Random Forest Approach Research Articles

An Improved Random Forest Approach on GAN-Based Dataset Augmentation for Fog Observation

The monitoring of fog density is of great importance in meteorology and its applications in environment, aviation and transportation. Nowadays, vision-based fog estimation from images taken with surveillance cameras has made a great supplementary contribution to the scarcely traditional meteorological fog observation. In this paper, we propose a new Random Forest (RF) approach for image-based fog estimation. In order to reduce the impact of data imbalance on recognition, the StyleGAN2-ADA (generative adversarial network with adaptive discriminator augmentation) algorithm is used to generate virtual images to expand the data of low proportions. Key image features related to fog are extracted, and an RF method, integrated with the hierarchical and k-medoid clustering, is deployed to estimate the fog density. The experiment conducted in Sichuan in February 2024 shows that the improved RF model has achieved an average accuracy of fog density observation of 93%, 6.4% higher than the RF model without data expansion, 3–6% higher than the VGG16, the VGG19, the ResNet50, and the DenseNet169 with or without data expansion. What is more, the improved RF method exhibits a very good convergence as a cost-effective solution.

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

Transferability of models for predicting potato plant nitrogen content from remote sensing data and environmental variables across years and regions

The use of remote sensing technologies to monitor the nitrogen nutrient status of crops is gradually becoming a more sensible choice, as traditional methods are time-consuming, labor-intensive, and destructive. However, most predictive models utilizing remote sensing data are statistical rather than mechanistic, making them difficult to extend at interannual and regional scales. This study explored the interannual and regional transferability of the potato plant nitrogen content (PNC) prediction models, which combined environmental variables (EVs, e.g. temperature, precipitation, etc.) with proximal hyperspectral vegetation indices (VIs). Two methodologies were implemented to fuse EVs and VIs. The first involved a multiple regression analysis utilizing a multivariate linear model and a random forest approach, with VIs and EVs treated as independent variables, respectively. The second, a hierarchical linear model (HLM), employed EVs to dynamically adjust the relationship between VIs and PNC for different experimental sites. The predictive outcomes demonstrated that (i) the conventional method relying solely on optical VIs exhibited limited accuracy and stability in interannual and regional PNC forecasting; (ii) albeit the multivariate regression approach significantly enhanced model accuracy within the calibration set, its scalability across years and regions remained suboptimal; (iii) the HLM method exhibited high precision and scalability across years and regions, with R2, RMSE, and NRMSE values of 0.68, 0.50 %, and 19.68 % in the validation set, respectively. Those findings corroborate that using a two-tier HLM method can automatically adjust for discrepancies in VIs response to PNC through EVs, thereby enhancing the model's stability. Provided that remote sensing data and EVs are sustainably acquired over the potato growth cycle, it will provide a particularly promising approach to potato nitrogen diagnostics as a decision-making tool for regional application of nitrogen fertilizer.

Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models

ABSTRACT This study examined the cyclic behavior of glass-fiber-reinforced polymer composite column-beam connectors in various diameters. Mean learning (XGBoost and the random forest algorithm) and numerical methods were used to identify the data collected experimentally due to the rotation behavior. The initial sample had a column length of 1200 mm and a cross-section of 80 × 80 mm, with a beam section measuring 80 × 160 mm. The second sample had a 100 × 100 mm column and a beam section measuring 100 × 200 and 1200 mm, respectively. For the third sample, the column’s dimensions were 130 × 130 mm in cross-section and 1200 mm in length, and the beam was 130 × 260 mm. Spruce wood is used to make glulam components for columns and beams. Following the creation of the column-beam connections, glass-based fiber-reinforces polymer (FRP) fabric was used to wrap the column-beam connections that needed strengthening. The cyclic behavior of the reinforced reference samples and the samples with reinforced column-beam joint areas was examined. The column-beam connection with code C13B13-R has the most load-carrying capacity (13.9 kN), while the beam with code C08B08-UR has the lowest load-carrying capacity (03.4 kN). Examining the table reveals that the findings of the numerical analysis and the experiment provide comparable conclusions. When comparing experimental and numerical results, similar values are found for stiffness values (R 2 of 0.99), load-bearing capacity (R 2 of 0.99), and energy consumption capacity (R 2 of 0.99). With R 2 of 0.9978, RMSE of 0.01017, and MAE of 0.01043, the random forest approach identified the stiffness values in the model with the best accuracy. It has been shown that the seismic behavior of column-beam couplings built with these materials may be studied with mean learning models and numerical analysis, instead of demanding and time-consuming experimental studies.

