Root Mean Square Error Research Articles

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

AbstractThis study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data

Abstract. Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot effectively be achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range light detection and ranging (lidar) data, which enables the measurement of individual tree heights in forest plots with high precision. Apart from lidar data, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and synthetic aperture radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining the basal area of individual tree within plots using uncrewed aerial vehicle (UAV) lidar data, this study calculated the weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at a large spatial scale, we also developed a machine-learning-based mixed-effects (MLME) model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 and 13.3 m with standard deviations of 2.9 and 3.3 m, respectively. The accuracy of mapped products was validated utilizing lidar and field measurement data. The correlation coefficient (r) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while the root mean square error (RMSE) ranged from 2.6 to 4.1 and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024).

Application of Machine Learning to Predict the Capacity of Fractured Horizontal Wells in Shale Reservoirs

Shale oil wells typically have numerous volume fracturing segments in their horizontal sections, resulting in significant variability in productivity across these segments. Conventional productivity prediction and fracturing effect evaluation methods are challenging to apply effectively. Establishing a stable and efficient intelligent productivity prediction method using machine learning is a promising approach for the effective development of shale oil reservoirs. This study is based on geological data, fracturing records, and a production database of 91 production wells in a shale oil reservoir in a specific area. Fourteen key parameters affecting productivity were selected from geological and engineering perspectives, and the recursive feature elimination method based on support vector machines identified five optimal main controlling factors. Three machine learning methods—decision tree, random forest, and gradient boosting decision tree (GBDT)—were used to model productivity prediction, with root mean square error (RMSE) employed to evaluate model performance. The study results indicate that formation coefficient, cluster spacing, treatment volume, sand volume, and fracturing segment length are the main controlling factors influencing productivity in fractured horizontal wells. Among the models, the random forest algorithm with bootstrap sampling produced the most stable prediction results, achieving a prediction accuracy of 94% and an RMSE of 0.934 on the test set, outperforming the decision tree and GBDT models in terms of minimum RMSE on the test set.

Modelling of Carbon Monoxide and Suspended Particulate Matter Concentrations in a Rural Area Using Artificial Neural Networks

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m3 air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m3 air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R2) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R2, RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m3 air, and 23.889 μg/m3 air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters.

Advanced Trans-BiGRU-QA Fusion Model for Atmospheric Mercury Prediction

With the deepening of the Industrial Revolution and the rapid development of the chemical industry, the large-scale emissions of corrosive dust and gases from numerous factories have become a significant source of air pollution. Mercury in the atmosphere, identified by the United Nations Environment Programme (UNEP) as one of the globally concerning air pollutants, has been proven to pose a threat to the human environment with potential carcinogenic risks. Therefore, accurately predicting atmospheric mercury concentration is of critical importance. This study proposes a novel advanced model—the Trans-BiGRU-QA hybrid—designed to predict the atmospheric mercury concentration accurately. Methodology includes feature engineering techniques to extract relevant features and applies a sliding window technique for time series data preprocessing. Furthermore, the proposed Trans-BiGRU-QA model is compared to other deep learning models, such as GRU, LSTM, RNN, Transformer, BiGRU, and Trans-BiGRU. This study utilizes air quality data from Vietnam to train and test the models, evaluating their performance in predicting atmospheric mercury concentration. The results show that the Trans-BiGRU-QA model performed exceptionally well in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2), demonstrating high accuracy and robustness. Compared to other deep learning models, the Trans-BiGRU-QA model exhibited significant advantages, indicating its broad potential for application in environmental pollution prediction.

Wind turbine energy forecasting using real wind farm’s measurement data and performance of gene expression programming analytical model in comparison to traditional algorithms

