Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan.

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( U r ) was below 50% for concentrations between 9.06 and 34.99 µg/m3 in HS and 5.75 and 17.58 µg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13 µg/m3 in HS and 11.72 to 38.86 µg/m3 in NHS.

Analysis of canopy interception characteristics and influencing factors in typical artificial forest in the Loess Plateau semi-arid region.

Interception loss (IL) is an important process in the hydrological cycle within semi-arid forest ecosystems, directly affecting the amount of effective rainfall. However, the factors influencing IL during individual rainfall events remain to be quantified. This study collected rainfall, vegetation, and interception data during the 2022 and 2023 growing seasons in a typical black locust forest within the Zhifanggou watershed. It employed the Random Forest Regression (RFR) and back-propagation neural network (BPNN) methods to quantitatively evaluate the contribution rates of various factors to the IL and interception loss percentage (ILP). The IL among the 48 effective rainfall events was 172.05 mm, accounting for 19.54% of the rainfall amount. IL and ILP increased as the distance from the trunk decreased. During all rainfall events, both IL and ILP were significantly negatively correlated with the leaf area index (LAI) and canopy cover (CC); IL is significantly positively correlated with total rainfall (TR) and rainfall intensity (RI), while ILP is significantly negatively correlated with TR, RI, and rainfall duration (RD). The BPNN and RFR results indicated that rainfall, canopy, and tree characteristics contributed 43.06%, 44.79%, and 12.15% to IL, respectively, and 57.27%, 34.09%, and 8.63% to ILP, respectively. TR, CC, and LAI represented the primary influencing factors. Rainfall and canopy characteristics were the main factors affecting IL (ILP). As rainfall event magnitude increases, canopy contributions to IL and ILP decrease. In semi-arid areas, managing forest canopies to control IL helps address water imbalances in ecosystems.

Microbial community composition predicts bacterial production across ocean ecosystems.

Microbial ecological functions are an emergent property of community composition. For some ecological functions, this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites-Palmer LTER in Antarctica and Station SPOT in California-we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2 pmol L-1 hr-1 on independent validation data, outperforming a model based solely on environmental data (R2 = 0.32, RMSE = 51.4 pmol L-1 hr-1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015 to 2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 × 10-4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.

Experimental insights and modeling innovations: Deciphering Fe(VI) oxidation in imidazole ionic liquids through QSAR and RFR.

In this investigation, we conducted a detailed analysis of the oxidation of 16 imidazole ionic liquid variants by Fe(VI) under uniform experimental setups, thereby securing a dataset of second-order reaction rate constants (kobs). This methodology ensures superior data consistency and comparability over traditional methods that amalgamate disparate data from varied studies. Utilizing 16 chemical structural parameters obtained via Density Functional Theory (DFT) as descriptors, we developed a Quantitative Structure Activity Relationship (QSAR) model. Through rigorous correlation analysis, Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Applicability Domain (AD) evaluation, we identified a pronounced negative correlation between the molecular orbital gap energy (Egap) and kobs. MLR analysis further underscored Egap as a pivotal predictive variable, with its lower values indicating heightened oxidative reactivity towards Fe(VI) in the ionic liquids, leading the QSAR model to achieve a predictive accuracy of 0.95. Furthermore, we integrated an advanced machine learning approach - Random Forest Regression (RFR), which adeptly highlighted the critical factors influencing the oxidation efficiency of imidazole ionic liquids by Fe(VI) through elaborate decision trees, feature importance assessment, Recursive Feature Elimination (RFE), and cross-validation strategies. The RFR model demonstrated a remarkable predictive performance of 0.98. Both QSAR and RFR models pinpointed Egap as a key descriptor significantly affecting oxidation efficiency, with the RFR model presenting lower root mean square errors, establishing it as a more reliable predictive tool. The application of the RFR model in this study significantly improved the model's stability and the intuitive display of key influencing factors, introducing promising advanced analytical tools to the field of environmental chemistry.

Machine learning optimization for hybrid electric vehicle charging in renewable microgrids.

