An AutoML-Powered Analysis Framework for Forest Fire Forecasting: Adapting to Climate Change Dynamics by Shuo Zhang, Mengya Pan

“…Wildfires pose a serious threat to ecosystems and human safety, and with the backdrop of global climate change, the prediction of forest fires has become increasingly important. Traditional machine learning methods face challenges in forest fire prediction, such as difficulty identifying feature parameters, manual intervention in model selection, and hyperparameter tuning, which affect prediction accuracy and efficiency. …”
Get full text

In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…Mean squared error (MSE) and root mean square error (RMSE) were employed to assess the primary performance indicators of the models, while mean absolute error (MAE) and the R-squared value were used to evaluate the goodness of fit of the models.Results: The RMSE, MAE and MSE obtained for the Spark-based implementation were lower, compared to the MapReduce-based implementation, although these low values indicate high prediction accuracy. …”
Get full text

Maturity Prediction in Soybean Breeding Using Aerial Images and the Random Forest Machine Learning Algorithm by Osvaldo Pérez, Brian Diers, Nicolas Martin

“…However, information for making predictions in the current growing season using models fitted in previous years is still necessary. Using the Random Forest machine learning algorithm and time series of RGB (red, green, blue) and multispectral images taken from a drone, this work aimed to study, in three breeding experiments of plant rows, how maturity predictions are impacted by a number of factors. …”
Get full text

Intelligent irrigation strategy model for farmland using dung beetle optimization-random forest algorithms by Wenwen Hu, Yong Liu, Jun An, Shipu Xu, Zhiwen Zhou, Mingming An, Xiaokun Guo, Xiang Ma, Wenfei Jiang, Yunsheng Wang

“…The obtained results demonstrated that the DBO algorithm significantly could enhance the Random Forest (RF) model's predictive accuracy, the Mean Absolute Error (MAE) and the Mean Square Error (MSE) were reduced to 0.30321 and 0.16382 respectively, the coefficient of determination (R²) had increased to 0.86255. …”
Get full text

Development and Validation of Quantile Regression Forests for Prediction of Reference Quantiles in Handgrip and Chair‐Stand Test by Giulia Giordano, Luca Mastrantoni, Francesco Landi, The Lookup 8+ Study Group

“…After a 70/20/10 split in training, validation and test set, a quantile regression forest (QRF) was trained. Performance metrics were R‐squared (R2), mean squared error (MSE), root mean squared error (RMSE) and mean Winkler interval score (MWIS) with 90% prediction coverage (PC). …”
Get full text

Random Forest versus Support Vector Machine Models’ Applicability for Predicting Beam Shear Strength by Hayder Riyadh Mohammed Mohammed, Sumarni Ismail

“…In the quantitative term, the minimal root mean square error value was attained (RMSE = 89.68 kN).…”
Get full text

Quantifying and mitigating bias and increased variability when using large-scale estimates of forests for subdomains by Jordan Golinkoff, Mauricio Zapata-Cuartas, Emily Witt, Adam Bausch, Donal O’Leary, Reza Khatami, Wu Ma

“…The application of this method relies on user-defined levels of risk and inventory confidence combined with the distribution of observed error. This method allows remote sensing estimates of carbon stocks to be applied to forest carbon offset quantification. …”
Get full text

Explainable forecasting of air quality index using a hybrid random forest and ARIMA model by Anuradha Yenkikar, Ved Prakash Mishra, Manish Bali, Tabassum Ara

“…This study presents a hybrid forecasting framework that combines the strengths of Random Forest Regression (RFR) and Autoregressive Integrated Moving Average (ARIMA) models to improve AQI prediction accuracy while maintaining model transparency. …”
Get full text

Learning deep forest for face anti-spoofing: An alternative to the neural network against adversarial attacks by Rizhao Cai, Liepiao Zhang, Changsheng Chen, Yongjian Hu, Alex Kot

“…In this paper, we have proposed a novel solution for FAS against adversarial attacks, leveraging a deep forest model. Our approach introduces a multi-scale texture representation based on local binary patterns (LBP) as the model input, replacing the grained-scanning mechanism (GSM) used in the traditional deep forest model. …”
Get full text

InSAR-based estimation of forest above-ground biomass using phase histogram technique by Chuanjun Wu, Peng Shen, Stefano Tebaldini, Mingsheng Liao, Lu Zhang

“…This paper introduces a method for estimating forest above-ground biomass (AGB) using the Interferometric SAR (InSAR)-based Phase Histogram (PH) technique. …”
Get full text

Estimating ages of white‐tailed deer: Age and sex patterns of error using tooth wear‐and‐replacement and consistency of cementum annuli by Daniel J. Storm, Michael D. Samuel, Robert E. Rolley, Thomas Beissel, Bryan J. Richards, Timothy R. Van Deelen

Get full text

Counting animals in orthomosaics from aerial imagery: Challenges and future directions by Ismael V. Brack, Camila Ferrara, German Forero‐Medina, Enrique Domic‐Rivadeneira, Omar Torrico, Kevin Thomas Wanovich, Benjamin Wilkinson, Denis Valle

Get full text

IMPROVING CLUSTER ACCURACY IN TUITION FEES: A MULTILAYER PERCEPTRON NEURAL NETWORK AND RANDOM FOREST APPROACH by Sumin Sumin, Prihantono Prihantono, Khairawati Khairawati

“…In addition, the Random Forest model successfully identified the most influential input variables in STF classification. …”
Get full text

Pharmacist-led surgical medicines prescription optimization and prediction service improves patient outcomes - a machine learning based study by Xianlin Li, Xianlin Li, Xiunan Yue, Lan Zhang, Xiaojun Zheng, Nan Shang

“…The Random Forest (RF) model performed the best (AUC = 0.893) and retained high accuracy with 12 features (AUC = 0.886). …”
Get full text

Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression by Qile Ding, Yiren Wang, Yu Zheng, Fengyang Wang, Shudong Zhou, Donghui Pan, Yuchun Xiong, Yi Zhang

“…This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in a study area in southeast China, we first use random forest regression to predict and optimize variogram parameters. …”
Get full text

Refining the Forest Vegetation Simulator for projecting the effects of spruce budworm defoliation in the Acadian Region of North America by Cen Chen, Aaron Weiskittel, Mohammad Bataineh, David A. MacLean

“…The Forest Vegetation Simulator (FVS) is an individual-tree growth model widely used in the US and parts of Canada, which has been developed to predict stand dynamics in response to various disturbance-causing agents. …”
Get full text

A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence by Hatef Dastour, Quazi K. Hassan

“…Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. …”
Get full text

Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties by Chengcheng Xu, Jingyi Huang, Alfred E. Hartemink, Nathaniel W. Chaney

“…Here, we present a pruned Hierarchical Random Forest (pHRF) framework to map soil taxa and properties over the National Ecological Observatory Network (NEON) sites in the Contiguous United States (CONUS). …”
Get full text

High-Resolution Mapping of Litter and Duff Fuel Loads Using Multispectral Data and Random Forest Modeling by Álvaro Agustín Chávez-Durán, Miguel Olvera-Vargas, Inmaculada Aguado, Blanca Lorena Figueroa-Rangel, Ramón Trucíos-Caciano, Ernesto Alonso Rubio-Camacho, Jaqueline Xelhuantzi-Carmona, Mariano García

“…Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. …”
Get full text

Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

“…From the machine learning results, random forest algorithm outperformed other methods in predicting streamflow, with a mean square error of 0.02 and a coefficient of determination of 0.98. …”
Get full text