Faults Detection and Diagnosis of a Large-Scale PV System by Analyzing Power Losses and Electric Indicators Computed Using Random Forest and KNN-Based Prediction Models by Yasmine Gaaloul, Olfa Bel Hadj Brahim Kechiche, Houcine Oudira, Aissa Chouder, Mahmoud Hamouda, Santiago Silvestre, Sofiane Kichou

“…This paper introduces a novel approach for fault detection and diagnosis in large-scale PV systems, utilizing power loss analysis and predictive models based on Random Forest (RF) and K-Nearest Neighbors (KNN) algorithms. …”
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Data-driven seismic mechanical performance evaluation of RC columns based on adaptive optimization ensemble learning method integrating random forest and back propagation neural ne... by Ayong Jiao, Ziqing Jiao, Bin Gao

“…The model integrates the strengths of random forest (RF) and back propagation neural network (BP) models, employing the dynamic weighting strategy based on mean absolute error (MAE). …”
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Predicción del diámetro sobre muñones en pinus taeda l. Origen marion, mediante curvas de perfil de fuste by Ernesto Andenmatten, Hugo Fassola, Féderico Letourneau, Paula Ferrere, Ernesto Crechi

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Advancement of a diagnostic prediction model for spatiotemporal calibration of earth observation data: a case study on projecting forest net primary production in the mid-latitude... by Eunbeen Park, Hyun-Woo Jo, Gregory Scott Biging, Jong Ahn Chun, Seong Woo Jeon, Yowhan Son, Florian Kraxner, Woo-Kyun Lee

“…This study showed enhanced diagnostic prediction concept can be applied to diverse environmental modeling approaches, offering valuable insights for climate adaptation and forest policy formulation. By accurately predicting various environmental targets, including drought and forest NPP, this approach aids in making informed policy decisions across different scales.…”
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Ecological niche models as a tool for estimating the distribution of plant communities by Mayra Flores-Tolentino, Enrique Ortiz, José Luis Villaseñor

“…The model best supported by the observed data and balanced in the percentage of omission and commission errors was our model, and the model most like ours in terms of the predicted area, was the one proposed by INEGI (2003). …”
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A review of SAR tomography by Xin Zhao, Jie Dong, Yanghai Yu, Mingsheng Liao, Lu Zhang, Jianya Gong

“…For the TomoSAR applications of forest, urban, and glacier scenarios, we present their scattering mechanisms using real data and explore their application potentials. …”
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Assessing the accuracy of multi-model approaches for downscaling land surface temperature across diverse agroclimatic zones by Debasish Roy, Bappa Das, Pooja Singh, Priyabrata Santra, Shovik Deb, Bimal Kumar Bhattacharya, Ajit Govind, Raghuveer Jatav, Deepak Sethi, Tridiv Ghosh, Joydeep Mukherjee, Vinay Kumar Sehgal, Prakash Kumar Jha, Sheshakumar Goroshi, P. V. Vara Prasad, Debashis Chakraborty

“…The calibration accuracy of the RF model was in better agreement with the coefficient of determination (R2), root mean square error (RMSE), and normalized RMSE (nRMSE) values ranging between 0.961–0.997, 0.103–0.439 K, and 0.034–0.143%, respectively, and lower values of standard errors for all three locations. …”
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PSLDV-Hop: a robust localization algorithm for WSN using PSO and refinement process by Bhupinder Kaur, Deepak Prashar, Arfat Ahmad Khan, Seifedine Kadry, Jungeun Kim

“…By utilizing an improved iterative evolution algorithm, the PSLDV-Hop algorithm reduces localization errors by achieving a higher degree of accuracy in node localization. …”
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Refractive Profile and Angle of Deviation in Patients with Congenital Esotropia and Congenital Exotropia by Masoud Khorrami-Nezhad, Mohamad Reza Akbari, Ali Hassan Khaleel, Razieh Bahreini, Froozan Narooie-Noori, Babak Masoomian

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Yes, they're all individuals: Hierarchical models for repeat survey data improve estimates of tree growth and size by Tess O'Brien, David Warton, Daniel Falster

