Soft Computing Solutions for Reducing the Carbon Footprint of Fly Ash Based Concrete. Advances in Civil Engineering by Awoyera, Paul O., Adetola, Joshua, Nayeemuddin, Mohammed, Mewada, Hiren, George Fadugba, Olaolu

“…The construction industry significantly contributes to environmental degradation,with many structures exhibiting high carbon footprints throughout their construction processes and lifespans.Activities such as cement hydration and other commoncon-struction practices substantially influence environmental conditions overtime,necessitating a critical evaluation of material and design choices.This study reported the environmental impact of fly ash(FA),which is largely used to enhance concrete strength.A prediction of two end point indicators,that is,global warming potential(GWP)and CO2 emission using soft computing methods are presented,which are particularly effective for handling complex,non linear relationships in environmental data.To achieve this, two machine learning approaches,the random forest(RF)and decision tree(DT)models,are employed to assess the environ- mental impact of structural materials and designs.Two data sets were obtained from reputable databases,including ResearchGate, Science Direct, Semantic Scholar,and Mendeley Data.The models are trained to explore the potential for optimizing structural designs and material selection stominimize environmental impacts.Feature importance is analyzed using Shapley values,providing insights into the most influential factors affecting GWP and CO2 emission Model performance is evaluated using R2 and root mean square error(RMSE) metrics. …”
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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). …”
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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

“…Four different machine learning algorithms, namely, support vector regression (SVR), Bayesian ridge (BYR), random forest regression (RFG), and extreme gradient boosting (XGBoost), were used to predict best corrected visual acuity (BCVA) values. …”
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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. …”
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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

“…For two rivers, the random forest model proves to be the best model, with an MAE of 0.750 m and an MAPE of 10.806%. …”
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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%. …”
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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.…”
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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. …”
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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

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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. …”
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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. …”
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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

“…Regression model RMSE was 12° and calibration was 1.00 (95% CI: 0.94, 1.06). Random Forest RMSE was 9° and calibration was 1.33 (95% CI: 1.29, 1.37). …”
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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

“…In the first case, artificial neural network models, decision tree analysis, random forest, and the K-nearest neighbor model were used for prediction. …”
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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

“…Support Vector Regressions (SVR), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting Machine (XGBoost) and Prophet to analyze the 15,888 daily time series climate data in Johor Bahru city, Malaysia. …”
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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. …”
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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. …”
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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. …”
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Classification evaluation and improvement of airborne PolSAR images for land use mapping using deep learning by Jiafeng Wang, Yongjiu Feng, Xiaohua Tong, Zhenkun Lei, Mengrong Xi, Yi Zhou, Panli Tang

“…On the other hand, noise, polarization error and motion error had a negative impact on the classification performance. …”
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Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

“…The findings showed that K-Nearest Neighbors performed best in predicting FR3C tensile strength, achieving the lowest mean absolute error MAE (0.001) and root mean squared error (RMSE 0.001) and highest coefficient of determination (R2 = 0.999) in test scores. …”
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Optimizing Chlorophyll-a Concentration Inversion in Coastal Waters Using SVD and Deep Learning Approach by Lili Zhan, Yongxin Xu, Jinshan Zhu, Zhangshuo Liu

“…Other machine learning methods, such as random forest (RF) and the support vector machine (SVM) are also used to establish the inversion models for the comparison. …”
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