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The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI waveform metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI waveform, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI waveform to produce more accurate canopy height or volume estimates. [ABSTRACT FROM AUTHOR]

Dolphins are social animals that depend on sound to communicate, navigate, and find food. The aim of our research was to describe dolphin whistle characteristics and compare whistles between two isolated populations of bottlenose dolphins in New Zealand. We focused on three aspects of dolphin bioacoustics (1) classification of whistles; (2) comparison of the whistles of two discrete bottlenose dolphin populations; and (3) comparison of whistles of dolphin populations from around the world. At Great Barrier Island, the most common type of whistle was upsweep followed by sine, while at Doubtful Sound, convex and sine whistles were the most common whistle types. Random Forest models discriminated between whistles from the two populations with an estimated accuracy of 90%. The key differences were whistle-type contour, duration, and end frequency. Linear Mixed models show that whistle acoustic parameters were influenced by location but not by group size or the presence of immatures. Whistle parameters of both populations fell within the range of multiple populations from around the world. This studyprovides a benchmark for future research into drivers of geographic variation in dolphin vocal communication. [ABSTRACT FROM AUTHOR]

The Storm Water Management Model (SWMM) is a hydrological model for simulating and predicting runoff. Although powerful, SWMM can be computationally demanding. Therefore, we develop machine learning (ML) models to approximate the behavior of SWMM and expedite the task of predicting runoff. We perform a case study for the First Creek watershed in Knoxville, Tennessee, USA. We train ML models using rainfall data and subcatchment characteristics and apply feature engineering and clustering to objectively compare the outputs from SWMM and ML models. The results show that random forests can predict runoff volume accurately, with a Mean Absolute Error (MAE) of 0.006 (0.001) ${10^6}$ 10 6 gallons, where predictions are made almost instantaneously. Hence, our proposed ML-based approach can accurately predict runoff while greatly reducing computational requirements, filling a critical need in the field. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of constructing a nonparametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the regression function as well as a partial recovery of the missing values. The imputation is done through iterative assignation of the missing values to the tree's cells, maximising the CART criterion. A proof of the consistency of the random forest estimator is given in the case where each latent variable is missing completely at random (MCAR). [ABSTRACT FROM AUTHOR]

This research focuses on understanding the spatial variation of Soil Organic Matter (SOM) and pH levels in the North of Morocco. The study employs a comprehensive approach to enhance predictive modelling, incorporating the Boruta algorithm for effective environmental covariates selection and optimizing model parameters through hyperparameter optimization. Utilizing a Random Forest (RF) model with remote sensing indices and topographic features, the research predicts SOM and pH to identify key contributors to their spatial variability. SOM prediction saw significant success, with a notable correlation to remote sensing indices such as the RVI, NDVI, and TNDVI. These indices, indicative of vegetation health and productivity, emerged as primary influencers of SOM. In comparison, the influence of topographic features like elevation, slope, and aspect was found to be less significant. Conversely, predicting pH was challenging due to the minimal spatial variability within the dataset. Addressing this limitation could involve dataset expansion or alternative models for low-correlated data handling. Despite the RF model's limited efficacy in pH prediction, an observable correlation between SOM and pH was identified, consistent with prior research. Areas with higher SOM exhibited lower pH values, indicating relative soil acidification from organic matter decomposition. The study's RF model demonstrated potential in SOM prediction using remote sensing indices, but enhancing pH prediction is essential. Future research may explore dataset expansion, diverse sampling, or testing alternative predictive models for better performance with low-correlated datasets. The study offers valuable insights for advanced predictive model development and enriches understanding of soil management practices. [ABSTRACT FROM AUTHOR]

This article reports on an advanced level physical laboratory experiment designed for college level undergraduate education and for scholars who need specialized training on using and interpreting Laser-Induced Breakdown Spectroscopy. The technical principle, experimental operation and sample preparation of Laser-Induced Breakdown Spectroscopy are introduced in detail. A presentation and discussion of the use of Laser-Induced Breakdown Spectroscopy in the traceability of four common samples is emphasized. Combining Laser-Induced Breakdown Spectroscopy with Machine Learning, two distinct datasets are constructed through the extraction of spectral features. Dimensionality reduction of spectral data is performed using Linear Discriminant Analysis, while the Random Forest model is employed for provenance classification. Finally, the interpretability of the Random Forest model is leveraged to explore the contributions of different spectral elements to provenance tracing. Results demonstrated the system's effectiveness in not only accurately identifying ash types but also in elucidating the influential chemical components, offering significant implications for material analysis and environmental monitoring. On an educational standpoint, this paper will allow any reader, in particular, undergraduate and graduate students, to gain a better understanding of the theory and practice of Laser-Induced Breakdown Spectroscopy and machine learning. [ABSTRACT FROM AUTHOR]

Machine learning techniques have been widely used in the oil and gas industry to improve the qualitative and quantitative characterization of subsurface reservoirs. Because rock properties are strongly influenced by lithological and sedimentological information, lithofacies classification is an important step in 3D reservoir modeling. The aim of this study is to use supervised classification algorithms to predict the spatial distribution pattern of lithofacies classes using borehole and seismic data. In this study, lithofacies classes are distributed away from the wells using a machine-learning classifier. Seismic data attributes extracted from well locations are utilized as training data features in various supervised classification algorithms. Machine learning classifiers trained and evaluated for lithofacies classification include K-nearest neighbors, support vector machine, Gaussian naive Bayes, decision tree, Gradient Boosting, and Random Forests. A number of parameters are optimally determined in order to achieve the highest value of classification accuracy in the model. Comparing machine learning classifiers based on evaluation metrics reveals that ensemble-based decision tree approaches such as Random Forests and Gradient Boosting are the most effective for supervised classification. The results are validated using testing data and have an 80% classification accuracy. The predicted volume of lithofacies classes contributes to improved 3D reservoir modeling for the pre-salt carbonate reservoir. [ABSTRACT FROM AUTHOR]