Renewable microgrids enhance security, reliability, and power quality in power systems by integrating solar and wind sources, reducing greenhouse gas emissions. This paper proposes a machine learning approach, leveraging Gaussian Process (GP) and Krill Herd Algorithm (KHA), for energy management in renewable microgrids with a reconfigurable structure based on remote switching of tie and sectionalizing. The method utilizes Gaussian Process (GP) for modeling hybrid electric vehicle (HEV) charging demand. To counteract HEV charging effects, two scenarios are explored: coordinated and intelligent charging. A novel optimization method inspired by the Krill Herd Algorithm (KHA) is introduced for the complex problem, along with a self-adaptive modification to tailor solutions to specific situations. Simulation on an IEEE microgrid demonstrates efficiency in both scenarios. The predictive model yields a remarkably low Mean Absolute Percentage Error (MAPE) of 1.02381 for total HEV charging demand. Results also reveal a reduction in microgrid operation cost in the intelligent charging scenario compared to coordinated charging.

Non-trade-off Changes in Soil Conservation Service and Soil Loss on the Tibetan Plateau Underlying the Impacts of Climate Change and human activities.

Climate change and human activities have significantly influenced soil loss and the soil conservation service, posed threats to regional ecological sustainability. However, the relationships and underlying driving forces between potential soil loss, actual soil loss, and soil conservation service have not been well understood. Utilizing the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, we evaluated the soil conservation service on the Tibetan plateau from 1990 to 2020. We analyzed the spatial and temporal trends and examined the driving factors using linear regression, Pearson correlation, and random forest regression. The soil conservation service exhibited a complex pattern of increase followed by a decrease, with a turning point around 2010. Soil conservation service and soil loss demonstrated non-trade-off changes. The potential soil loss dominated the spatiotemporal patterns of soil conservation service on the Tibetan Plateau. Climatic factors significantly influenced the spatiotemporal patterns of soil conservation service, with annual precipitation emerging as the dominant driving factor, contributing approximately 20%. However, the impacts of human activities became more pronounced since 2010, and the contribution of vegetation to changes in soil conservation service was increased. The impact of the Normalized Difference Vegetation Index (NDVI) on soil conservation service for the grades I, II, and III increased by 13.19%, 3.08%, and 3.41%, respectively. Conversely, in northern Tibet before 2010 and eastern Three-River-Source after 2010, soil conservation service exhibited an increasing trend driven by both climate factors and human activities. Which indicates that the implementation of ecological restoration measures facilitated vegetation improvement and subsequently reduced actual soil loss. This study provides a scientific basis for resource management, land development strategies, and the formulation of ecological restoration measures on the Tibetan Plateau.

Optical frequency multiplication using residual network with random forest regression.

In this work, we present a method for optical frequency multiplication utilizing a hybrid deep learning approach that integrates the Residual Network (ResNet) with the Random Forest Regression (RFR) algorithm. Three different frequency multiplication modulation schemes are adopted to illustrate the method, which can obtain suitable parameters for these schemes. Based on the parameters predicted by the algorithm, the 8-tupling, 12-tupling, and 16-tupling mm-wave signals are generated by numerical simulation. The simulation results show that for 8-tupling frequency multiplication, an OSSR (optical sideband suppression ratio) is 30.73 dB and an RFSSR (radio frequency spurious suppression ratio) of 80 GHz is 42.29 dB. For 12-tupling frequency multiplication, the OSSR is 30.09 dB, and the RFSSR of the 120 GHz mm wave is 36.21 dB. For generating 16-tupling frequency mm-wave, an OSSR of 29.86 dB and an RFSSR of 34.52 dB are obtained. In addition, the impact of amplitude fluctuation and bias voltage drift on the quality of mm-wave signals is also studied.

Urban cemeteries: The forgotten but powerful cooling islands.