A 10 km daily-level ultraviolet-radiation-predicting dataset based on machine learning models in China from 2005 to 2020

Abstract. Ultraviolet (UV) radiation is closely related to health; however, limited measurements have hindered further investigation of its health effects in China. Machine learning algorithms have been widely used to predict environmental factors with high accuracy, but a limited number of studies have implemented it for UV radiation. The main aim of this study is to develop a UV radiation prediction model using the random forest approach and predict the UV radiation with a daily and 10 km resolution in mainland China from 2005 to 2020. The model was developed with multiple predictors, such as UV radiation data from satellites as independent variables and ground UV radiation measurements from monitoring stations as the dependent variable. Missing satellite-based UV radiation data were obtained using the 3 d moving average method. The model performance was evaluated using multiple cross-validation (CV) methods. The overall R2 and root mean square error between measured and predicted UV radiation from model development and model 10-fold CV were 0.97 and 15.64 W m−2 and 0.83 and 37.44 W m−2 at the daily level, respectively. The model that incorporated erythemal daily dose (EDD) retrieved from the Ozone Monitoring Instrument (OMI) had a higher prediction accuracy than that without it. Based on predictions of UV radiation at the daily level, 10 km spatial resolution, and nearly 100 % spatiotemporal coverage, we found that UV radiation increased by 4.20 %, PM2.5 levels decreased by 48.51 %, and O3 levels increased by 22.70 % from 2013–2020, suggesting a potential correlation among these environmental factors. The uneven spatial distribution of UV radiation was associated with factors such as latitude, elevation, meteorological factors, and season. The eastern areas of China pose a higher risk due to both high population density and high UV radiation intensity. Using a machine learning algorithm, this study generated a gridded UV radiation dataset with extensive spatiotemporal coverage, which can be utilized for future health-related research. This dataset is freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024).

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

Development of an accelerometer-based wearable sensor approach for alcohol consumption detection.

Alcohol is a commonly used substance associated with significant public health consequences. Treatment is often stigmatized and limited with regard to both access and affordability, demonstrating the need for innovations in alcohol treatment. Accelerometer sensors can detect drinking without user input and are widely incorporated into wearable devices, increasing accessibility and affordability. We compared a distributional and random forest classification approach to detect and evaluate sensor-based drinking data. Data were collected at a local state fair (n = 194), where participants drank water at specified intervals interspersed with confounding behaviors (e.g., touching nose, rubbing forehead, or yawning) while wearing an Android-based smartwatch for 10 min. Participants were randomized to receive one of three drinking container shapes: pint, martini, or wine. The random forest model achieved an overall testing accuracy of 93% (sensitivity = 0.32; specificity = 0.99; positive predictive value = 0.74). The distributional algorithm achieved an overall accuracy of 95% (sensitivity = 0.76; specificity = 0.97; positive predictive value = 0.72). The distributional algorithm had a significantly greater accuracy (t(193) = 7.73, p < 0.001, d = 0.56) and sensitivity (t(193) = 24.5, p < 0.001, d = 1.76). Equivalency testing demonstrated significant equivalency to the ground truth for sip duration (tlower(193) = 16.92, p < 0.001; tupper(193) = -9.85, p < 0.001) and between-sip interval (tlower(193) = 1.72, p = 0.044; thigher(193) = -3.96, p < 0.001). However, the random forest did not have significant equivalency to the ground truth for between-sip interval (tlower(193) = 1.98, p = 0.025; thigher(193) = 0.160, p = 0.564). Overall, the results indicated that consumer-grade smartwatches can be utilized to detect and measure alcohol use behavior using machine learning and distributional algorithms. This work provides the methodological foundation for future research to analyze the behavioral pharmacology of alcohol use and develop accessible just-in-time clinical interventions.