Cyber-attacks using malicious uniform resource locator (URL) propagation are very common and serious. Statistics indicate that there is a need to research and apply techniques and methods for identifying and preventing malicious URLs. The main objective of this research is to train machine learning models on selected dataset to predict phishing websites based on URL-related features. The accuracy level of each model is measured and compared. Finally, the best performing model will be used to develop a web application that provide internet users with an easy way to check suspicious URLs. We have used five different machine learning models to classify URLs as legitimate or phishing, these models are eXtreme Gradient Boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), Decision Tree, and Random Forest. Finally, we used Voting Classifier to combine the work of Random Forest (RF) algorithm with other two models, Gaussian Naive Bayes, and Logistic Regression, to check if we can increase the accuracy of RF as suggested in the literature, but we found that the accuracy of RF alone was higher than the accuracy of the combined models. This project can be implemented as a browser extension or mobile application to classify suspicious URLs to legitimate or phishing with the use of the saved model. [ABSTRACT FROM AUTHOR]

Spectral diversity (SD) in reflectance can be used to estimate plant taxonomic diversity (TD) according to the Spectral Variation Hypothesis (SVH). However, contrasting relationships between SD and TD have been reported by different studies. Indeed, multiple factors may affect SD, including spatial and spectral scales, vegetation characteristics and the adopted SD computational method. Here, we tested the SVH over 171 plots within a large and heterogeneous forest area in North-Eastern Italy using Sentinel-2 data, aiming at identifying possible factors affecting the strength and direction of SD-TD relationship. SD was determined using 'biodivMapR' (BD) and 'rasterdiv' (RD) R packages and 38 possible combinations of SD indices, at both α (within a community) and β (among communities) levels, and computational parameters accounting for spatial and spectral scales. Information on vegetation structure was either retrieved from ground-based or LiDAR data. A Random Forest approach was used to disentangle the relationships between SD, TD and vegetation structure, and to identify the best combination of SD computational parameters. At the α-level, we found negative relationship between TD and RD SD indices, which was mainly driven by the presence of gaps within the forest canopy. As regards BD, we found that this algorithm reduced background contribution on SD and was able to differentiate major forest types (broadleaves vs conifers), but derived α-SD indices were marginally correlated with α-TD. At the β-level, we observed a statistically significant positive correlation between BD SD indices and TD (maximum r = 0.24). Finally, we found stronger correlations and R2 when SD indices were calculated using smaller computation windows and over a larger pixels extraction area. Our findings suggest that vegetation cover and structure play a major role, with respect to inter-species spectral differences, in determining α-SD, and that SD might better capture differences in species composition at the landscape-level rather than the richness of individual communities. [ABSTRACT FROM AUTHOR]

Red-light running (RLR) is one of the riskiest behaviors at signalized intersections. This study utilizes intersection- and corridor-level characteristics to identify variables that impact the frequency and severity of RLR violations. The severity of RLR is defined based on the time when the violations happened. That is, less severe violations are the ones that occur within 3 seconds of the signal light turning red (RLR3), while more severe violations happen between 3 and 10 seconds of the signal light turning red (RLR10). Results of the zero-inflated negative binomial model showed that an increase in intersection delay and split failure increases the frequency of both types of RLR severities. An increase in the yellow interval, cycle length, and number of lanes reduces the frequency of RLR3 but increases the frequency of RLR10. Furthermore, based on the factor importance analysis conducted through the random forest, it was shown that split failure is one of the most predictive variables for RLR3 and RLR10. The findings of this research could help transportation agencies in different geographic jurisdictions utilize the calibrated models to understand the impact of varying intersection- and corridor-based characteristics on the frequency and severity of RLR. [ABSTRACT FROM AUTHOR]

As distributed and multi-organization Machine Learning emerges, new challenges must be solved, such as diverse and low-quality data or real-time delivery. In this paper, we use a distributed learning environment to analyze the relationship between block size, parallelism, and predictor quality. Specifically, the goal is to find the optimum block size and the best heuristic to create distributed Ensembles. We evaluated three different heuristics and five block sizes on four publicly available datasets. Results show that using fewer but better base models matches or outperforms a standard Random Forest, and that 32 MB is the best block size. [ABSTRACT FROM AUTHOR]

Malnutrition significantly impacts the post-operative process of gynecological cancer patients. A prominent variable for determining perioperative morbidity is the Prognostic Nutritional Index (PNI). To investigate PNI's predictive value on the risk of post-operative infections, we conducted a prospective cohort study involving women who underwent surgery for gynecological malignancies. Out of the 208 patients enrolled, 28 (13.5%) were malnourished and post-operative infections occurred in 43 patients. Notably, there was a significant difference in PNI between patients who developed infections and those who did not (p = 0.027), as well as between malnourished patients and those with normal nutritional status (p = 0.043). Univariate analysis showed that preoperative PNI predicts the risk of post-operative infections better than post-operative white blood cell count (AUC of 0.562 vs 0.375). However, the most accurate diagnostic results in the multivariate analysis were obtained from random forest and classification tree models (AUC of 0.987 and 0.977, respectively). Essentially, PNI and post-operative white blood cell count provided the best information gain according to rank probabilities. In conclusion, PNI appears to be a critical parameter that merits further investigation during the preoperative evaluation of gynecological malignancies. [ABSTRACT FROM AUTHOR]

This study combines data from five studies in a quantitative modeling approach to improve identification of tics and tic disorders using two questionnaires (the Motor or Vocal Inventory of Tics and the Description of Tic Symptoms), administered to parents and children (N = 1,307). Combining final diagnoses (positive or negative for tic disorder) with data from recently developed questionnaires implemented to assist in the identification of tics and tic disorders in children, we investigate methods for predicting positive diagnosis while also identifying which items in the questionnaires are most predictive. Logistic regression and random forest models are compared using various summary statistics. We further discuss the differences in errors (false positives versus false negatives) in the specification of predictive model tuning parameters. Compared to logistic regression models, random forest models provided comparable and often superior predictive abilities and were also more useful in summarizing the contributions to predictions from individual questions. The combined analyses identified a subset of screener questions that were the best predictors of tic disorders; the identified questions differed based on parent or self-report. These results provide information to inform the future development of tools to screen for tics in a variety of healthcare and epidemiological settings. [ABSTRACT FROM AUTHOR]

In recent years, supervised machine learning methods have increased attention to extracting clinically relevant information from complex health data. Ensemble learning methods enable the establishment of more successful models by training multiple learners jointly to solve the same problem. Herein, we aimed to compare the performance of classification algorithms after data preprocessing to problems such as missing data, class noise, and class imbalance that may be encountered in the datasets used to make an accurate disease diagnosis. To this end, we used random forest and weighted subspace random forest as bagging algorithms while additive logistic regression and gradient boosted machines algorithms were used as boosting algorithms. The performance and running time of the algorithms were also calculated. Our findings indicated that the performance of algorithms increased after data preprocessing and the performance of boosting algorithms yielded higher results than the bagging algorithms. We also observed that the boosting algorithms were the longest-running ones. In conclusion, complementing existing studies, our work highlights the importance and effect of using multiple data preprocessing methods together. [ABSTRACT FROM AUTHOR]