Urban parks play a key role in UHI mitigation. However, the role of other prominent types of urban green infrastructure has not been comprehensively studied. Thus, the main objective of this study was to evaluate the role of cemeteries and allotments as cooling islands compared to the well-studied park areas. We assessed the LST of cemeteries, allotments and parks based on Landsat 8 TM images across the five largest German cities during summertime. Random forest regressions explain the LST spatial variability of the different urban green spaces (UGS) with spectral indices (NDVI, NDMI, NDBaI) as well as with tree characteristics (tree type, tree age, trunk circumferences, trunk height or canopy density). As a result, allotments were identified as the hottest UGS with the city means varying between 23.1 and 26.9 °C, since they contain a relatively high proportion of sealed surfaces. The LST spatial variability of allotment gardens was best explained by the NDVI indicating that fields with a higher percentage of flowering shrubs and trees reveal lower LST values than those covered by annual crops. Interestingly, cemeteries were characterized as the coolest UGS, with city means between 20.4 and 24.7 °C. Despite their high proportion of sealed surfaces, they are dominated by old trees resulting in intensive transpiration processes. Parks show heterogeneous LST patterns which could not be systematically explained by spectral indices due to the variability of park functionality and shape. Compared to parks, the tree-covered areas of cemeteries have a higher cooling potential since cemeteries as cultural heritage sites are well-protected allowing old tree growth with intensive transpiration. These findings underline the relevance of cemeteries as cooling islands and deepen the understanding of the role of tree characteristics in the cooling process.

Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.

Introduction: In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems. Methods: Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression. Results and discussion: The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

Machine Learning-Based Interpretable Modeling for Subjective Emotional Dynamics Sensing Using Facial EMG.

Understanding the association between subjective emotional experiences and physiological signals is of practical and theoretical significance. Previous psychophysiological studies have shown a linear relationship between dynamic emotional valence experiences and facial electromyography (EMG) activities. However, whether and how subjective emotional valence dynamics relate to facial EMG changes nonlinearly remains unknown. To investigate this issue, we re-analyzed the data of two previous studies that measured dynamic valence ratings and facial EMG of the corrugator supercilii and zygomatic major muscles from 50 participants who viewed emotional film clips. We employed multilinear regression analyses and two nonlinear machine learning (ML) models: random forest and long short-term memory. In cross-validation, these ML models outperformed linear regression in terms of the mean squared error and correlation coefficient. Interpretation of the random forest model using the SHapley Additive exPlanation tool revealed nonlinear and interactive associations between several EMG features and subjective valence dynamics. These findings suggest that nonlinear ML models can better fit the relationship between subjective emotional valence dynamics and facial EMG than conventional linear models and highlight a nonlinear and complex relationship. The findings encourage emotion sensing using facial EMG and offer insight into the subjective-physiological association.

Real-time monitoring and predictive analysis of VOC flux variations in soil vapor: Integrating PID sensing with machine learning for enhanced vapor intrusion forecasts.

In the rapidly evolving domain of vapor intrusion (VI) assessments, traditional methodologies encompass detailed groundwater and soil vapor sampling coupled with comprehensive laboratory measurements. These models, blending empirical data, theoretical equations, and site-specific parameters, evaluate VI risks by considering a spectrum of influential factors, from volatile organic compounds (VOC) concentrations in groundwater to nuanced soil attributes. However, the challenge of variability, influenced by dynamic ambient conditions and intricate soil properties, remains. Our study presents an advanced on-site gas sensing station geared towards real-time VOC flux monitoring, enriched with an array of ambient sensors, and spearheaded by the reliable PID sensor for VOC detection. Integrating this dynamic system with machine learning, we developed predictive models, notably the random forest regression, which boasts an R-squared value exceeding 79 % and mean relative error near 0.25, affirming its capability to predict trichloroethylene (TCE) concentrations in soil vapor accurately. By synergizing real-time monitoring and predictive insights, our methodology refines VI risk assessments, equipping communities with proactive, informed decision-making tools and bolstering environmental safety. Implementing these predictive models can simplify monitoring for residents, reducing dependence on specialized systems. Once proven effective, there's potential to repurpose monitoring stations to other VI-prone regions, expanding their reach and benefit. The developed model can leverage weather forecasting data to predict and provide alerts for future VOC events.

Potential impact of future climate change on grassland cover in Burkina Faso.

