Explainable Machine Learning to Predict the Construction Cost of Power Plant Based on Random Forest and Shapley Method by Suha Falih Mahdi Alazawy, Mohammed Ali Ahmed, Saja Hadi Raheem, Hamza Imran, Luís Filipe Almeida Bernardo, Hugo Alexandre Silva Pinto

“…Performance measures, comprising the coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), were used to evaluate and contrast the performance of the implemented models. …”
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Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach by Mehmet Ali Cengiz, Dilek Sabancı

“…The best model was estimated for both Test MSE and GCV criteria by examining the error of measurement criteria, variable importance averages, and frequencies of the knot values for each model. …”
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SP-RF-ARIMA: A sparse random forest and ARIMA hybrid model for electric load forecasting by Kamran Hassanpouri Baesmat, Farhad Shokoohi, Zeinab Farrokhi

“…This methodology, termed SP-RF-ARIMA, is evaluated against existing approaches; it demonstrates more than 40% reduction in mean absolute error and root mean square error compared to the second-best method.…”
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Spatiotemporal Bayes model for estimating the number of hotspots as an indicator of forest and land fires in Kalimantan Island, Indonesia by FADILLAH ROHIMAHASTUTI, ANIK DJURAIDAH, HARI WIJAYANTO

“… Forest and land fires often occur on the island of Kalimantan and have a widespread impact on neighboring countries. …”
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Joint-Pixel Inversion for Ground Phase and Forest Height Estimation Using Spaceborne Polarimetric SAR Interferometry by Zenghui Huang, Jingyu Gao, Xiaolei Lv, Xiaoshuai Li

“…Quantitative evaluations in forest height estimation show that the proposed method achieves a lower mean error (1.23 m) and RMSE (3.67 m) than the existing method (mean error: 3.09 m; RMSE: 4.70 m), demonstrating its improved reliability.…”
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An Intelligent Crow Search Optimization and Bi-GRU for Forest Fire Detection System Using Internet of Things by Syed Abdul Moeed, Bellam Surendra Babu, M. Sreevani, B. V. Devendra Rao, R. Raja Kumar and Gouse Baig Mohammed

“…The high accuracy and low error rate underscore the potential of this system to be a valuable tool in mitigating the risks associated with forest fires, ultimately contributing to the preservation of natural ecosystems.…”
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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

“…The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. …”
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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. …”
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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. …”
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Integration of UAV Multispectral Remote Sensing and Random Forest for Full-Growth Stage Monitoring of Wheat Dynamics by Donghui Zhang, Hao Qi, Xiaorui Guo, Haifang Sun, Jianan Min, Si Li, Liang Hou, Liangjie Lv

“…The results demonstrated that the NDRE and TVI indices were the most effective indices for monitoring wheat growth. The random forest model exhibited superior predictive accuracy, with a mean squared error (MSE) significantly lower than that of traditional regression models, particularly during the flowering and ripening stages, where the prediction error for plant height was less than 1.01 cm. …”
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Study on rapid determination method of ash content in wheat flour based on stochastic forest regression model by LIU Yanqun, XIAO Fugang, CHEN Caihong

“…The final determination result was obtained by calculating the arithmetic mean, achieving rapid determination of ash content in wheat flour.ResultsThe method was basically consistent with the actual results, with a measurement error of less than 0.01 g/100 g and a repeatability fluctuation difference of less than 0.01 g/100 g. …”
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Modeling of CO₂ Efflux from Forest and Grassland Soils Depending on Weather Conditions by Sergey Kivalov, Irina Kurganova, Sergey Bykhovets, Dmitriy Khoroshaev, Valentin Lopes de Gerenyu, Yiping Wu, Tatiana Myakshina, Yakov Kuzyakov, Irina Priputina

“…To increase the magnitude of the model resolutions, we controlled the slope and intercept of the linear model comparison between the measured and modeled data through the change in R₀—CO₂ efflux at Tsoil = 0 °C. The mean bias error (MBE), root-mean-square error (RMSE), and determination coefficient (R²) were employed to assess the quality of the model’s performance. …”
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Tropical Forest Carbon Accounting Through Deep Learning-Based Species Mapping and Tree Crown Delineation by Georgia Ray, Minerva Singh

“…Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. …”
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Random-forest-based task pricing model and task-accomplished model for crowdsourced emergency information acquisition by Wenxiang Li, Shengqun Chen, Lijin Lin, Li Chen

“…Our simulation results demonstrate that the proposed method has an average reduction in Mean Squared Error (MSE) by 44.16 % for task pricing and an average increase in accuracy of 17.71 % for task-accomplished prediction compared to traditional regression models. …”
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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

“…Unlike traditional hierarchical soil classification, pHRF conditions the probabilities of finer taxonomic levels based on their parent levels and removes implausible predictions (identified as errors) using field-validated soil taxa, improving prediction intervals. …”
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An Improved Sparse Bayesian Learning SAR Tomography Method and its Application for Forest Vertical Structure Inversion by Jie Wan, Changcheng Wang, Peng Shen, Yonghui Wei

“…The results demonstrate that the proposed method achieved high-resolution SAR tomography imaging outcomes even within a limited baseline span. In terms of forest structure parameter inversion, the root mean square error (RMSE) of inverted forest height is 2.58 and 4.16 m compared to LiDAR measurements, while the RMSE of inverted underlying topography is 1.77 and 5.49 m. …”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…Experimental results show that the YOLOv8n-Seg-ASB model achieves enhanced combined segmentation performance, with a 14.3 % increase in mAP@0.5, a 17.35 % reduction in model parameters, and an inference speed of 82.2 FPS. The mean error in impurity rates between the RF monitoring model and manual count was 6.14 %, with an average detection time of 1.43 seconds. …”
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General Framework for Georeferencing and Interpretation of Multi-Resolution LiDAR Data for Fine-Scale Forest Inventory by H. Hanafy, S.-Y. Shin, A. M. Eissa, Y. Hany, S. Park, S. Fei, A. Habib

“…Accurate forest inventory is critical for sustainable management, ecological assessment, and biomass estimation. …”
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Leaf carbon nitrogen and phosphorus concentrations in dominant trees across China’s forests from 2005 to 2020 by Chenxi Li, Honglin He, Xiaoli Ren, Qian Xu, Shiyu Dong, Zining Lin, Luxiang Lin, Zexin Fan, Yongbiao Lin, Juxiu Liu, Qingkui Wang, Anzhi Wang, Ruiying Chang, Zongqiang Xie, Lingli Liu, Fusun Shi

“…The dataset underwent rigorous quality control, including unit harmonization, error checking, and outlier detection, and is provided in accessible CSV format. …”
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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. …”
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