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order. [ABSTRACT FROM AUTHOR]

Real-time monitoring of transient stability has gained significant importance in the power system. This paper proposes an advanced hybrid Transient Stability Assessment (TSA) monitoring system, composed of a Convolutional Neural Network (CNN) and Random Forest (RF) classifier which captures the PMU measurements in real-time. CNN utilizes heatmaps for detecting the transient stability status which is hereby used as a feature extractor for the RF classifier. The IEEE-14 and the IEEE-39 bus systems are used as case studies in MATLAB. A wind turbine with a doubly-fed induction generator is also installed to inspect its impact on the power system stability. The efficiency of the system is tested under different contingency scenarios by implementing various faults on distinct locations under dynamic loading conditions. The test results verified that the proposed hybrid CNN-RF (Convolutional Neural Network-Random Forest) model exhibited excellent results in terms of monitoring accuracy, higher precision, and performance in the presence of noise. [ABSTRACT FROM AUTHOR]

This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

Artificial Intelligence is quickly emerging as a technological solution for the agriculture industry to surmount its classical challenges. Artificial Intelligence is facilitating farmers to refine their products and alleviate unfavourable impacts due to the environment. The central concern of this paper is predictive analytics to develop a machine learning model to identify and predict crop yield based on multiple environmental factors. In this paper, a hybrid learner 'RaNN' is proposed that combines the feature sampling and majority voting technique of Random Forest in-combination with the multilayer Feedforward Neural Network to predict the crop yield. Research has also ascertained the essential features responsible for accurate yield prediction. The proposed model works for rice yield prediction, one of the chief grains of India. The region chosen for the work is Punjab, which is among the largest producer states of India for rice. The dataset consists of 15 attributes comprising the weather and agriculture data collected from the Indian Meteorological Department Pune, and Punjab Environment Information System (ENVIS) Center, Government of India. The study has also made a comparative assessment of 'RaNN' with machine learning methods like Multiple Linear Regression, Random Forest, Decision Tree, Boosting Regression, Support Vector Machine Regression, Ensemble Learner, and Artificial Neural Network. Our model RaNN has listed a better prediction accuracy with minimal error among the other techniques providing a 98% correlation between the actual and the predicted yield. Abbreviations: AI – Artificial Intelligence; ANN – Artificial Neural Network; BR – Boosting Regression; Chem Fert – chemical fertilisers; DT – Decision Tree; EL – Ensemble Learner; ENVIS – Punjab Environment Information System; GBM – Stochastic Gradient Boosting Method; GPS – Global Positioning System; HMAX – highest maximum temperature in degrees C; IMD – Indian Meteorological Department; L1 – Lasso regression; L2 – Ridge regression; LMIN – lowest minimum temperature; ML – Machine Learning; MAE – Mean Absolute Error; MEVP – mean evaporation in mm; MLR – Multiple Linear Regression; MMAX – mean maximum temperature in degrees C; MMIN – mean minimum temperature in degrees C; MSSH – Mean sunshine duration in hours; MWS – mean wind speed in km/h; P1 – number of days with precipitation (0.1–0.2 mm); P2 – number of days with precipitation (greater than or equal to 0.3 mm); RaNN – Hybrid RF-ANN model; RMSE – Root Mean Squared Error; $${R^2}$$ R 2 – Coefficient of determination; RD – number of rainy days; RF – Random Forest; SVM Reg – Support Vector Machine Regression; TMRF – total rainfall per month in mm [ABSTRACT FROM AUTHOR]

Precise estimation of Sea Surface Salinity (SSS) is of prime importance to know marine physical and biochemical processes. In this manuscript we have utilized the Random Forest (RF) and Support Vector Regression (SVR) to estimate the SSS concentration. Particularly, we have utilized the global SSS values provided by the Copernicus Marine Services. We have also extracted the Aqua MODIS and Sentinel-3 data from the Google Earth Engine platform. The complete range of SSS varies approximately from 6 psu to 38 psu which enables the global application of the dataset. We have also studied the importance of different wavelength bands and their significance to SSS using the RF model. It is found that wavelengths around 400 nm have their highest significance due to the sensitiveness to Chromophoric Dissolved Organic Matter (CDOM) and other related constituents. For the regression analysis we have also utilized the Laplacian kernel in the SVR. It is found that the SVR with Laplace kernel outperforms the RF and SVR with Radial Basis Function (RBF) kernel. The trained algorithms are used to estimate the SSS content over the Bay of Bengal and the Arabian Sea regions which show interesting variations in SSS content due to diverse climatological conditions. [ABSTRACT FROM AUTHOR]

The invasion and expansion of Spartina alterniflora have led to a certain degree of degradation in the ecological environment of coastal wetlands. To discuss the impacts of Spartina alterniflora expansion on the tidal channels, we took the third core area of the Dafeng Elk National Nature Reserve in Yancheng, Jiangsu Province as the study area, retrieved the spatial distribution of Spartina alterniflora by random forest algorithm based on long time series of Landsat and Sentinel-2 images, analysed the expansion models. The tidal channels were obtained by the image segmentation based on MSAVI and visual interpretation and the spatiotemporal evolution traits were finally analysed. The results revealed that the area and perimeter of Spartina alterniflora are continuously increasing, with a wide-ranging invasion. The coverage area peaks at 13.4%, making it the dominant species in the study area. When excluding the influence of human activities, a significant negative correlation emerges between the invasion rate and compactness of Spartina alterniflora and the spatio-temporal evolution of tidal channels. Faster invasion rates and greater compactness correspond to a deceleration in tidal channel morphology development. Additionally, we also found that the growth of Spartina alterniflora has a positive influence on the growth and reproduction of elk. [ABSTRACT FROM AUTHOR]