Exploring predictors of interaction among low-birth-weight infants and their caregivers: a machine learning–based random forest approach

BackgroundQuality caregiver-infant interaction is crucial for infant growth, health, and development. Traditional methods for evaluating the quality of caregiver-infant interaction have predominantly relied on rating scales or observational techniques. However, rating scales are prone to inaccuracies, while observational techniques are resource-intensive. The utilization of easily collected medical records in conjunction with machine learning techniques offers a promising and viable strategy for accurate and efficient assessment of caregiver-infant interaction quality.MethodsThis study was conducted at a follow-up outpatient clinic at two tertiary maternal and infant health centers located in Shanghai, China. 68 caregivers and their 3-15-month-old infants were videotaped for 3–5 min during playing interactions in non-threatening environment. Two trained experts utilized the Infant CARE-Index (ICI) procedure to assess whether the caregivers were sensitive or not in a dyadic context. This served as the gold standard. Predictors were collected through Health Information Systems (HIS) and questionnaires, which included accessible features such as demographic information, parental coping ability, infant neuropsychological development, maternal depression, parent-infant interaction, and infant temperament. Four classification models with fivefold cross-validation and grid search hyperparameter tuning techniques were employed to yield prediction metrics. Interpretable analyses were conducted to explain the results.ResultsThe score of sensitive caregiver-infant interaction was 6.34 ± 2.62. The Random Forest model gave the best accuracy (83.85%±6.93%). Convergent findings identified infant age, care skills of infants, mother age, infant temperament-regulatory capacity, birth weight, positive coping, health-care-knowledge-of-infants, type of caregiver, MABIS-bonding issues, ASQ-Fine Motor as the strongest predictors of interaction sensitivity between infants and their caregiver.ConclusionsThe proposed method presents a promising and efficient approach that synergistically combines rating scales and artificial technology to detect important features of caregiver-infant interactions. This novel approach holds several implications for the development of automatic computational assessment tools in the field of nursing studies.

Climate, altitude, yield, and varieties drive lodging in sugarcane: A random forest approach to predict risk levels on a tropical island

Lodging is a critical factor in reducing sugarcane yields worldwide, mainly due to the selection of highly productive varieties. Understanding the response of yield and lodging to the combined effects of climate, sugarcane traits, and varieties has become a priority under climate change. The aim of this study was to better understand the influence of plant characteristics, climate, and soil conditions on the trade-off between sugarcane yield and lodging on the tropical Reunion Island. Data from a 14-year experimental network run by the eRcane breeding institute were used to build random-forest models to predict sugarcane yield and lodging classes, i.e. <10 %, 10–50 %, >50 % of lodging. Yield and lodging probability were then predicted across the island using climate change projections from 2015 to 2035. Both yield and lodging were highly influenced by the variety and characteristics (height and tillering) and climatic conditions. Areas on the island at high altitudes were subject to high probability of lodging (>50 %), while in areas with high wind speed, the risk of moderate lodging (10–50 %) increased. Overall, conditions or plant characteristics that favor higher yields increased lodging probability. Nevertheless, the correlation between yield and lodging probability varied considerably depending on the variety, highlighting the importance of sugarcane characteristics in resistance to lodging. This study highlights the fact that promoting more productive varieties in recent decades has led to an increase in lodging and identified critical environments on the island prone to increased risk of lodging.

Forecasting Foreign Direct Investment Inflow to Bangladesh: Using an Autoregressive Integrated Moving Average and a Machine Learning-Based Random Forest Approach

