Assessing the impact of multi-source environmental variables on soil organic carbon in different land use types of China using an interpretable high-precision machine learning meth... by Feng Wang, Ruilin Liang, Shuyue Li, Meiyan Xiang, Weihao Yang, Miao Lu, Yingqiang Song

“…However, the traditional ML model is limited by the hyperparameter adjustment of artificially trial-and-error experimentation and the inexplicability of fitting process, and the precision and performance of ML model cannot be fully utilized. …”
Get full text

A comparative study of machine learning in predicting the mechanical properties of the deposited AA6061 alloys via additive friction stir deposition by Qian Qiao, Quan Liu, Jiong Pu, Haixia Shi, Wenxiao Li, Zhixiong Zhu, Dawei Guo, Hongchang Qian, Dawei Zhang, Xiaogang Li, C. T. Kwok, L. M. Tam

“…Prediction results show that the ANN model performs the best prediction accuracy with the highest R2 (0.9998) and the lowest mean absolute error (MAE, 0.0050) and root mean square error (RMSE, 0.0063). …”
Get full text

Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant by Tawsif Mahmud, Jiaul Haque Saboj, Preetom Nag, Goutam Saha, Bijan K. Saha

“…This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. …”
Get full text

An optimized ensemble ML-WQI model for reliable water quality prediction by minimizing the eclipsing and ambiguity issues by Ashifur Rahman, M. M. Mahbubul Syeed, Md. Rajaul Karim, Kaniz Fatema, Razib Hayat Khan, Mohammad Faisal Uddin

“…To evaluate performance, mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R-squared ( $$R^2$$ R 2 ), fivefold cross-validation, and a comparative evaluation with existing ML models are carried out. …”
Get full text

Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks by Yanhong Peng, Yuxin Wang, Fangchao Hu, Miao He, Zebing Mao, Xia Huang, Jun Ding

“…KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. …”
Get full text

Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…The XGBoost model predicted BCVA values with the lowest mean absolute error (MAE), at 0.1598 logarithm of the minimum angle of resolution (logMAR); the lowest root mean square error (RMSE), at 0.2402 logMAR; and the highest accuracy, at 0.8959.ConclusionPromising outcomes in BCVA prediction were achieved by the PVEP-trained machine learning models, which will be helpful in the clinical evaluation of patients after ocular trauma.…”
Get full text

Prediction of Soybean Yield at the County Scale Based on Multi-Source Remote-Sensing Data and Deep Learning Models by Hongkun Fu, Jian Li, Jian Lu, Xinglei Lin, Junrui Kang, Wenlong Zou, Xiangyu Ning, Yue Sun

“…The ant colony optimization-convolutional neural network with gated recurrent units and multi-head attention (ACGM) model showcases remarkable predictive prowess, as evidenced by a coefficient of determination (R²) of 0.74, a root mean square error (RMSE) of 123.94 kg/ha, and a mean absolute error (MAE) of 105.39 kg/ha. …”
Get full text

Estimating Water Depth of Different Waterbodies Using Deep Learning Super Resolution from HJ-2 Satellite Hyperspectral Images by Shuangyin Zhang, Kailong Hu, Xinsheng Wang, Baocheng Zhao, Ming Liu, Changjun Gu, Jian Xu, Xuejun Cheng

“…Among four inversion methods, the multilayer perceptron demonstrates superior performance for the water reservoir, achieving the mean absolute error (MAE) and the mean absolute percentage error (MAPE) of 1.292 m and 22.188%, respectively. …”
Get full text

Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…The proposed framework was validated by comparing the GA-RF predictions with simulation results under different reservoir conditions, which yielded a minimum relative error of 0.34% and an average relative error of 5.3%. …”
Get full text

The relationship between the annual catch of bigeye tuna and climate factors and its prediction by Peng Ding, Hui Xu, Xiaorong Zou, Xiaorong Zou, Xiaorong Zou, Shuyi Ding, Siqi Bai

“…The fitting degree between the predicted values and the actual values of the SSA-XGBoost model is 0.853, the mean absolute error is 0.104, the root mean square error is 0.124.DiscussionThe trend between the predicted values and the actual values of the SSA-XGBoost model is generally consistent, indicating good model fitting performance, which can provide a basis for the management of bigeye tuna fisheries.…”
Get full text

Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…The results show that the LSTM performed best in inverting the three parameters, with the LAI inversion accuracy on 21 August reaching a coefficient of determination (R²) of 0.72, root mean square error (RMSE) of 0.34, and mean absolute error (MAE) of 0.27. …”
Get full text

A GIS-Independent method of Stream-Order-Law Ratios Prediction in Catchments for direct Runoff Hydrograph Estimation by Mostafa Akbari Kheirabadi, Touraj Sabzevari, Andrea Petroselli, Mohsen Tarahi

Get full text

Assessing Building Seismic Exposure Using Geospatial Technologies in Data-Scarce Environments: Case Study of San José, Costa Rica by Javier Rodríguez-Saiz, Beatriz González-Rodrigo, Juan Gregorio Rejas-Ayuga, Diego A. Hidalgo-Leiva, Miguel Marchamalo-Sacristán

“…In the case of San José, 7226 buildings were classified into eight typologies using the derived attributes, achieving a classification error of 46%. When the building height—visually sampled—was included, the error decreased significantly to 13%, confirming its importance in typology prediction and emphasizing the need for efficient acquisition strategies. …”
Get full text

Multivariate bias correction of ERA5 climatic data for assessing climate-related road vulnerabilities in Nigeria by A. F. Abdussalam, Z. Isa, A. Babati, B. M. Baba, A. Suleiman, A. S. Abubakar, A. O. Eberemu, M. Hamma-Adama, H. M. Alhassan, M. N. Ibrahim

“…Evaluation metrics such as mean absolute error (MAE), root mean square error (RMSE), and spatial mean difference are utilised to assess the bias correction techniques. …”
Get full text

Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers by Garrett S Bullock, Charles A Thigpen, Gary S Collins, Nigel K Arden, Thomas K Noonan, Michael J Kissenberth, Ellen Shanley

“…Currently there are no cheap and easy to use clinical tools to measure humeral torsion, inhibiting clinical assessment. Models with low error and "good" calibration slope may be helpful for prediction…”
Get full text

Digital Mapping of Soil Equivalent Calcium Carbonate Using Landsat 8 Satellite Images and Environmental Data by Machine Learning Models in Badr Watershed, Kurdistan Province by M. Zarinibahador

“…Among the models used to predict the equivalent amount of calcium carbonate using the 10-fold cross-validation method, the multiple linear regression (MLR) model had the highest prediction accuracy with a coefficient of determination of 0.796 and a mean square error of 6.514. In the 5-fold cross-validation method, the K-nearest neighbor (KNN) model had the highest predictive accuracy with a coefficient of determination of 0.9845 and a root mean square error of 2.1258. …”
Get full text

Comparison of machine learning model performance for predicting the climate variables in Johor Bahru, Malaysia by Farid Zamani Che Rose, Nur Aqilah Khadijah Rosili, Muhammad Fadhil Marsani

“…RF exhibits the lowest error to predict the T2M (RMSE: 0.2182, MAE: 0.1679), T2MDEW (RMSE: 0.2291, MAE: 0.1750), T2MWET (RMSE: 0.1621, MAE: 0.1251), QH2M (RMSE: 0.3502, MAE: 0.2701) and RV2M (RMSE: 1.4444, MAE: 1.1090). …”
Get full text

High-Resolution Daily XCH₄ Prediction Using New Convolutional Neural Network Autoencoder Model and Remote Sensing Data by Mohamad M. Awad, Saeid Homayouni

“…The CNN-AE model demonstrated the highest accuracy in regional-scale analysis, achieving a Mean Absolute Error (MAE) of 28.48 ppb and a Root Mean Square Error (RMSE) of 30.07 ppb. …”
Get full text

Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…The model’s prediction performance is evaluated using mainstream metrics such as the Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R²), Root Mean Square Error (RMSE), robustness analysis, overfitting analysis, and grey relational analysis. …”
Get full text

Analysis of the most influential factors affecting outcomes of lung transplant recipients: a multivariate prediction model based on UNOS Data by Reza Safdari, Marsa Gholamzadeh, Hamidreza Abtahi, Mehrnaz Asadi Gharabaghi

“…Then, six ML models with optimised hyperparameters including multiple linear regression, random forest regressor (RF), support vector machine regressor, XGBoost regressor, a multilayer perceptron model and a deep learning model were developed based on the UNOS dataset.Primary and secondary outcome measures The performance of each model was evaluated using R-squared (R2) and other error rate metrics. …”
Get full text