ABSTRACT Forecasting wind power is vital to ensure steady, sustainable, and renewable energy. The complex nonlinear nature of wind flow and its interrelated factors make power prediction challenging. This study predicted wind power curves inspired by the Biologically Inspired Evolutionary Computation (BIEC) paradigm, incorporating Gene Expression Programming (GEP), Artificial Neural Networks (ANN), Least Square Support Vector Machines (LSSVM), and Random Forest (RF) models. Key input parameters include wind speed, yaw azimuth, turbulent intensity, veer, horizontal and vertical shear, ambient temperature, blade pitch, and rotor speed. The study evaluates these models’ effectiveness using root mean square error (RMSE) and correlation coefficient R. Results indicate that the GEP model offers transparent modeling, emphasizing critical inputs like wind speed, rotor speed, blade pitch angle, and temperature for wind power prediction. The GEP model identified wind speed, rotor speed, blade pitch angle, and temperature as the most influential parameters, with variable importance index (Ii) values of 69.39%, 24.39%, 5.46%, and 0.64% for training, and 69.37%, 25.03%, 4.98%, and 0.54% for validation. The study demonstrates the GEP model’s efficacy in accurately forecasting wind power curve, achieving a high correlation with training and validation coefficients of 0.9899 and 0.994, respectively, outperforming traditional models with minor errors.

Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System

Mountainous land types are characterized by a scarcity of observational data, particularly in remote areas such as the Kunlun Mountains, where conventional Automatic Weather Stations (AWSs) typically do not record land surface temperature (LST) data. This study aims to develop and evaluate models for converting air temperature (TA) to LST using newly established meteorological station data from the Kunlun Mountain Gradient Observation System, thereby providing time-continuous LST data for AWSs. We constructed a conceptual model to explore the relationship between 1.5 m TA and LST and instantiated it using three machine learning algorithms: Support Vector Machine (SVR), Convolutional Neural Network (CNN), and CatBoost. The results demonstrated that the CatBoost algorithm outperformed the others under complex terrain and climatic conditions, achieving a coefficient of determination (R2) of 0.997 and the lowest root mean square error (RMSE) of 0.627 °C, indicating superior robustness and accuracy. Consequently, CatBoost was selected as the optimal model. Additionally, this study analyzed the spatiotemporal distribution characteristics of cloud cover in the Kunlun Mountain region using the MOD11A1 product and assessed the uncertainties introduced by the 8-day average compositing method of the MOD11A2 product. The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. These findings emphasize the importance of hourly LST calculations based on AWSs for accurately assessing the spatiotemporal characteristics of LST in the Kunlun Mountains, thus providing more precise spatiotemporal support for remote sensing applications in high-altitude regions.

Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China

The cation exchange capacity (CEC) of the clay fraction (<2 μm), denoted as CECclay, serves as a crucial indicator for identifying low-activity clay (LAC) soils and is an essential criterion in soil classification. Traditional methods of estimating CECclay, such as dividing the whole-soil CEC (CECsoil) by the clay content, can be problematic due to biases introduced by soil organic matter and different types of clay minerals. To address this issue, we introduced a soil pedotransfer functions (PTFs) approach to predict CECclay from CECsoil using experimental soil data. We conducted a study on 122 pedons in South China, focusing on highly weathered and strongly leached soils. Samples from the B horizon were used, and eight models and PTFs (four machine learning methods, multiple linear regression (MLR) and three PTFs from publication) were evaluated for their predictive performance. Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CECsoil by clay content led to significant overestimation of CECclay, with a mean error of 14.42 cmol(+) kg−1. MLR produced the most accurate predictions, with an R2 of 0.63–0.71 and root mean squared errors (RMSE) of 3.21–3.64 cmol(+) kg−1. The incorporation of environmental variables improved the accuracy by 2–10%. A linear model was fitted to enhance the current calculation method, resulting in the equation: CECclay = 15.31 + 15.90 × (CECsoil/Clay), with an R2 of 0.41 and RMSE of 4.48 cmol(+) kg−1. Therefore, given limited soil data, the MLR PTFs with explicit equations were recommended for predicting the CECclay of B horizons in humid subtropical regions.

Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas

AbstractPredicting rainfall is a challenging and critical task due to its significant impact on society. Timely and accurate predictions are essential for minimizing human and financial losses. The dependence of approximately 60% of agricultural land in India on monsoon rainfall implies the crucial nature of accurate rainfall prediction. Precise rainfall forecasts can facilitate early preparedness for disasters associated with heavy rains, enabling the public and government to take necessary precautions. In the North-Western Himalayas, where meteorological data are limited, the need for improved accuracy in traditional modeling methods for rainfall forecasting is pressing. To address this, our study proposes the application of advanced machine learning (ML) algorithms, including random forest (RF), support vector regression (SVR), artificial neural network (ANN), and k-nearest neighbour (KNN) along with various deep learning (DL) algorithms such as long short-term memory (LSTM), bi-directional LSTM, deep LSTM, gated recurrent unit (GRU), and simple recurrent neural network (RNN). These advanced techniques hold the potential to significantly improve the accuracy of rainfall prediction, offering hope for more reliable forecasts. Additionally, time series techniques, including autoregressive integrated moving average (ARIMA) and trigonometric, Box-Cox transform, arma errors, trend, and seasonal components (TBATS), are proposed for predicting rainfall across the altitudinal gradients of India’s North-Western Himalayas. This approach can potentially revolutionise how we approach rainfall forecasting, ushering in a new era of accuracy and reliability. The effectiveness and accuracy of the proposed algorithms were assessed using meteorological data obtained from six weather stations at different elevations spanning from 1980 to 2021. The results indicate that DL methods exhibit the highest accuracy in predicting rainfall, as measured by the root mean squared error (RMSE) and mean absolute error (MAE), followed by ML algorithms and time series techniques. Among the DL algorithms, the accuracy order was bi-directional LSTM, LSTM, RNN, deep LSTM, and GRU. For the ML algorithms, the accuracy order was ANN, KNN, SVR, and RF. These findings suggest that altitude significantly affects the accuracy of the models, highlighting the need for additional weather stations in this mountainous region to enhance the precision of rainfall prediction.

DGTNet：dynamic graph attention transformer network for traffic flow forecasting

Abstract Graph-based traffic flow prediction plays a crucial role in urban traffic management and planning. In this paper, we propose a novel Dynamic Graph Attention Transformer Network (DGTnet), which is designed to address the issue of inadequately integrating of temporal and spatial dimensions in traditional models. DGTnet maintains temporal continuity while revealing the complex dynamic relationships between key nodes in the urban traffic system, capturing the periodic changes in the rhythm of city life. Specifically, this study adopts adaptive signal decomposition technology to decompose traffic data into multiple intrinsic mode functions (IMFs), effectively capturing the dynamic changes in traffic flow. This decomposition method is key to the implementation of DGTnet dynamic graph construction, enabling the analysis of traffic flow at different time scales, thereby providing a new perspective for traffic flow prediction research. In the traffic prediction module, we comprehensively consider node, edge, and graph structural information, adopting a multi-head self-attention mechanism to achieve direct cross-modeling in both temporal and spatial dimensions. Finally, we introduce a position-wise feed forward network layer to integrate different types of data and capture nonlinear relationships. The experimental results, based on the public transportation network data sets METR_LA, PEMS_BAY, PEMS03 and PEMS07, demonstrate that DGTNet exhibits notable enhancements in three evaluation indicators, namely the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The pertinent code has been made available for public access at https://github.com/chenjing0616/DGTNet.

Rapid retrieval of soil moisture using a novel portable L-band radiometer in the Hulunbeier Prairie, China

ABSTRACT The soil moisture products derived from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions have garnered widespread adoption in drought surveillance, meteorological/climatic forecasting, and hydrological investigations. Nevertheless, satellite platforms inherently suffer from coarse spatial resolutions (approximately 43 km), stemming from constraints on antenna dimensions, thereby posing challenges in leveraging their data for agricultural and ecological endeavors that necessitate meter-scale soil moisture maps. This research endeavor innovatively employed a Portable L-band Radiometer (PoLRa), leveraging microstrip patch array antenna technology, to facilitate portable and cost-effective soil moisture monitoring within the context of the Hulunbeier Prairie Experiments in China. Three distinct soil moisture datasets were procured utilizing Scheme I, a PoLRa-based default iteration of the tau-omega semi-empirical model; Scheme II, a brightness temperature forward simulation akin to the CMEM (Community Microwave Emission Model) framework; and Scheme III, a regression-based approach. The findings are: 1. Employing the brightness temperature data as input, the CMEM-inspired schemes achieve soil moisture retrieval with an RMSE (Root Mean Square Error) of approximately 0.06 cm3/cm3, whereas the regression model between retrieved and observed data manifests a linear bias. 2. The CMEM forward simulation scheme outperforms the tau-omega model but necessitates more intricate modules for the retrieval process. 3. Both in terms of linear bias and RMSE, the regression-based scheme exhibits the poorest performance. This study anticipates enhancing the applicability of soil moisture remote sensing devices and methodologies across diverse disciplines, including agriculture, meteorology, and soil science, by advancing the precision and accessibility of soil moisture monitoring at finer scales.