Imagery acquired by the Moderate-resolution Imaging Spectroradiometer (MODIS) provides a global archive of dailyNormalized Difference Snow Index (NDSI) at 500 m nominal resolution since the year 2000. While Sentinel-2 (S2) NDSI provides an increased spatial resolution of 20 m since the year 2015, the temporal resolution amounts to only 5 days and thus lacks the high temporal resolution of MODIS. Efforts to combine NDSI datasets for an increased temporal and spatial resolution have so far focused on the deriving binary snow cover maps or combining data from other sensors. In contrast, we produce fine scale (20 m) fractional snow cover (FSC) by downscaling MODIS NDSI to S2 resolution. Random forest regression predicts S2 NDSI based on dynamic features (MODIS NDSI, day-of-year) and static, topographic features for an alpine study site. Subsequently, FSC is derived from S2 NDSI. Cross-validation results in R2 of 0.795 and RMSE of 0.155 for FSC and outperforms common resampling methods. Multi-annual S2 NDSI metrics are able to slightly improve model accuracy. Our results suggest that combining topographical data and low-resolution NDSI allows to produce daily, high-resolution S2 NDSI and FSC and improve fine scale characterization of snow cover dynamics in mountain landscapes. [ABSTRACT FROM AUTHOR]

As valued members of forest ecosystems, fast-growing tree species are of great importance for protecting natural forests, fulfilling the demand for raw wood material, and preserving biodiversity and ecosystem services. In this regard, multi-temporal monitoring and evaluation of poplar planted areas are essential for decision makers and planners to keep the inventory maps of planted areas up-to-date and track the temporal changes accurately. Using a spectral library, available spectral knowledge can be incorporated into inventory mapping and image classification processes. In this study, in situ spectral measurements of five Populus (four clones and one species) planted sites in Türkiye were collected at three phenological stages (early, mid, and late vegetation) and a novel spectral library was created after completing strict processing stages. Analysis of the spectral reflectances of the Populus species revealed that their spectral properties in the visible region are similar, whereas the discrimination in the near-infrared and shortwave infrared (SWIR) regions was noticeably higher. Two types of applications were conducted to effectively use the spectral library. First, the spectral library was used to validate the poplar class labels of the sites visited during the field campaign. The effectiveness of Worldview-3 imagery with eight SWIR bands was evaluated by pairwise comparison of the spectral curves of the sample fields with those of the spectral library. Second, the spectral library facilitated the extraction of poplar samples from Sentinel-2 imagery in an unvisited region, and a supervised classification process was conducted using the random forest algorithm. The results revealed that the Populus species (Samsun and I-214) could be delineated with high accuracy (>70%) using 10-band Sentinel-2 imagery, and object-based classification outperformed pixel-based classification by approximately 5% for the poplar types. The developed spectral library stands as a valuable asset for studies focused on identifying and classifying Populus species using high-resolution imagery. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging effectively distinguishes land cover classes with discriminative spectral-spatial characteristics. However, hyperspectral image (HSI) classification encounters three significant challenges: the curse of dimensionality, the scarcity of training samples, and the necessity to incorporate spatial information within the classification process. The large number of redundant and irrelevant spectral bands in HSI can negatively impact the classification performance. Furthermore, the annotation process for training samples is expensive and time-consuming. To address these challenges, this work presents a novel semi-supervised approach for hyperspectral image dimensionality reduction using the Mayfly Algorithm (MA). The proposed method aims to preserve the most informative spectral bands while leveraging both labelled and unlabelled samples in the process. The MA algorithm effectively balances exploration and exploitation strategies, enhancing population diversity and mitigating challenges such as premature convergence and local optima. Additionally, the inclusion of structural variation and thresholding (SVT) in the Bitonic filter enables the incorporation of spatial features during the classification phase. This improves the separability of pixels belonging to different objects and leads to significant improvements in classification performance. Finally, two machine learning classifiers, namely Random Forest (RF) and Support Vector Machine (SVM), are applied at the pixel level for hyperspectral image classification. Extensive experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed approach compared to state-of-the-art algorithms like Harmony Search (HS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and more with an average improvement of 3.2%. [ABSTRACT FROM AUTHOR]

Classification accuracy of any classifier can be enhanced by performing the classification on selected informative features. Features selection methods are generally approached for the purpose. Combining multiple models known as ensemble method emerged as prominent method for achieving classification accuracy. These techniques can be considered for variable selection for accuracy gain and interpretability of a classifier. we propose an ensemble of penalized logistic models (EPLM) for feature selection. EPLM employs Lasso, adaptive Lasso and elastic net for feature selection. EPLM is applied to high dimensional microarray data sets and simulated data sets. State-of-the-art classifiers, Lasso, Random Forest (RF), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN) are employed for assessment of EPLM. Experimental comparisons reveal that significant improvement in classification accuracy is achieved for all the classifiers considered. The proposed method is also compared to the Ensemble of subset of KNN classifiers (ESKNN). In comparison to ESKNN EPLM achieves better performance accuracy for all the classifiers on simulated and microarray data sets. Moreover, it is also observed that EPLM has selected significantly smaller number of features as compared to the full feature set. [ABSTRACT FROM AUTHOR]

Previous machine learning approaches fail to consider race and ethnicity and social determinants of health (SDOH) to predict childhood asthma exacerbations. A predictive model for asthma exacerbations in children is developed to explore the importance of race and ethnicity, rural-urban commuting area (RUCA) codes, the Child Opportunity Index (COI), and other ICD-10 SDOH in predicting asthma outcomes. Insurance and coverage claims data from the Arkansas All-Payer Claims Database were used to capture risk factors. We identified a cohort of 22,631 children with asthma aged 5–18 years with 2 years of continuous Medicaid enrollment and at least one asthma diagnosis in 2018. The goal was to predict asthma-related hospitalizations and asthma-related emergency department (ED) visits in 2019. The analytic sample was 59% age 5–11 years, 39% White, 33% Black, and 6% Hispanic. Conditional random forest models were used to train the model. The model yielded an area under the curve (AUC) of 72%, sensitivity of 55% and specificity of 78% in the OOB samples and AUC of 73%, sensitivity of 58% and specificity of 77% in the training samples. Consistent with previous literature, asthma-related hospitalization or ED visits in the previous year (2018) were the two most important variables in predicting hospital or ED use in the following year (2019), followed by the total number of reliever and controller medications. Predictive models for asthma-related exacerbation achieved moderate accuracy, but race and ethnicity, ICD-10 SDOH, RUCA codes, and COI measures were not important in improving model accuracy. [ABSTRACT FROM AUTHOR]

