Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…In the end, model performance was validated using Accuracy, Kappa score, Root Mean Square Error (RMSE), F1-score, Confusion Matrix, and Receiver Operating Characteristic curve (ROC). …”
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Assessment of water fluxes under the dual threat of changes in land cover and climate variability in the Brazilian Cerrado biome by Dimaghi Schwamback, Abderraman R. Amorim Brandão, Ronny Berndtsson, Edson Wendland, Magnus Persson

“…The validated models demonstrated good performance, with a mass balance error of less than 0.9 %. The results indicate that climate change will affect certain water fluxes more than others, in a hierarchical (bottom-top) sequence: soil-water storage, bottom flux, infiltration, surface flux, evaporation, and root uptake. …”
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Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…This resulted in a spatially averaged correlation increase of over 0.5 for predicting the minor axis and anisotropy, along with a reduction of more than 0.15 in the Normalized Root Mean Square Error. These findings highlight the considerable potential of machine learning algorithms in predicting mixing ellipses and parameterizing eddy mixing processes within climate models.…”
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs by Michael Myers, Michael D. Brown, Sarkhan Badirli, George J. Eckert, Diane Helen-Marie Johnson, Hakan Turkkahraman

“…Model performance was evaluated using mean absolute error (MAE), intraclass correlation coefficients (ICCs), and clinical thresholds (2 mm or 2°). …”
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Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques by Momotaz Begum, Mehedi Hasan Shuvo, Imran Ashraf, Abdullah Al Mamun, Jia Uddin, Md Abdus Samad

“…Among them, XGBR outperformed, considering the accuracy, mean square error, and R2 score. We also used Explainable Artificial Intelligence (XAI), Local Interpretable Model (LIME), and SHapley Additive exPlanations (SHAP) to determine software fault features. …”
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Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete by Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, Naseer Muhammad Khan, Majid Khan, Fahid Aslam, Diaa Salama Abd Elminaam

“…The performance of the developed algorithms was assessed using several error metrices, k-fold validation, and residual assessment etc. …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

“…We evaluated the performance of each model using Nash-Sutcliffe Efficiency (NSE), Pearson's Correlation Coefficient (PCC), and Root Mean Square Error (RMSE). Our results demonstrate test accuracy with area weighted average NSE, PCC, and RMSE of 0.51 ± 0.31, 0.71 ± 0.23, and 4.75 ± 3.63 cm, respectively. …”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…Our optimized models achieved R2 (coefficient of determination) of 0.89 and RMSE (root-mean-square error) of 0.47 for TOC predictions and 0.90 and 34.8 for hardness predictions, reducing RMSE by up to 13.52% compared to the unconstrained model. …”
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Crosswalk between HRSD and MADRS outcomes for rTMS in patients with depression by Xiao Chen, Fidel Vila-Rodriguez, Zafiris J Daskalakis, Daniel M Blumberger, Jonathan Downar, Chao-Gan Yan, Tyler S Kaster

“…Model performance was benchmarked using the root mean square error (RMSE).Results The linear regression model demonstrated the best performance (RMSE: 2.66–4.82), though the SVR model’s performance was slightly worse but comparable (RMSE: 2.69–5.32). …”
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An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty by Mohsen Pourmohammad Shahvar, Davide Valenti, Alfonso Collura, Salvatore Micciche, Vittorio Farina, Giovanni Marsella

“…The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R² values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…Notably, the coefficient of determination (R2) on the test set increased by 16.14%, while the root mean square error decreased by 20.44%, respectively. Nine machine learning algorithms, including decision tree (DT), random forest (RF), CatBoost, LassoLarsCV, histogram gradient boosting, extreme gradient boosting (XGBoost), K nearest neighbor, ridge regression (Ridge), and linear regression (Linear R), were employed to optimize PSC preparation and assess its impact on device performance. …”
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Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…The ANN model achieved the highest performance with a coefficient of determination (R2) of 0.23 ± 0.05, a root mean square error of AHI of 10.71 ± 1.01 events/h, an accuracy for outcomes classification of 81.3% ± 1.2% and an area under the receiver operating characteristic of 74.6% ± 1.9%, whereas for LR model, they were 0.094 ± 0.06, 11.61 ± 0.76 events/h, 71.7% ± 1.5% and 68.8% ± 2.9%, respectively. …”
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Associations between ambient particulate matter exposure and the prevalence of arthritis: Findings from the China Health and Retirement Longitudinal Study. by Yuntian Ye, Kuizhi Ma, Aifeng Liu

“…However, owing to its cross-sectional design, the absence of subtype differentiation and reliance on self-reported diagnoses, these findings may be influenced by reverse causation and measurement error, and should therefore be interpreted with caution.…”
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A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis by Bin Li, Bo Qiao, Qianyu Zhao, Dan Yang, Rongcheng Zhu, Zhexuan Wang, Yujie Yang

“…Based on the collected data, a support vector regression (SVR) algorithm was trained and its performance was compared with that of a backpropagation (BP) neural network, a radial basis function (RBF) neural network, and a random forest (RF) algorithm. To optimize performance, a grid search with five-fold cross-validation was conducted to identify optimal hyperparameters; this process yielded a cost parameter (C) of 38 and a gamma parameter (γ) of 8, which minimized the root mean square error (RMSE) on the training set. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…Among the tested models, GPR consistently outperformed all others, achieving a maximum coefficient of determination (R²) of 0.93 and the lowest root mean square error (RMSE) of 16.54, thereby demonstrating superior capability in capturing the underlying nonlinear relationships within the data. …”
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Predicting Oncological and Functional Outcomes by Nephrectomy Type for T1 Renal Tumors Using Machine Learning Models by Dongrul Shin, Maisy Song, Jungyo Suh, Cheryn Song

“…Model performance for recurrence prediction was evaluated with area under the curve receiver operating characteristic, area under the precision-recall curve, and log-loss, while eGFR prediction was assessed using root mean square error (RMSE) and R2. Results Of the 823 patients, 463 (56.3%) had T1a tumors and 487 (59.2%) underwent PN. …”
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Automated Broiler Mobility Evaluation Through DL and ML Models: An Alternative Approach to Manual Gait Assessment by Mustafa Jaihuni, Yang Zhao, Hao Gan, Tom Tabler, Hairong Qi

“…The RF model, outperforming others, was able to predict GS with a generalized R² of 0.62, root mean squared error (RMSE) of 0.54, and 65% classification accuracy.…”
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Predicting Nitrous Oxide Emissions from China’s Upland Fields Under Climate Change Scenarios with Machine Learning by Tong Li, Yunpeng Li, Wenxin Cheng, Jufeng Zheng, Lianqing Li, Kun Cheng

“…Comparative analysis revealed that the RF algorithm performed the best, with the highest R² at 0.66 and the lowest root mean square error at 0.008 kg N₂O ha⁻¹ day⁻¹. …”
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Estimation of Reference Crop Evapotranspiration in the Yellow River Basin Based on Machine Learning and Its Regional and Drought Adaptability Analysis by Jun Zhao, Huayu Zhong, Congfeng Wang

“…Among them, the RF model demonstrated the highest simulation accuracy, achieving an R² of 0.77, and reduced the mean daily ET₀ estimation error by 0.057 mm/day and 0.076 mm/day compared to the PT and HG models, respectively. …”
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Biomass distribution law of winter wheat in mining-affected area based on UAV remote sensing by Jing WANG, Wenbing GUO, Zhichao CHEN, Erhu BAI

“…The best model was selected based on the coefficient of determination (R2) and root mean square error (RMSE). The final spatial distribution inversion results of winter wheat biomass in the study area were obtained. …”
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