Watershed scale soil moisture estimation model using machine learning and remote sensing in a data-scarce context by Marcelo Bueno, Carlos Baca García, Nilton Montoya, Pedro Rau, Hildo Loayza

“…Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). …”
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Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. Comparative analysis unequivocally demonstrates the superior predictive performance of the random forest model, achieving a remarkable R-squared value of 97.4 % and a root mean square error of 165. …”
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Impact of Parameters and Tree Stand Features on Accuracy of Watershed-Based Individual Tree Crown Detection Method Using ALS Data in Coniferous Forests from North-Eastern Poland by Marcin Kozniewski, Łukasz Kolendo, Szymon Chmur, Marek Ksepko

“…The accurate detection of individual tree crowns and estimation of tree density is essential for effective forest management, biodiversity assessment, and ecological monitoring. …”
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<i>In situ</i> and <i>ex situ</i> variability of phenological and morphological features in <i>Caltha palustris</i> L. under the conditions of the West Siberian forest steppe by Т. I. Fomina

“…Morphometric data were processed in the PAST program using statistical indicators: the arithmetic mean with an error (M ± mM), minimum and maximum values of the trait (lim), and coefficient of variation (Cv). …”
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Choosing blocks for spatial cross-validation: lessons from a marine remote sensing case study by Andy Stock, Andy Stock

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Cereal and Rapeseed Yield Forecast in Poland at Regional Level Using Machine Learning and Classical Statistical Models by Edyta Okupska, Dariusz Gozdowski, Rafał Pudełko, Elżbieta Wójcik-Gront

“…For the studied crops, all models had mean absolute errors and root mean squared errors not exceeding 6 dt/ha, which is relatively small because it is under 20% of the mean yield. …”
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Machine learning-based stem taper model: a case study with Brutian pine by Fadime Sağlam

“…The results show that the XGBoost model outperforms all other approaches, demonstrating superior predictive accuracy with minimal error, as indicated by lower root mean square error (RMSE), mean absolute error (MAE), and bias values. …”
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PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE by Delvian Christoper Kho, Hindriyanto Dwi Purnomo, Hendry Hendry

“…Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). …”
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Explainable AI analysis for smog rating prediction by Yazeed Yasin Ghadi, Sheikh Muhammad Saqib, Tehseen Mazhar, Ahmad Almogren, Wajahat Waheed, Ayman Altameem, Habib Hamam

“…Key performance metrics include a Mean Squared Error of 0.2269, R-Squared (R2) of 0.9624, Mean Absolute Error of 0.2104, Explained Variance Score of 0.9625, and a Max Error of 4.3500. …”
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Machine Learning Model Coupled with Graphical User Interface for Predicting Mechanical Properties of Flax Fiber by T. Nageshkumar, Prateek Shrivastava, L. Ammayapan, Manisha Jagadale, L. K. Nayak, D. B. Shakyawar, Indran Suyambulingam, P. Senthamaraikannan, R. Kumar

“…In this study, a total of 432 patterns of input and output parameters obtained from laboratory experiments were used to develop machine learning algorithms (Random forest, support vector, and XGBoost). Among the machine learning models, random forest regressor yielded high R2 value, low mean squared error (MSE), and mean absolute error (MAE). …”
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Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms by Mintae Kim, Muharrem A. Senturk, Liang Li

“…The algorithms are trained and evaluated using metrics such as the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). …”
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Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam by HUANG Song, WU Jie, FANG Zhanchao, CHU Huaping, WU Yan'gang, XUE Zilong, HE Linbo

“…The results show that three models for predicting crack opening degree are successfully established based on the crack opening degree dataset measured in 2022. The random forest model has the best predictive ability (determination coefficient (<italic>R</italic>²) is 0.995; root mean square error (<italic>E</italic>_RMS) and mean absolute error (<italic>E</italic>_MA) are 0.174 mm and 0.124 mm, respectively), followed by the stepwise regression model (<italic>R</italic>² is 0.989; <italic>E</italic>_RMS<italic> </italic>and <italic>E</italic>_MA<italic> </italic>are 0.192 mm and 0.151 mm). …”
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Supervised and unsupervised machine learning approaches for tree classification using multiwavelength airborne polarimetric LiDAR by Zhong Hu, Songxin Tan

“…The Decision-Tree approach shows a re-substitution error of 0.14 % and a k-fold loss error of 0.57 % for 2,106 tree samples; and the clustering methods provide accuracies at about 80 %. …”
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Evaluating the RMR correlation with the rock mass wave velocity using the meta-heuristics algorithms by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…Deep estimation capability analyses of the proposed GA, TRR and GA-TRR models were performed using the performance evaluation metrics, scatter plots, error histogram, Taylor diagram and regression error characteristic curve. …”
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Data Mining Techniques in Decision Making by Amna Sajid, Basit Amin

“…Decision Tree, Naive Bayes, SMO, bagging, and Random Forest were chosen for precision, recall, and F-measure. …”
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Uncertainty quantification in tree structure and polynomial regression algorithms toward material indices prediction by Geng-Fu He, Pin Zhang, Zhen-Yu Yin

“…In the regions of sparse data, predicted uncertainty becomes larger as errors increase, demonstrating the validity of UQ. …”
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Performance evaluation of machine learning techniques for heart disease prediction: An overview by Dhanashri Shankar Karande, Shailendrakumar Mahadeo Mukane

“…Creating a system that accurately predicts heart disease with minimal errors is essential. Consequently, machine learning is vital for predicting the risk of future cardiopathy by analysing the patient's health conditions and past medical history to decrease the possibility of mortality from heart disease. …”
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Machine Learning Classifiers Based Classification For IRIS Recognition by Bahzad Taha Chicho, Adnan Mohsin Abdulazeez, Diyar Qader Zeebaree, Dilovan Assad Zebari

“…Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained. …”
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Interpretation of digital imagery to estimate juvenile stand attributes in managed boreal stands, density, stocking and height by Douglas E.B. Reid, Jevon Hagens

“…Forest regeneration monitoring is critical to inform forest management planning, evaluate silvicultural efficacy, and determine achievement of renewal standards in managed forests. …”
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Comparative Analysis of Oversampling and SMOTEENN Techniques in Machine Learning Algorithms for Breast Cancer Prediction by Tri Yulian, Erliyan Redy Susanto

“…SVM proved more effective in identifying both classes with minimal error, particularly when combined with oversampling. …”
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