This article shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number of predictor variables grows sub-exponentially with the sample size, under natural 0-norm and 1-norm sparsity constraints. The theory applies to a wide range of models, including (ordinary or logistic) additive regression models with component functions that are continuous, of bounded variation, or, more generally, Borel measurable. Consistency holds for arbitrary joint distributions of the predictor variables, thereby accommodating continuous, discrete, and/or dependent data. Finally, we show that these qualitative properties of individual trees are inherited by Breiman's random forests. A key step in the analysis is the establishment of an oracle inequality, which allows for a precise characterization of the goodness of fit and complexity tradeoff for a mis-specified model. for this article are available online. [ABSTRACT FROM AUTHOR]

Detecting building rooftops with very high-resolution (VHR) images is an important issue in many fields, including disaster management, urban planning, and climate change research. Buildings with varying geometrical features are challenging to detect accurately from VHR image due to complicated image scenes containing spectrally similar objects, illumination, occlusions, viewing angles, and shadows. This study aims to detect building rooftops with high accuracy using a new framework that includes VHR image, visible band difference vegetation index, digital surface and elevation models, the terrain ruggedness and the topographic position index. Five distinct feature sets were generated in order of importance by exposing the ten related stacking features to a feature selection procedure using the maximum relevance minimum redundancy method. Then, Auto-Encoder, k-NN, decision tree, RUSBoost, and random forest machine learning algorithms were utilized for binary classification. Random forest yielded the highest accuracy (97.2% F-score, 98.72% accuracy) when all features (F10) were used, while decision tree was the least successful (59.16% F-score, 83.56% accuracy) for RGB feature set (FRGB). It was revealed that classification of F10 with random forest increased F-score by about 23% compared to classification with FRGB. Additionally, McNemar's tests showed no statistically significant difference between random forest vs k-NN and decision tree vs RUSBoost. [ABSTRACT FROM AUTHOR]

An aggressive driving state impacts drivers' decisions, which could potentially lead to accidents. Real-time recognition of driving state is particularly important for improving road safety. However, the majority of modeling in existing studies relies on a single algorithm, which may lead to unreliable predictions. This paper proposes a stacking ensemble aggressive driving state recognition model using electroencephalography (EEG), which is able to combine different heterogeneous classification algorithms. Five types of classification algorithms and their variants are tested and compared to identify suitable base classifiers. All of these classifiers are optimized by Bayesian optimizer before the comparison. Three stacking ensemble recognition models using different meta-classifiers (i.e., logistic regression, random forest, and AdaBoost) and an equal-weight voting ensemble recognition model are established. The aforementioned recognition models are evaluated by using a dataset collected from a car-following simulated driving experiment. Fast Fourier transformation (FFT) and wavelet packet transformation (WPT) are adopted to extract features from raw EEG data. The results suggest that the stacking ensemble recognition models outperform the best single (i.e., support vector machine) model; the random Forest stacking recognition model achieves the best performance and the accuracy is increased from 81.21% to 84.23% using FFT features and from 86.45% to 87.38% using WPT features. [ABSTRACT FROM AUTHOR]

Anthropogenic land alterations in Maun village have transformed natural vegetation into urban infrastructure, including pavements, and residential and commercial areas, leading to elevated Land Surface Temperature (LST). This urban expansion resulted from economic growth driven by population increase and tourism-related development. This study aims to evaluate the relationship between Land Use and Land Cover Changes (LULCCs) and LST. Utilising Landsat 5-TM and Landsat-8 data from 1990, 2000, and 2020, we employed a random forest algorithm for supervised classification, generating Land Use and Land Cover (LULC) maps. The mono-window algorithm was used to extract LST data from Landsat 5 and 8 images, alongside Normalised Difference Vegetation Index (NDVI) maps. s regression analysis assessed the LST-NDVI correlation. Results indicate that urban LULCCs significantly contribute to rising LST. Minimum and maximum LST values for 1990, 2000, and 2020 were 18.6°C, 22.8°C, 22.6°C, and 26.7°C, 34.5°C, and 42.1°C, respectively. NDVI values ranged from −0.2 to 0.56 in 1990, −0.17 to 0.58 in 2000, and 0.07 to 0.46 in 2020. Roads, pavements, barren land, and built-up areas displayed the highest LST (44.6°C), while water bodies and healthy vegetation exhibited the lowest (16.1°C). Additionally, NDVI exhibited a negative correlation with LST. Our findings emphasise the role of human activities in exacerbating LST. They highlight the need for regulated urban growth patterns to ensure sustainable development. Moreover, quantifying spatiotemporal variations in LULC, LST, and NDVI holds importance for conserving land resources and enhancing land use planning policies. Policymakers and city planners can utilise this research to mitigate heat stress effects and promote sustainable urban environments by evaluating distribution maps of LULC, NDVI, and LST. [ABSTRACT FROM AUTHOR]

The rapid detection of moisture content in tomato leaves can provide an important basis for tomato cultivation. Compared with spectroscopy technology, image technology, and dielectric detection technology, electrical impedance spectroscopy (EIS) has the potential to analyze the internal components of leaf microstructure qualitatively and quantitatively, and the characteristics of fast detection and low cost. The moisture content in tomato leaves was detected effectively and accurately at the anthesis of the first inflorescence using the EIS method. The influence of reduction of moisture content on the impedance characteristics and equivalent circuit parameters was analyzed. The frequencies of different impedance characteristics were selected based on the out-of-bag (OOB) importance. Different regression algorithms, including partial least square regression (PLSR), back-propagation neural network (BPNN), and random forest (RF), were used for estimating the leaf moisture content (LMC). The LMC models based on different impedance characteristics and different methods were established and compared. The results demonstrated that the impedance modulus in the low-frequency region, the extracellular resistance (Re), and the intracellular resistance (Ri) increased first and then decreased with the decrease in the moisture content of the tomato leaves. The RF model established with the combination data had the best performance, with the coefficient of determination (R2), normalized root-mean-square error (NRMSE), and the ratio of performance of deviation (RPD) for prediction being 0.8861, 6.02%, and 2.95, respectively. Therefore, the EIS method combined with RF was a feasible and effective method to predict LMC, providing a promising tool for moisture content detection. [ABSTRACT FROM AUTHOR]

