A case study comparing approaches to mask satellite-derived bathymetry by Galen Richardson, Anders Knudby, Yulun Wu, Mohsen Ansari

“…Lawrence River, and a Random Forest model using neighbouring pixel information to predict SDB. …”
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An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers by Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha, Seiyed Mossa Hosseini

“…SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. …”
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Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 s... by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński

“…Models from gradient boosting, CatBoost, and random forest machine learning (ML) algorithms were used to guarantee an accurate analysis, along with the artificial neural network regressions. …”
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Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features by Rodrigo Oliveira Almeida, Richardson Barbosa Gomes da Silva, Danilo Simões

“…In default mode, we ran 25 popular algorithms through the database and compared them based on accuracy and error metrics. Although the combination models performed well, the Random Forest model performed better in the default mode with an accuracy of 0.933. …”
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Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields by Larona Keabetswe, Yiyin He, Chao Li, Zhenjiang Zhou

“…Motivated by the robustness of deep learning models, this study employed two hybrid models that integrate Convolution Neural Network with either Random Forests (CNN-RF) or Support Vector Machine (CNN-SVR) to estimate potato ETc act using a limited set of input features. …”
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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Finally, five kinds of machine learning methods, random forest, extreme gradient boosting, light gradient boosting machine, linear support vector classifier and support vector machine, were used to establish classification prediction models, and error-correcting output codes were used to optimize each model. …”
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Using intelligence techniques to automate Oracle testing by Nour Sulaiman, safwan hasson

“…The model was trained using the random forest algorithm and using the convolutional neural network. …”
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Does digital soil mapping prediction performance of soil texture improve when adding uncertain field texture estimates? A study based on clay content by Anne C. Richer-de-Forges, Songchao Chen, Gerard B.M. Heuvelink, Stephan van der Westhuizen, Thomas G. Orton, Hocine Bourennane, Dominique Arrouays

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Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. …”
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Stacked ensemble model for accurate crop yield prediction using machine learning techniques by Ramesh V, Kumaresan P

“…The ensemble model’s performance was assessed using several metrics, including a Mean Absolute Error of 7.20 tons/hectare, Mean Square Error of 15570.32 tons ^2 /hectare ^2 , Root Mean Square Error of 124.78 tons/hectare, and Coefficient of Determination (R ^2 Score) of 0.98. …”
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Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…The results showed that: 1） The Beggs & Bill model and Gray model exhibited poor applicability for predicting the bottom-hole flowing pressure of deep CBM wells, while the single-phase gas model demonstrated reduced overall error as water production declined. Predictions using the neural network method were more accurate, with a relative error of less than 10% compared to measured values. 2） Using Kendall's tau-b correlation analysis, the discrete dominant factor was identified as the microstructural position, primarily located in uplifted positive structural zones, with the secondary factor being fracture development, categorized mainly as “well-developed” or “developed.” 3） By combining lasso regression-random forest- decision tree algorithm to iteratively eliminate irrelevant factors, the continuous dominant factors influencing productivity were ranked in descending order as: ash content, average construction discharge rate, total sand volume pumped, flowback rate at gas breakthrough, net pay thickness, acoustic travel time, gamma ray log value, average construction pressure, percentage of 100-mesh sand, and average gas measurement value. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…Among the evaluated ML models, ANN and Random Forest Regressor performed the best. The ANN model demonstrated superior predictive capability, achieving a Root Mean Squared Error (RMSE) of 2.34, a Mean Absolute Error (MAE) of 1.24, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a Coefficient of Determination (R2) of 0.97. …”
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Navigating the missing data maze: exploring multiple imputation techniques for environmental performance index data by Muhammed Haziq Muhammed Nor, Mohd Aftar Abu Bakar, Noratiqah Mohd Ariff, Wan Hanna Melini Wan Mohtar, Bernard Kok Bang Lee

“…Sensitivity analysis revealed that MissForest and k NN were more stable and consistent than MICEForest across all error metrics when parameters were adjusted. …”
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True Leaf Area Index Retrieval Using Terrestrial LiDAR for Broadleaf Trees via Novel Multiinclined-Planes Method by Yuyang Guo, Shihua Li, Hao Tang, Ze He

“…True leaf area index (LAIt) is a more crucial and efficient structural parameter to characterize the photosynthetic capacity of broadleaf forests than the concept of effective leaf area index, which is the predominant form retrieved by remote sensing. …”
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Constitutive modelling and microstructural analysis of 92W-5Co-3Ni alloy subjected to high strain rate testing at elevated temperatures by Suswanth Poluru, Aarjoo Jaimin, Nitin R. Kotkunde, Swadesh Kumar Singh, Amit Kumar, Ashutosh Panchal, Prabhu Gnanasambandam

“…Subsequently, the Arrhenius constitutive model and machine-learning-based Random Forest model (RF) were developed to predict flow stress. …”
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Estimation of Biophysical Parameters of Forage Cactus Under Different Agricultural Systems Through Vegetation Indices and Machine Learning Using RGB Images Acquired with Unmanned A... by Gabriel Italo Novaes da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Wagner Martins dos Santos, Alan Cézar Bezerra, Elisiane Alba, Marcos Vinícius da Silva, Jhon Lennon Bezerra da Silva, Luciana Sandra Bastos de Souza, Gabriel Thales Barboza Marinho, Abelardo Antônio de Assunção Montenegro, Thieres George Freire da Silva

“…The prediction analysis using the Random Forest algorithm, highlighting DM, which presented a mean absolute error of 1.39, 0.99, and 1.72 Mg ha⁻¹ in experimental units I and II, III, and IV, respectively. …”
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Development of Machine Learning Models for Estimating Metabolizable Protein Supply from Feed in Lactating Dairy Cows by Mingyung Lee, Dong Hyeon Kim, Seongwon Seo, Luis O. Tedeschi

“…A dataset comprising 1779 observations from 436 scientific publications was used to train support vector regression (SVR) and random forest regression (RFR) models. Different predictor sets were identified for each model, including factors such as days in milk (DIM), dry matter intake (DMI), dietary fiber content, and crude protein fractions. …”
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Comparison of dynamic mode decomposition with other data-driven models for lung cancer incidence rate prediction by L. Raymond Guo, Jifu Tan, M. Courtney Hughes

“…All models were evaluated based on their ability to predict 2021 lung cancer incidence rates.ResultsThe time series model achieved the lowest root mean squared error, followed by random forest. Meanwhile, DMD had an RMSE similar to that of Random Forest. …”
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Detection Technology for Catenary Stagger Value Based on Acceleration Signals by CHEN Hongming, ZHOU Ning, LU Wenwei, YANG Zixian, CHENG Yao, WANG Dong, ZHANG Weihua

“…［Method］ A detection method combining continuous wavelet transform (CWT) and random forest (RF) is proposed to effectively detect catenary stagger value across different speed levels. …”
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