Nadu, research works were carried out using Sentinel 2 optical data by relating with ground truth data, to identify the crops in pixel-based classification and further classified the crops using Random Forest machine learning algorithms. The total area estimated under crop classification was 15767.97 and 28818.17 ha respectively for the summer seasons of 2018 and 2021. Since, the summer season experiences high crop diversification. The water spread area and water volume of tanks estimated were 612.31 and 1177.89 ha and 6,39,248 and 14,06,056 m3 respectively for 2018 and 2021. The accuracy assessment of ground truth points by confusion matrix reveals an overall classification accuracy of 96.8% (2018) and 94.9 % (2021) with kappa scores of 0.96 and 0.94 respectively. The crop diversification assessments were estimated using the Simpson Index of Diversity and values of 0.63 and 0.68 were accounted for in 2018 and 2021 respectively. The diversified pattern of crops is significantly correlated with tank water availability which increased the cropping area in 2021 as confirmed by the Crop Diversification factor.