The application of machine learning in geotechnical problems has grown rapidly. However, an issue in phenomena, such as rockburst, is the limited and imbalanced datasets, which deteriorate the reliability of machine learning algorithms. This research proposes a methodology for overcoming such issues by incorporating both the undersampling and oversampling concepts along with the use of numerical modelling. The core of the methodology is the substitution of clusters containing rockburst instances among similar materials by representative synthetic instances derived from numerical modelling on the basis of the BHP index. The models are tuned and calibrated so that their mechanical properties are aligned with the clusters' centroids and mimic their nearest neighbours in terms of stresses and rockburst intensities. Subsequently, they are subjected to a variety of stresses to fill all rockburst classes. As a result, a balanced synthetic dataset in terms of material and class participation is formed, which is used to train a Random Forest algorithm to predict rockburst. The results attained indicate the potential of the proposed methodology in enhancing the learning process and thus provide reliable long-term rockburst predictions. [ABSTRACT FROM AUTHOR]

Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique. [ABSTRACT FROM AUTHOR]

To avoid the expensive quadratic programming in the L2-loss support vector regression (SVR) model, smooth approximation and iteratively reweighted least square (IRLS) techniques were introduced in literature, resulting in smoothed SVR (SSVR) and IRLS-SVR. However, for nonlinear models, SSVR and IRLS-SVR both involve operations with matrices and vectors of the same size as the training set. Thus, as the training set becomes large, nonlinear SSVR and IRLS-SVR both need long training time and large memory. To further alleviate the training cost, this paper projects the original data into a low dimensional space via random Fourier feature. The inner product of the random Fourier features of two data points is approximately the same as the kernel function evaluated at these two data points. Hence, it is possible to use a linear model in the new low dimensional space to approximate the original nonlinear model, and consequently the time/memory efficient linear training algorithms could be applied. This paper applies the idea of random Fourier features to nonlinear SSVR and IRLS-SVR, and our testing results on real-world datasets show that, the introduction of random Fourier features makes SSVR and IRLS-SVR achieve similar prediction accuracy as the original nonlinear version with substantially higher time efficiency. [ABSTRACT FROM AUTHOR]

Urban agriculture continues to play a key role in addressing food insecurity and poverty across developing countries. This study used Landsat 5 imagery to map the spatial and temporal distribution of cropland in Harare Metropolitan in Zimbabwe using the Random Forest classifier in Google Earth Engine. Results show that the area under cultivation fluctuated considerably between 1985 and 2019 and several factors account for this trend. The results of this study illustrate the increasing importance of remote sensing in assessing cropland in urban areas that provides insights into the contribution of urban agriculture to food security. [ABSTRACT FROM AUTHOR]

In real-world scenarios, most of the collected data are disorganised, and the probability of getting messy data is more in the data collection. However, retrospective data collected from the health sector have reasonable missing values. For example, an Arthritis Profile Data (APD) dataset collected from a clinical laboratory has several missing values. This incomplete dataset is converted to a reasonable complete dataset using imputation methods. There are several approaches to missingness in the literature, but how well they work depends on the missingness techniques. Unfortunately, there has not been enough investigation into missing data on autoimmune diseases. Although many studies have discussed and compared different imputation techniques, some have limited their comparison without in-depth analyses of the features' imputed and original values. Moreover, some studies lack the identity of the missing category of the features in the dataset. Therefore, this paper fills the gap by describing and exploring APD dataset analysis. The objective is to find a suitable imputation technique for the APD dataset. Compared with other imputation techniques, such as mean imputation, median imputation, mode imputation, random value imputer, kNN imputer, MICE imputer and random forest imputer, the random value imputer and mode imputation techniques are suggested to be the best using the proposed degree of proximity and degree of residual procedures respectively. [ABSTRACT FROM AUTHOR]

Erythemato-squamous diseases (ESD) diagnosis is a significant challenge in dermatology. It is divided into six categories. Artificial intelligence models have been applied to categorize these categories. Artificial intelligent models are black boxes in nature. The objective of this study is to unbox the black-box behavior and interpret the decision-making. Random Forest and XGBoost models are applied on a standard dataset with SHAP value to get interpretability and causability of decision. The Random Forest model had a classification accuracy of 98.21%. Integration of explainability increase the transparency of result and identify the root cause of the disease in the subject. A comprehensive quantitative study will help to adopt artificial intelligence in healthcare with ethical issues like transparency, causability, and interpretability of diagnosis. [ABSTRACT FROM AUTHOR]

In recent years, the solitary reasons for mortality in the world are chronic diseases such as heart disease, diabetes, and chronic kidney disease. These diseases should be diagnosed earlier; however, the technique is costly as well as it leads to many complications. Considering the complexity, datamining performs a major part in accurately classifying chronic disease. A new approach to classify chronic disease is by merging the multi-objective firefly optimisation algorithm (MOFFA) and random forest (RF). The main goal is generating an efficient and heterogeneous decision trees, while determining the optimum training sets to run at the same time. Rather utilising traditional approach like bootstrap, multi-objective firefly optimisation algorithm and random forest algorithm are proposed in this method. As a result, to train random forest, various training sets are generated with alternative instances and attributes. As a result, the performance of random forests can be improved and thus the prediction accuracy. The effectiveness of the proposed method is explored by juxtaposing the effectiveness of the proposed method with other classifiers for different datasets. The proposed work is tested on six UCI datasets. According to the findings, the proposed MOFFA-RF algorithm surpass other classifiers by the accuracy of 88% on CKD, 87% on CVD, 82% on diabetes, 88% on hepatitis, 88% on WBC, and 76% on ILPD. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind turbine output power is important to ensure safe, stable, and efficient wind power generation. In this study, a hybrid prediction model based on a combination of segmented multivariate polynomial and extreme gradient boosting (XGBoost) under different operating conditions is proposed. First, the wind farm supervisory control and data acquisition (SCADA) data are processed using isolated forest for outliers, in order to improve model prediction accuracy. Then, random forest is used for feature selection, where wind speed, rotor speed, pitch angle, wind direction and temperature are found to be the most relevant features related to the output power. Next, based on the analysis of the wind turbine characteristic curves, its operating state is divided into three phases: constant power (CP), constant speed (CS), and maximum power point tracking (MPPT). Different polynomials are established for each phase, the CP phase is of order 5, the CS phase is of order 7, and the MPPT phase is of order 4. Finally, the XGBoost model is trained for power prediction with polynomial new features for each phase. The segmented multivariate polynomial-XGBoost model has a mean absolute percentage error (MAPE) of 0.04, a root mean square error (RMSE) of 49, and a coefficient of determination R2 of 0.99. Compared to the single XGBoost model and the unsegmented polynomial-XGBoost model, the MAPE decreases by 42% and 33% respectively, while RMSE decreases by 50% and 12% respectively, resulting in high prediction accuracy. [ABSTRACT FROM AUTHOR]