This study focuses on the challenge of accurately forecasting foreign direct investment (FDI) inflows to Bangladesh, which are crucial for the country’s sustainable economic growth. Although Bangladesh has strong potential as an investment destination, recent FDI inflows have sharply declined due to global economic uncertainties and the impact of the COVID-19 pandemic. There is a clear gap in applying advanced forecasting models, particularly the autoregressive integrated moving average (ARIMA) model and machine learning techniques like random forest (RF), to predict FDI inflows in Bangladesh. This study aims to analyze and forecast FDI inflows in Bangladesh by employing a hybrid approach that integrates the ARIMA model and the RF algorithm. This study covers the period from 1986 to 2022. The analysis reveals that net FDI inflow in Bangladesh is integrated into the first order, and the ARIMA (3,1,2) model is identified as the most suitable based on the Akaike Information Criterion (AIC). Diagnostic tests confirm its consistency and appropriateness for forecasting net FDI inflows in the country. This study’s findings indicate a decreasing trend in net FDI inflows over the forecasted period, with an average of USD 1664 million, similar to recent values. The results from the RF model also support these findings, projecting average net FDI values of USD 1588.99 million. To achieve the aims of Vision 2041, which include eradicating extreme poverty and becoming a high-economic nation, an increasing trend of FDI inflow is crucial. The current forecasting trends provide insights into the potential trajectory of FDI inflows in Bangladesh, highlighting the importance of attracting higher FDI to accomplish their economic goals. Additionally, strengthening bilateral investment agreements and leveraging technology transfer through FDI will also be essential for fostering sustainable economic growth.

Prediction of pre-eclampsia with machine learning approaches: Leveraging important information from routinely collected data

BackgroundGlobally, pre-eclampsia (PE) is a leading cause of maternal and perinatal morbidity and mortality. PE prediction using routinely collected data has the advantage of being widely applicable, particularly in low-resource settings. Early intervention for high-risk women might reduce PE incidence and related complications. We aimed to replicate our machine learning (ML) published work predicting another maternal condition (gestational diabetes) to (1) predict PE using routine health data, (2) identify the optimal ML model, and (3) compare it with logistic regression approach. MethodsData were from a large health service network with 48,250 singleton pregnancies between January 2016 and June 2021. Supervised ML models were employed. Maternal clinical and medical characteristics were the feature variables (predictors), and a 70/30 data split was used for training and testing the model. Predictive performance was assessed using area under the curve (AUC) and calibration plots. Shapley value analysis assessed the contribution of feature variables. ResultsThe random forest approach provided excellent discrimination with an AUC of 0.84 (95% CI: 0.82–0.86) and highest prediction accuracy (0.79); however, the calibration curve (slope of 1.21, 95% CI 1.13–1.30) was acceptable only for a threshold of 0.3 or less. The next best approach was extreme gradient boosting, which provided an AUC of 0.77 (95% CI: 0.76–0.79) and well-calibrated (slope of 0.93, 95% CI 0.85–1.01). Logistic regression provided good discrimination performance with an AUC of 0.75 (95% CI: 0.74–0.76) and perfect calibration. Nulliparous, pre-pregnancy body mass index, previous pregnancy with prior PE, maternal age, family history of hypertension, and pre-existing hypertension and diabetes were the top-ranked features in Shapley value analysis. ConclusionTwo ML models created the highest-performing prediction using routinely collected data to identify women at high risk of PE, with acceptable discrimination. However, to confirm this result and also examine model generalisability, external validation studies are needed in other settings, utilising standardised prognostic factors.

Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning

The welfare of animals under human care is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant pacing. Traditional behaviour monitoring methods, however, are labour-intensive and subject to observer bias. Our study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in managed populations, exemplified by a longitudinal study of two polar bears. We designed an animal tracking framework to localise and identify individual animals in the enclosure. After determining their position on the enclosure map via homographic transformation, we refined the resulting trajectories using a particle filter. Finally, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %. The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological settings. Ultimately, we analysed 607 days for the occurrence of SB, allowing us to discuss seasonal patterns of SB in both the male and female polar bear monitored. This work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated detection and monitoring of stereotypical behaviour, paving the way for its application across various settings and species that can be continuously monitored with cameras. We made the code publicly available at https://github.com/team-vera/stereotypy-detector.

Elevation-dependent dynamics of soil properties in a hilly watershed: a landform-based approach.

