Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Therefore, the BWO is proposed to optimize these parameters, with sample entropy and error reconstruction serving as key fitness function indicators. …”
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Monitoring vegetation dynamics in Qinling–Daba mountains during 2001–2023 using an improved two-leaf model and remote-sensing datasets by Enjun Gong, Jing Zhang, Jun Wang

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Predicting Minimum Temperatures of Plastic Greenhouse During Strawberry Growing in Changfeng, China: A Comparison of Machine Learning Algorithms and Multiple Linear Regression by Xuelin Wang, Qinqin Huang, Dong Wu, Jinhua Xie, Ming Cao, Jun Liu

“…RF, BP performed much better than MLR, as it showed much lower error indices (AE and RMSE) and higher R² than MLR. …”
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Downscaling Satellite Night-Time Light Imagery While Addressing the Blooming Effect by Nikolaos Tziokas, Ce Zhang, Alexandros Tziokas, Qunming Wang, Peter M. Atkinson

“…During the RF regression, the <italic>R</italic>² increased and the root-mean-squared error decreased for both study regions when accounting for the PSF. …”
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Method for Extracellular Electrochemical Impedance Spectroscopy on Epithelial Cell Monolayers by Athena Chien, Colby Lewallen, Hanna Khor, Analia Cegla, Rongming Guo, Adrienne Watson, Chris Hatcher, Nael McCarty, Kapil Bharti, Craig Forest

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Comparative Analysis of RF, SVR with Gaussian Kernel and LSTM for Predicting Loan Defaults by Konstantinos Kofidis, Cătălina Lucia Cocianu

“…This research reports an experimental-based comparative evaluation of three ML and DL models—Long Short-Term Memory (LSTM) networks, Random Forest (RF), and Support Vector Regression (SVR)—to assess their efficacy in forecasting loan defaults. …”
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Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites by Harshit Sharma, Gaurav Arora, Raj Kumar, Suman Debnath, Suchart Siengchin

“…This study presents the prediction of hardness based on machine learning models for both PP/CNT and LDPE/CNT composite materials, and the results show that the Random Forest model consistently performs better than the others models in context with performance metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Rate of determination (R2) values. …”
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House Value Estimation using Different Regression Machine Learning Techniques by Tarek Ghamrawi, Müesser Nat

“…Comparisons of different models such as linear regression, Ridge regression, Lasso regression, Elastic Net, Decision Tree, Random Forest, Gradient Boosting, and XGBoost. The models were evaluated using R-squared (R²) and Mean Absolute Error (MAE) as performance metrics. …”
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STL-DCSInformer-ETS: A Hybrid Model for Medium- and Long-Term Sales Forecasting of Fast-Moving Consumer Goods by Yecheng Ma, Lili He, Junhong Zheng

“…Through numerical experiments, the method demonstrates excellent performance by achieving a 35.9% reduction in Mean Squared Error and a 21.4% decrease in Mean Absolute Percentage Error, significantly outperforming traditional methods. …”
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Applying a Four-Way Factorial Experimental Model to Diagnose Optimum kNN Parameters for Precise Aboveground Biomass Mapping by Chinsu Lin, Nova D. Doyog

“…The k-nearest neighbors (kNN) algorithm is a versatile tool for mapping forest attributes. However, the effects of using inadequate reference plots for modeling have not been thoroughly investigated. …”
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A Comparative Study between Different Machine Learning Algorithms for Estimating the Vehicular Delay at Signalized Intersections by Yazan Alatoom, Abdallah Al-Hamdan

“…Consequently, this study aimed to compare a wide array of machine learning algorithms, including Artificial Neural Networks (ANN), Random Forest (RF), decision tree, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), AdaBoost, Gradient Boost, XGBoost, and Partial Least Squares (PLS) regression. …”
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PIDLF+: A Novel Adaptive PIDLF for Forestry Height Mapping Based on Single-Baseline PolInSAR Data by Dandan Li, Hailiang Lu, Mercedes E. Paoletti, Juan M. Haut, Juntao Gu, Chao Li, Weipeng Jing

“…The experiments show that the proposed <monospace>PIDLF+</monospace> works well in different forest terrains and improves accuracy. It achieved a root-mean-square error (RMSE) of 8.69 m and a coefficient of determination (<inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>) of 0.94 in the Lop&#x00E9; site, an RMSE of 11.31 m and an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.90 in the Pongara site, and an RMSE of 8.86 m and an <inline-formula><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> of 0.92 in the Rabi site.…”
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Enhancing stem volume estimation for savanna species using variable-exponent taper equation and close-range photogrammetry by Finagnon Gabin Laly, Gilbert Atindogbe, Gbèdonou Michée Amos Sohou, Hospice Afouda Akpo, Noël Houédougbé Fonton

“…Stem volume estimation is crucial in forest ecology and management, particularly for timber harvesting strategies and carbon stock assessments. …”
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What diameter? What height? Influence of measures of average tree size on area-based allometric volume relationships by Yilin Wang, John A. Kershaw, Mark J. Ducey, Yuan Sun, James B. McCarter

“…The overall best equation used quadratic mean diameter, Lorey’s height, and density (root mean square error ​= ​5.26 ​m3⋅ha−1; 1.9 % relative error). The best equation without density used mean diameter of the largest trees needed to calculate a stand density index of 400 and the mean height of the tallest 400 trees per ha (root mean square error ​= ​32.08 ​m3⋅ha−1; 11.8 % relative error). …”
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Developing and evaluating satellite-derived phenology and physiology indicators for modeling annual gross primary productivity variability by Hanliang Gui, Qinchuan Xin, Xuewen Zhou, Ying Sun, Yongjian Ruan, Wei Wu, Zhenhua Xiong, Yuhang Tian, Kun Xiao

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Why is Multi-Business Forestry Needed to Overcome the Low Performance of Forestry Governance and Food Security in Indonesia? by Suryanto Suryanto, Dodik Ridho Nurrochmat, Suria Darma Tarigan, Iskandar Zulkarnaen Siregar, Ishak Yassir, Mangarah Silalahi, Irdika Mansur, Rhett D. Harisson, Agus Wahyudi, Lutfy Abdulah

“… The 0.6% contribution of the forestry sector to GDP is considered very low despite 64.1% of Indonesia's land area being allocated as forests. Most of the 64.8% production forest allocated is not yet optimized for strengthening national food security, in which Indonesia is ranked 65th in the world. …”
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Extraction of individual tree attributes using ultra-high-density point clouds acquired by low-cost UAV-LiDAR in Eucalyptus plantations by Mei Zhou, Chungan Li, Zhen Li

“…Context Eucalyptus plantation forests constitute the largest expanse of planted broad-leaved forests worldwide. …”
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Characterization of Intsia spp. from Indonesia using microsatellite markers by Purnamila Sulistyawati, Sapto Indrioko, Widiyatno, AYPBC Widyatmoko

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Effect of bin width on variogram model accuracy: a case study of teak tree volume specific to Solomon clone in Tawau, Sabah, Malaysia by Johannah Jamalul Kiram, Rossita Mohamad Yunus, Yani Japarudin, Mahadir Lapammu, Olivier Monteuuis, Doreen Kim Soh Goh

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Estimation of aboveground biomass of savanna trees using quantitative structure models and close-range photogrammetry by Finagnon Gabin Laly, Gilbert Atindogbe, Hospice Afouda Akpo, Noël Houédougbé Fonton

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