High-performance and low-cost emission characteristic prediction is very crucial for diesel engine design optimization and emission aftertreatment control and diagnosis. In this paper, a novel hybrid model that combines Convolutional Neural Network (CNN) and Gated Recurrent Unit Network (GRU) was proposed to predict the emission characteristic from diesel engines, encompassing CO, THC, CO2, NOx, exhaust temperature and exhaust pressure. Nine operating parameters from WHTC and WHSC cycles, including speed, torque, intake pressure, intake flow, intake temperature, oil pressure, fuel rate, oil temperature, water temperature, were considered as inputs. Firstly, the importance of each variable is evaluated by Random Forests algorithm to determine the optimal inputs for each emission characteristic parameter and reduce redundancy. Then the effect of different hyperparameters on the model performance was discussed in detail and PSO algorithm was used to obtain the optimal hyperparameters. Finally, the CNN-GRU hybrid model was assessed for its generalization and compared with ANN, LSTM and GRU models. The result demonstrates that the CNN-GRU hybrid model with PSO optimization has excellent prediction performance in either the training dataset or the validation dataset. The average value of R² is 0.993 in the training dataset and 0.985 on the validation dataset. In the test dataset, the average R² is 0.961, showing a minor decrease of 3.19% and 2.47% compared to the training and validation dataset, respectively. This indicates that the CNN-GRU hybrid model has strong generalization ability. Compared with other algorithms in the test dataset, the CNN-GRU hybrid model exhibits better comprehensive performance, with the average R² value exceeding that of ANN, LSTM and only GRU by 5.96%, 2.69% and 3.23%, respectively. [ABSTRACT FROM AUTHOR]

In this study machine learning (ML) models have been employed to predict the higher heating value (HHV) of biomass by utilizing input variables derived from ultimate, proximate, and structural analyses. In total, 180 models were developed, with 124 utilizing ultimate analysis data, 28 based on proximate analysis, and 28 relying on structural analysis. Various ML techniques, including polynomial models (SOP), support vector machines (SVM), random forest regression (RFR), and artificial neural networks (ANN), were employed for analysis. The study found that ANN models, when "fed" with FC and VM data, provided considerable accuracy in prediction results, with the best results obtained with 2-12-1 architecture (R2=0.96). In addition, a separate model configuration that processed inputs on biomass constituents such as cellulose, lignin, and hemicellulose showed remarkable agreement with empirical data. Additional findings revealed that the models created using SOP (R2=0.95), SVM (R2=0.95), and RFR (R2=0.90) demonstrated minimal discrepancies when predicting HHV. This study provides significant insights into the investigation of biomass analysis techniques employing ML tools, paving the way for future research aimed at constructing a robust tool for HHV prediction. Subsequent models may explore integrating inputs from diverse analysis methods and leveraging advanced machine learning techniques to enhance accuracy further [ABSTRACT FROM AUTHOR]

Digital light processing (DLP) is renowned for its precision, but the challenge lies in the identification of optimal print parameters to minimise print errors and enhance overall print accuracy. This study introduces a groundbreaking approach by integrating 'Random Forest' (RF) models with DLP printing to construct a predictive model for printing errors to achieve unparalleled precision. We conducted experiments using common commercial resins to print, resulting in a comprehensive dataset of 690 experimental datasets. Consequently, we validated this approach by fabricating Y-type microfluidic structures with a minimum feature size printing of 2 µm and herringbone mixer structures with feature sizes of 21.2 µm. We successfully controlled the average print error below 2.3 µm. The 'RF' model was also extended from the printing of microfluidics (2.5D) to complex spatial three-dimensional (3D) Triple-Periodic Minimal Surface (TPMS) structures. We have successfully achieved the printing of TPMS-Gyroid and TPMS-Diamond structures with a minimum feature size of 20 µm, while the printing errors were all kept within 1.5 µm. This study underscores the generalizability and immense potential of integrating machine learning techniques with high-precision DLP printing, offering valuable insights for future research in the realm of high-precision and expeditious 3D printing. [ABSTRACT FROM AUTHOR]

Rice is one of the most essential food crops in the world and accurate estimation of rice yield is a major content of agricultural research. Recently, scholars have used machine learning algorithms for rice yield estimation. However, there are few studies on rice yield prediction based on rice phenological stages. In this study, a method for rice yield prediction on the basis of rice phenology analysis was proposed. For this study, the cumulative NDVI and EVI based Logistic regression curves were carried out to determine the phenological period. Comparing several regression models, the results of the random forest regression model developed using phenology-based regression analysis performed better. The R2 of training and validation samples were 0.96 and 0.95, respectively, with RMSE of 0.06 ton/ha. This method is feasible for governments to predict rice yield and make farm risk management decisions. [ABSTRACT FROM AUTHOR]

There is strong interest to improve the therapeutic potential of gold nanoparticles (GNPs) while ensuring their safe development. The utility of GNPs in medicine requires a molecular-level understanding of how GNPs interact with biological systems. Despite considerable research efforts devoted to monitoring the internalisation of GNPs, there is still insufficient understanding of the factors responsible for the variability in GNP uptake in different cell types. Data-driven models are useful for identifying the sources of this variability. Here, we trained multiple machine learning models on 2077 data points for 193 individual nanoparticles from 59 independent studies to predict cellular uptake level of GNPs and compared different algorithms for their efficacies of prediction. The five ensemble learners (Xgboost, random forest, bootstrap aggregation, gradient boosting, light gradient boosting machine) made the best predictions of GNP uptake, accounting for 80–90% of the variance in the test data. The models identified particle size, zeta potential, GNP concentration and exposure duration as the most important drivers of cellular uptake. We expect this proof-of-concept study will foster the more effective use of accumulated cellular uptake data for GNPs and minimise any methodological bias in individual studies that may lead to under- or over-estimation of cellular internalisation rates. [ABSTRACT FROM AUTHOR]

Background: Machine learning algorithms that can quickly and easily estimate skin permeability (Kp) are increasingly being used in drug delivery research. The linear free energy relationship (LFER) developed by Abraham is a practical technique for predicting Kp. The permeability coefficients and Abraham solute descriptor values for 175 organic compounds have been documented in the scientific literature. Purpose: The purpose of this project was to use a publicly available dataset to make skin permeability predictions using the random forest and XBoost regression techniques. Methods: We employed Pandas-based methods in JupyterLab to predict permeability coefficient (Kp) from solute descriptors (excess molar refraction [E], combined dipolarity/polarizability [S], overall solute hydrogen bond acidity and basicity [A and B], and the McGowan's characteristic molecular volume [V]). Results: The random forest and XG Boost regression models established statistically significant association between the descriptors and the skin permeability coefficient. [ABSTRACT FROM AUTHOR]