The objective of this study was to analyse the potential impact of future climate change on grassland cover in Burkina Faso. MODIS NDVI 250 m time series were used to monitor changes in grassland over 2000-2022. The random forest regression (RFR) model was fit by regressing reference data of grassland cover against current climatic and other environmental predictors to predict the current grassland cover map (2022). Projected climate model data of CMIP6 used under SSP 126 and SSP 585 scenarios were integrated into the fit RFR model to predict future change. The results revealed that grassland areas were largely dominated by non-significant productivity change (55%) during 2000-2022. In this period, grassland area knew more increased productivity (35%) than decrease (10%). Burkina Faso is predicted to face more decreased areas of grassland cover than increase by 2061-2080 under SSP 126 and SSP 585 scenarios. The findings of this study can help to set up appropriate adaptation measures to combat climate change in Burkina Faso.

Bioaerosols in the coastal region of Qingdao: Community diversity, impact factors and synergistic effect.

Atmospheric bioaerosols are influenced by multiple factors, including physical, chemical, and biotic interactions, and pose a significant threat to the public health and the environment. The nonnegligible truth however is that the primary driver of the changes in bioaerosol community diversity remains unknown. In this study, putative biological association (PBA) was obtained by constructing an ecological network. The relationship between meteorological conditions, atmospheric pollutants, water-soluble inorganic ions, PBA and bioaerosol community diversity was analyzed using random forest regression (RFR)-An ensemble learning algorithm based on a decision tree that performs regression tasks by constructing multiple decision trees and integrating the predicted results, and the contribution of different rich species to PBA was predicted. The species richness, evenness and diversity varied significantly in different seasons, with the highest in summer, followed by autumn and spring, and was lowest in winter. The RFR suggested that the explanation rate of alpha diversity increased significantly from 73.74 % to 85.21 % after accounting for the response of the PBA to diversity. The PBA, temperature, air pollution, and marine source air masses were the most crucial factors driving community diversity. PBA, particularly putative positive association (PPA), had the highest significance in diversity. We found that under changing external conditions, abundant taxa tend to cooperate to resist external pressure, thereby promoting PPA. In contrast, rare taxa were more responsive to the putative negative association because of their sensitivity to environmental changes. The results of this research provided scientific advance in the understanding of the dynamic and temporal changes in bioaerosols, as well as support for the prevention and control of microbial contamination of the atmosphere.

Development of the RF-GSEA Method for Identifying Disulfidptosis-Related Genes and Application in Hepatocellular Carcinoma.

Disulfidptosis is a newly discovered cellular programmed cell death mode. Presently, a considerable number of genes related to disulfidptosis remain undiscovered, and its significance in hepatocellular carcinoma remains unrevealed. We have developed a powerful analytical method called RF-GSEA for identifying potential genes associated with disulfidptosis. This method draws inspiration from gene regulation networks and graph theory, and it is implemented through a combination of random forest regression model and Gene Set Enrichment Analysis. Subsequently, to validate the practical application value of this method, we applied it to hepatocellular carcinoma. Based on the RF-GSEA method, we developed a disulfidptosis-related signature. Lastly, we looked into how the disulfidptosis-related signature is connected to HCC prognosis, the tumor microenvironment, the effectiveness of immunotherapy, and the sensitivity of chemotherapy drugs. The RF-GSEA method identified a total of 220 disulfidptosis-related genes, from which 7 were selected to construct the disulfidptosis-related signature. The high-disulfidptosis-related score group had a worse prognosis compared to the low-disulfidptosis-related score group and showed lower infiltration levels of immune-promoting cells. The high-disulfidptosis-related score group had a higher likelihood of benefiting from immunotherapy compared to the low-disulfidptosis-related score group. The RF-GSEA method is a powerful tool for identifying disulfidptosis-related genes. The disulfidptosis-related signature effectively predicts HCC prognosis, immunotherapy response, and drug sensitivity.

Establishment of a random forest regression model to estimate the age of bloodstains based on temporal colorimetric analysis.