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km2 watershed were collected using a gridded sampling scheme (2km × 2km resolution) over 57weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Estimation of district-level spring barley yield in southern Sweden using multi-source satellite data and random forest approach

Remote sensing observations and artificial intelligence algorithms have emerged as key components for crop yield estimation at various scales during the past decades. However, the utilization of multi-source satellite data and machine learning for estimating aggregated crop yield at the regional level in Europe has been only scarcely explored. Our study aims to bridge this research gap by focusing on the district-level spring barley yield estimation in southern Sweden from 2017 to 2022. We developed an estimation method with the random forest (RF) approach using four satellite-derived products along with two climate variables. These variables were used individually and in combinations as inputs for the RF approach. The results showed that vegetation indices (VIs) outperformed solar-induced chlorophyll fluorescence (SIF) in barley yield estimation, while combining VIs and SIF variables achieved the highest model performance (R2 = 0.77, RMSE = 488 kg/ha). The inclusion of climate variables generally had little added contributions to the model performance. Importantly, barley yield prediction could be achieved two months prior to harvest, using monthly VIs and SIF data from April and May. Our study demonstrated the feasibility of using freely accessible satellite data and the machine learning approach for estimating crop yield at the pan-European regional level. We expect that our proposed methodology can be extended to different crop types and regional-scale crop yield estimation in Europe, benefiting national and local authorities in making agricultural productivity decisions.

Artificial Intelligence-Assisted Perfusion Density as Biomarker for Screening Diabetic Nephropathy.

To identify a reliable biomarker for screening diabetic nephropathy (DN) using artificial intelligence (AI)-assisted ultra-widefield swept-source optical coherence tomography angiography (UWF SS-OCTA). This study analyzed data from 169 patients (287 eyes) with type 2 diabetes mellitus (T2DM), resulting in 15,211 individual data points. These data points included basic demographic information, clinical data, and retinal and choroidal data obtained through UWF SS-OCTA for each eye. Statistical analysis, 10-fold cross-validation, and the random forest approach were employed for data processing. The degree of retinal microvascular damage in the diabetic retinopathy (DR) with the DN group was significantly greater than in the DR without DN group, as measured by SS-OCTA parameters. There were strong associations between perfusion density (PD) and DN diagnosis in both the T2DM population (r = -0.562 to -0.481, P < 0.001) and the DR population (r = -0.397 to -0.357, P < 0.001). The random forest model showed an average classification accuracy of 85.8442% for identifying DN patients based on perfusion density in the T2DM population and 82.5739% in the DR population. Quantitative analysis of microvasculature reveals a correlation between DR and DN. UWF PD may serve as a significant and noninvasive biomarker for evaluating DN in patients through deep learning. AI-assisted SS-OCTA could be a rapid and reliable tool for screening DN. We aim to study the pathological processes of DR and DN and determine the correspondence between their clinical and pathological manifestations to further clarify the potential of screening DN using AI-assisted UWF PD.

A Latitudinal Cline in the Taxonomic Structure of Eelgrass Epifaunal Communities is Associated With Plant Genetic Diversity

ABSTRACTAimBiogenic structural complexity increases mobile animal richness and abundance at local, regional and global scales, yet animal taxa vary in their response to complexity. When these taxa also vary functionally, habitat structures favouring certain taxa may have consequences for ecosystem function. We characterised global patterns of epifaunal invertebrates in eelgrass (Zostera marina) beds that varied in structural and genetic composition.LocationNorth America, Europe and Asia.Time Period2014.Major Taxa StudiedPeracarid crustaceans and gastropod molluscs.MethodsWe sampled epifaunal invertebrate communities in 49 eelgrass beds across 37° latitude in two ocean basins concurrently with measurements of eelgrass genetic diversity, structural complexity and other abiotic and biotic environmental variables. We examined how species richness, abundance and community composition varied with latitude and environmental predictors using a random forest approach. We also examined how functional trait composition varied along with community structure.ResultsTotal species richness decreased with latitude, but this was accompanied by a taxonomic shift in dominance from peracarid crustaceans to gastropods, which exhibited different sets of functional traits. Greater eelgrass genetic diversity was strongly correlated with both richness and abundance of peracarids, but less so for gastropods.Main ConclusionsOur results add to a growing body of literature that suggests genetic variation in plant traits influences their associated faunal assemblages via habitat structure. Because peracarids and gastropods exhibited distinct functional traits, our results suggest a tentative indirect link between broad‐scale variation in plant genetic diversity and ecosystem function.