Since forest fire is one of the most dangerous natural disasters and presents serious threats to the local ecological environment, economic growth, and public safety, it is essential to carry out accurate and real-time forest fire monitoring. In this study, a forest fire monitoring method that combines a threshold-based algorithm and random forest (RF) model using Himawari-8 data is proposed. The threshold-based algorithm employs the solar zenith angle to adaptively determine the potential fire point judgement threshold to extract possible fire points. The RF model constructed with spectral features and spatio-temporal information is subsequently utilized to eliminate pseudo-fire points from the results of the threshold-based algorithm. To eliminate fire points in non-forest areas, post-processing is performed using land cover data. Five fire occurrence moments in the research area are selected to verify the identification accuracy. The results reveal that the overall accuracy and the overall comprehensive evaluation values are 97.36% and 0.913, respectively, which demonstrates that the proposed method is capable of accurately identifying forest fire points and providing an effective means for forest fire monitoring. [ABSTRACT FROM AUTHOR]

In this paper, we develop a new dataset on indirect state rivalry relations based on different matrix calculations for 154 countries, over the period 1970-2015, and demonstrate their importance in explaining civil wars. After controlling for spatial distances between rival countries, we demonstrate that 1) the presence of direct and indirect rivals exerts a positive and significant effect on the risk of civil war; 2) decreasing levels of military capacity of one state relative to its rivals (direct and indirect) also influence the probability of internal conflict. Finally, we confirm the significance of our indicators by using on the one hand the random forest algorithm, a machine learning method using decision trees and on the other hand, the Kaplan-Meier estimate for the duration of the civil wars. [ABSTRACT FROM AUTHOR]

Quickly and accurately obtaining information on rice planting areas is crucial for maintaining food security and social stability. Based on the time series of Sentinel-1A images, this study proposes an automatic rice mapping method called the Dynamic Threshold Time-Weighted Dynamic Time Warping method (DT-TWDTW). This method consists of two parts. First, rice samples are extracted automatically by a dynamic threshold method based on the unique flooding signal before and after rice transplanting. Then, the Time-Weighted Dynamic Time Warping (TWDTW) method is used for rice mapping based on the cumulative cost matrix between unknown pixels and rice samples. The results show that the DT-TWDTW method can achieve satisfactory mapping accuracy with an overall accuracy of 94.6%, higher than random forest (89.9%), and support vector machine (91.7%). This study provides a new approach and perspective for remote sensing monitoring of rice, especially for some regions without prior phenological knowledge and collection of the rice samples. [ABSTRACT FROM AUTHOR]

Efficiently obtaining tobacco planting area is significant for rationally allocating tobacco resources and realizing the balance between supply and demand. However, tobacco fields are characterized by fragmentation in hilly areas, which brings various challenges to extracting the tobacco planting area. A 16-square-kilometre tobacco planting region in Xuancheng City, Anhui Province, China, was selected as the study area in this paper. First, the single-temporal full-feature sets (STFFS), a time-series full-feature set (TFFS) and a time-series optimal-feature set (TOFS) were constructed from multi-period Sentinel-2 images, respectively. Then, we applied Random Forest (RF), Support Vector Machine (SVM), Neural Network Classification (NNC) and Maximum Likelihood Classification (MLC) to classify the feature sets and compare the classification accuracy. The experimental results demonstrate that: (1) The best growth stage of tobacco remote sensing recognition is the mulching film phase of the spherical plant stage (ST1 period). (2) The classification accuracy indicates that TOFS outperforms the STFFS. (3) TOFS can still maintain the same classification accuracy as TFFS with fewer feature dimensions. (4) The RF classifier has great stability and robustness, which achieve an overall accuracy (OA) of 90.65%, 93.50%, and 93.09% based on the feature sets of ST1 period, TFFS, and TOFS, respectively. [ABSTRACT FROM AUTHOR]

A methodology for assessing crash risk using vehicle driving trajectories based on data mining techniques was developed in this study. A variety of safety indicators reflecting the characteristics of traffic and road geometric conditions were evaluated in terms of their capability of capturing hazardous traffic flow. Comprehensive data preparation was conducted by matching driving trajectory data obtained from in-vehicle digital tachograph devices and crash data to classify and analyze hazardous and normal traffic flows. The random forest approach was adopted to quantify the importance of safety indicators. The crash risks were evaluated using the logistic regression model and multivariate adaptive regression splines model based on the set of safety indicators with high importance. The results show that the dangerous driving events rate and driving volatility indicators were found to be particularly significant in identifying hazardous conditions. The multivariate adaptive regression splines model showed better performance and a classification accuracy of 86% was achieved. The proposed methodology will be useful for deriving effective countermeasures to prevent crashes, which is the backbone of proactive traffic safety management. [ABSTRACT FROM AUTHOR]

Real-time prediction of crash risk can support traffic incident management by generating critical information for practitioners to allocate resources for responding to anticipated traffic crashes proactively. Unlike previous studies using archived traffic data covering a limited highway environment such as a segment or corridor, this study uses a statewide live traffic database from HERE to develop real-time traffic crash prediction models. This database provides crowdsourced probe vehicle data that are high-resolution real-time traffic speed for the entire freeway network (nearly 2,000 miles) in Alabama. This study aims to use machine learning models to predict crash risk on freeways according to pre-crash traffic dynamics (e.g., mean speed, speed reduction) along with static freeway attributes. Traffic speed characteristics were extracted from the HERE database for both pre-crash and crash-free traffic conditions. Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) were developed and compared. Separate models were estimated for three major crash types: single-vehicle, rear-end, and sideswipe crashes. The model prediction accuracy indicated that the RF models outperform other models. Models for rear-end crashes are found to have greater accuracy than other models, which implies that rear-end crashes have a significant relationship with pre-crash traffic dynamics and are more predictable. The traffic speed factors that are ranked high in terms of feature importance are the speed variance and speed reduction prior to crashes. According to partial dependence plots, the rear-end crash risk is positively related to the speed variance and speed reductions. More results are discussed in the paper. [ABSTRACT FROM AUTHOR]