Bloodstain age estimation is important in forensic science. Although several studies have used spectroscopy to estimate bloodstain ages, this method has not yet been practically applied due to the need for expensive equipment and low reproducibility. Thus, we aimed to develop a bloodstain age estimation model that can be easily performed using a spectrophotometric colorimeter. First, bloodstains were prepared by placing blood obtained from five healthy volunteers on a plastic plate. The bloodstains were kept on conditions with various brightness and temperatures. Then, each bloodstain was dissolved in saline every 24 h to a final concentration of 1%, measured with a spectrophotometric colorimeter, and subjected to machine learning to generate a random forest regression (RFR) model, and finally, the prediction accuracy of the bloodstain age was verified. We also elucidated the mechanism of the color changes utilizing aminoguanidine, which is an inhibitor of Maillard reaction. Finally, we measured the time-dependent color changes of the blood fluids obtained from healthy volunteers and examined if the method could be potentially applied to estimate postmortem interval (PMI). Our results showed that the RFR model estimated the bloodstain age with no substantial assessment, and it was applicable to bloodstains, regardless of the brightness or temperature. The color changes were affected by the addition of aminoguanidine. Furthermore, the method could be applied to blood fluids, suggesting its potential usefulness for PMI estimation. Considering its feasibility, the present method could potentially be introduced to practical forensic sciences in the near future.

Antiphospholipid antibodies are enriched post-acute COVID-19 but do not modulate the thrombotic risk.

BACKGROUND AND OBJECTIVES: COVID-19-associated coagulopathy, shown to increase the risk for the occurrence of thromboses and microthromboses, displays phenotypic features of the antiphospholipid syndrome (APS), a prototype antibody-mediated autoimmune disease. Several groups have reported elevated levels of criteria and non-criteria antiphospholipid antibodies (aPL), assumed to cause APS, during acute or post-acute COVID-19. However, disease heterogeneity of COVID-19 is accompanied by heterogeneity in molecular signatures, including aberrant cytokine profiles and an increased occurrence of autoantibodies. Moreover, little is known about the association between autoantibodies and the clinical events. Here, we first aim to characterise the antiphospholipid antibody, anti-SARS-CoV-2 antibody, and the cytokine profiles in a diverse collective of COVID-19 patients (disease severity: asymptomatic to intensive care), using vaccinated individuals and influenza patients as comparisons. We then aim to assess whether the presence of aPL in COVID-19 is associated with an increased incidence of thrombotic events in COVID-19. METHODS AND RESULTS: We conducted anti-SARS-CoV-2 IgG and IgA microELISA and IgG, IgA, and IgM antiphospholipid line immunoassay (LIA) against 10 criteria and non-criteria antigens in 155 plasma samples of 124 individuals, and we measured 16 cytokines and chemokines in 112 plasma samples. We additionally employed clinical and demographic parameters to conduct multivariable regression analyses within multiple paradigms. In line with recent results, we find that IgM autoantibodies against annexin V (AnV), ß2-glycoprotein I (ß2GPI), and prothrombin (PT) are enriched upon infection with SARS-CoV-2. There was no evidence for seroconversion from IgM to IgG or IgA. PT, ß2GPI, and AnV IgM as well as cardiolipin (CL) IgG antiphospholipid levels were significantly elevated in the COVID-19 but not in the influenza or control groups. They were associated predominantly with the strength of the anti-SARS-CoV-2 antibody titres and the major correlate for thromboses was SARS-CoV-2 disease severity. CONCLUSION: While we have recapitulated previous findings, we conclude that the presence of the aPL, most notably PT, ß2GPI, AnV IgM, and CL IgG in COVID-19 are not associated with a higher incidence of thrombotic events.

Enhancing Safety and Efficiency in Firefighting Operations via Deep Learning and Temperature Forecasting Modeling in Autonomous Unit.