Developing practical models for predicting chlorophyll levels in the Andong Reservoir using empirical and machine learning approaches based on integrated analysis of physical, chemical, and hydrological indicators

Managing excessive phytoplankton biomass is a pressing issue worldwide and requires precise predictions and efficient water quality control. In this study, we employed the random forest approach to build a practical predictive model for phytoplankton biomass as determined by chlorophyll (CHL-a) levels. Using an integrative 15-year dataset, we obtained insights into CHL-a dynamics and identified important factors influencing it. Seasonal dynamics were crucial for shaping water quality, with meteorological and hydrological fluctuations playing pivotal roles. Elevated suspended solids and phosphorus levels during the summer monsoon indicated increased runoff and nutrient loading, contributing to fluctuations in nutrient ratios. CHL-a was also responsive to seasonal variations in nutrient availability, particularly in phosphorus and ammonium-nitrogen (NH4-N). These results suggest the importance of nutrient management strategies for controlling and mitigating eutrophication risk. The random forest model had practical accuracy (R2 = 0.66, RMSE = 1.85) for predicting long-term CHL-a variation and identified phosphate-phosphorus, water temperature, and NH4-N as the most important predictors. However, our findings emphasize the importance of comprehensive analyses to identify the factors driving CHL-a variation. Combining the strengths of machine learning with multifaceted insights would enhance prediction accuracy and efficiency, ultimately facilitating informed decision-making for reservoir management and environmental conservation efforts.

Random forest analysis of the relative importance of meteorological indicators for heatstroke cases in Japan based on the degree of severity and place of occurrence

Heatstroke is a serious health concern in Japan. To reduce heatstroke risk, the government of Japan implemented the “Heatstroke Alert” nationwide in 2021, employing the wet bulb globe temperature (WBGT) as a criterion. Although the WBGT is a useful meteorological indicator for assessing the risk of heatstroke, other important meteorological indicators must also be investigated. Therefore, using a random forest approach, this study analyzed the relative importance of several meteorological indicators, including those representing heat acclimatization, for each of the 47 Japanese prefectures. Using the generalized linear model, important meteorological indicators were employed as explanatory variables in the heatstroke prediction model to determine the predictive meteorological indicator. Heatstroke cases were evaluated separately by the degree of severity and the place of occurrence. The results showed that the relative temperature (RelTemp), which represents heat acclimatization and was calculated considering past temperature history, was the most predictive (i.e., provided the best goodness of fit) concerning the degree of severity, place of occurrence, and prefectures. RelTemp can be a complementary indicator of WBGT in countries and regions such as Japan, where seasonal differences in heat acclimatization must be considered. In addition, the findings of this study contribute to the development of a more accurate assessment of heatstroke risk.

Impact of serum potassium on length of hospitalization stay: A random forest approach

Impact of serum potassium on length of hospitalization stay: <scp>A</scp> random forest approach

Analysis of Road Roughness and Driver Comfort in 'Long-Haul' Road Transportation Using Random Forest Approach.

Global trade depends on long-haul transportation, yet comfort for drivers on lengthy trips is sometimes neglected. Rough roads have a major negative influence on driver comfort and increase the risk of weariness, distracted driving, and accidents. Using Random Forest regression, a machine learning technique well-suited to examining big datasets and nonlinear relationships, this study examines the relationship between road roughness and driver comfort. Using the MIRANDA mobile application, data were gathered from 1,048,576 rows, including vehicle acceleration and values for the International Roughness Index (IRI). The Support Vector Regression (SVR) and XGBoost models were used for comparative analysis. Random Forest was preferred because of its ability to be deployed in real time and use less memory, even if XGBoost performed better in terms of training time and prediction accuracy. The findings showed a significant relationship between driver discomfort and road roughness, with rougher roads resulting in increased vertical acceleration and lower comfort levels (Road Roughness: SD-0.73; Driver's Comfort: Mean-10.01, SD-0.64). This study highlights how crucial it is to provide smooth surfaces and road maintenance in order to increase road safety, lessen driver weariness, and promote long-haul driver welfare. These results offer information to transportation authorities and policymakers to help them make data-driven decisions that enhance the efficiency of transportation and road conditions.