“…Overall, this study shows how we can gain new and improved insights on growth, using repeat forest surveys. Our new method offers improved biomass dynamics estimation through reduced error in sizes over time, coupled with novel information about within‐species variation in growth behaviour that is inaccessible with species average models, such as individual parameters for the growth function which allows for relationships between parameters to be considered for the first time.…”
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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index by Priya Singh, Manoj Jha, Harshita Patel

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RADIO FREQUENCY BASED INPAINTING FOR INDOOR LOCALIZATION USING MEMORYLESS TECHNIQUES AND WIRELESS TECHNOLOGY by Tammineni Shanmukha Prasanthi, Swarajya Madhuri Rayavarapu, Gottapu Sasibhushana Rao, Raj Kumar Goswami, Gottapu Santosh Kumar

“…This study examines four memoryless positioning algorithms, namely K-Nearest Neighbour (KNN), Decision tree, Naïve Bayes and Random Forest regressor. The algorithms are compared based on their performance in terms of Mean Square Error, Root Mean Square Error, Mean Absolute Error and R2. …”
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Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy by Yikun Sui, Zhiyong Zhou, Rui Zhao, Zheng Yang, Yang Zou

“…The model’s performance was evaluated using the coefficient of determination (<i>R</i>²), the mean square error (MSE), the root mean square error (RMSE), and the mean absolute error (MAE). …”
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Prediction of Urban House Rental Prices in Lagos - Nigeria: A Machine Learning Approach by Sunday Oluyele, Juwon Akingbade, Victor Akinode, Royal Idoghor

“…Five machine learning models were trained and evaluated using mean absolute error (MAE), root mean squared error (RMSE) and r-square (R2); the random forest regression model outperformed the other four models with the lowest MAE, RMSE and the highest R2. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…The dataset was divided into training and testing sets using different ratios (90:10, 80:20, 50:50) to evaluate model performance. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to assess prediction accuracy. …”
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Turbidity Estimation by Machine Learning Modelling and Remote Sensing Techniques Applied to a Water Treatment Plant by Víctor Gauto, Enid Utges, Elsa Hervot, Maria Daniela Tenev, Alejandro Farías

“…The highest performance metrics were from a Random Forest model with a coefficient of determination close to 1 (0.913) and the lowest root-mean-squared error (143.9 nephelometric turbidity units). …”
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Modeling rainfall input variability for flash floods in Portugal: the influence of predisposing factors by Caio Villaça, Pedro Pinto Santos, José Luís Zêzere

“…These factors include the basin area, the predominant lithology, drainage density, and the mean or median values of elevation, slope, stream power index (SPI), topographic wetness index (TWI), roughness, and four soil properties. The Random Forest algorithm was used to build the models and obtained mean absolute percentage error (MAPE) around 19%, an acceptable value for the objectives of the work. …”
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Prediction of Spatial Winter Wheat Yield by Combining Multiscale Time Series of Vegetation and Meteorological Indices by Hao Xu, Hongfei Yin, Jia Liu, Lei Wang, Wenjie Feng, Hualu Song, Yangyang Fan, Kangkang Qi, Zhichao Liang, WenJie Li, Xiaohu Zhang, Rongjuan Zhang, Shuai Wang

“…And we developed the yield prediction model by using random forest (RF) and long short-term memory (LSTM) networks. …”
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Using Satellites to Monitor Soil Texture in Typical Black Soil Areas and Assess Its Impact on Crop Growth by Liren Gao, Yuhong Zhang, Deqiang Zang, Qian Yang, Huanjun Liu, Chong Luo

“…This study utilizes the random forest algorithm, Landsat-8 satellite remote sensing data, and climate- and terrain-related environmental covariates to map the spatial distribution of soil texture and analyze its impact on crop growth. …”
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Predicting diabetes using supervised machine learning algorithms on E-health records by Sulaiman Afolabi, Nurudeen Ajadi, Afeez Jimoh, Ibrahim Adenekan

“…The research explores the effectiveness of three supervised machine learning algorithms: logistic regression, Random Forest, and k-nearest neighbors (KNN), in developing predictive models for diabetes. …”
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