Firefighters face numerous challenges when entering burning structures to rescue trapped victims, assess the conditions of a residential structure, and extinguish the fire as quickly as possible. These challenges include extreme temperatures, smoke, toxic gases, explosions, and falling objects, which can hinder their efficiency and pose risks to their safety. Accurate information and data about the burning site can help firefighters make informed decisions about their duties and determine when it is safe to enter and evacuate, reducing the likelihood of casualties. This research presents unsupervised deep learning (DL) to classify the danger levels at a burning site and an autoregressive integrated moving average (ARIMA) prediction model to forecast temperature changes using the extrapolation of a random forest regressor. The DL classifier algorithms provide the chief firefighter with an awareness of the danger levels in the burning compartment. The prediction models forecast the rise in temperature from a height ranging from 0.6 m to 2.6 m and the changes in temperature over time at an altitude of 2.6 m. Predicting the temperature at this altitude is critical as the temperature increases faster with height, and elevated temperatures can weaken the building's structural material. We also investigated a new classification method using an unsupervised DL autoencoder artificial neural network (AE-ANN). The prediction data analytical approach included using the autoregressive integrated moving average (ARIMA) with random forest regression implementation. The proposed AE-ANN model, with an accuracy score of 0.869, did not perform as well compared to previous work, with an accuracy of 0.989, at achieving high accuracy scores for the classification task using the same dataset. However, the random forest regressor and our ARIMA models are analyzed and evaluated in this work, while other research has not utilized this dataset, even though it is open-sourced. However, the ARIMA model demonstrated remarkable predictions of the trends of temperature changes in a burning site. The proposed research aims to classify fire sites into dangerous levels and predict temperature progression using deep learning and predictive modeling techniques. This research's main contribution is using a random forest regressor and autoregressive integrated moving average models to predict temperature trends in burning sites. This research demonstrates the potential of using deep learning and predictive modeling to enhance firefighter safety and decision-making processes.

Machine learning-based digital mapping of soil organic carbon and texture in the mid-Himalayan terrain.

Mountain soils have received significant attention due to their profound influence on ecological processes and environmental factors. However, mapping these soils in digital soil mapping technique encounters several challenges, including high local variability, non-linear relationships between environmental covariates and soil properties, limited accessibility in complex topographical settings, and the absence of universally applicable covariates for soil formation. To address these issues, this study integrates soil-forming factors of the scorpan model to map soil organic carbon (SOC) and soil texture in the mid-Himalayas. By considering over 100 environmental covariates, with a focus on terrain parameters relevant to mountainous environments, the study aims to enhance the accuracy of ML regression models through augmentation techniques that overcome data insufficiency. Using augmented soil observations and covariates, a non-parametric random forest regression model is trained and applied to predict soil variables across the study area, generating a continuous fine-resolution map. The model's performance, evaluated against an unknown dataset, was significant with an R-square of 0.80, 0.79, 0.72, and 0.84 for clay, sand, silt, and SOC, respectively. Furthermore, a sensitivity analysis of the environmental covariates and their impact on the model revealed that all the soil-forming factors make a significant contribution to the model's effectiveness. The insights gained from this research contribute to a better understanding of mountain soils and facilitate the development of effective conservation and sustainable management strategies for mountainous regions.

Random forest regression on joint role of meteorological variables, demographic factors, and policy response measures in COVID-19 daily cases: global analysis in different climate zones.

Different sources of factors in environment can affect the spread of COVID-19 by influencing the diffusion of the virus transmission, but the collective influence of which has hardly been considered. This study aimed to utilize a machine learning algorithm to assess the joint effects of meteorological variables, demographic factors, and government response measures on COVID-19 daily cases globally at city level. Random forest regression models showed that population density was the most crucial determinant for COVID-19 transmission, followed by meteorological variables and response measures. Ultraviolet radiation and temperature dominated meteorological factors, but the associations with daily cases varied across different climate zones. Policy response measures have lag effect in containing the epidemic development, and the pandemic was more effectively contained with stricter response measures implemented, but the generalized measures might not be applicable to all climate conditions. This study explored the roles of demographic factors, meteorological variables, and policy response measures in the transmission of COVID-19, and provided evidence for policymakers that the design of appropriate policies for prevention and preparedness of future pandemics should be based on local climate conditions, population characteristics, and social activity characteristics. Future work should focus on discerning the interactions between numerous factors affecting COVID-19 transmission.