Hybrid Machine Learning Approaches for 5G Traffic Prediction

ABSTRACT Accurate traffic prediction poses great difficulties because of the continuously increasing scale and diversity of 5G network traffic, which is driven by user demands. Moreover, certain characteristics of 5G traffic are constantly changing; thus, simulations using traditional models often lead to incorrect estimations or inefficient utilization of available resources. Consequently, we propose a hybrid machine learning model that integrates support vector machine (SVM) and decision tree algorithms to enhance efficiency of 5G traffic prediction. The structure of the hybrid model dynamically adjusts by adding or removing hidden layers and units within the network to improve prediction performance. The efficacy of the proposed model is evaluated using metrics like mean squared error, mean absolute error, and root mean squared error (RMSE). Findings show that the hybrid model consistently achieves lower error rates than SVM alone. Further performance enhancement of the hybrid model in predicting 5G traffic is also supported by comparisons of R-squared values against signal-to-noise ratios. These outcomes show the potential of the proposed method to improve traffic prediction accuracy in 5G networks, serving as a powerful tool for network control.

Efficient Removal of Cu(II) and Ni(II) Ions from Aqueous Solutions Using Reduced Graphene Oxide/Bentonite Clay/ZnO Nanocomposites: Kinetics, Mechanisms, and Adsorption Performance

<p style="text-align:justify;text-indent:36.0pt;"><span style="font-family:"Arial","sans-serif";font-size:11pt;line-height:150%;">To assess the efficacy of removing heavy metals such as Cu (II) and Ni (II) ions from aqueous solution, the adsorption behaviour of a nanocomposite powder comprising of reduced Graphene Oxide (rGO), bentonite clay, and ZnO was investigated. Various analytical techniques including Fourier transform infrared spectroscopy, Field emission scanning electron microscopy, Energy-Dispersive X-ray spectroscopy, X-Ray diffraction analysis, and N<sub>2</sub> adsorption/desorption analysis were utilized to analyse the synthesized composites. Through the batch equilibrium approach, parameters influencing adsorption behaviours, including initial metal ion concentration, pH, adsorbent dosage, and contact time, were comprehensively examined. Adsorption data were shown to have good fit to the Langmuir isotherm model and pseudo-first-order model, with high R<sup>2</sup>, low root mean square error (RMSE), low chi square (Χ<sup>2</sup>), and low standard deviation (SD) values when using the linear regression approach. For the raw sample (rGO/ bentonite clay) containing copper and nickel, equilibrium was achieved in 80 minutes, while for samples with 1% (rGO/ bentonite clay/1% ZnO), 3% (rGO/ bentonite clay/3% ZnO), and 5% (rGO/ bentonite clay/5% ZnO), equilibrium was attained within 70 minutes. </span><span style="background-color:white;font-family:"Arial","sans-serif";font-size:11pt;line-height:150%;">Within the pH range of 2 to 12, the adsorption kinetics of copper and nickel exhibited a notable pH-dependent relationship, highlighting the pronounced influence of pH conditions on the adsorption process</span><span style="background-color:white;color:#374151;font-family:"Segoe UI","sans-serif";font-size:11pt;line-height:150%;">. </span><span style="background-color:white;font-family:"Arial","sans-serif";font-size:11pt;line-height:150%;">Copper consistently demonstrated superior removal efficiency compared to nickel in all conducted adsorption tests, attributed to its heightened propensity for bond formation with adsorbent surfaces.</span><span style="font-family:"Arial","sans-serif";font-size:11pt;line-height:150%;"> The primary pathways for copper and nickel adsorption on the composites involved intermolecular interactions, cation exchange, physisorption and electrostatic interactions. The Langmuir model revealed maximum Cu (II) adsorption capacities of 312.5 mg/g, 217.39 mg/g, 161.95 mg/g, and 101.96 mg/g for raw sample, 1%, 3%, and 5% samples, respectively. Nickel (II) adsorption capacity were measured at 252.41 mg/g, 185 mg/g, 125.03 mg/g, and 96.60 mg/g for the raw sample, 1%, 3%, and 5% samples, respectively.</span></p>

Delamination assessment and prediction in ultrasonic-assisted drilling of laminated hybrid composites

This study presents a novel application of the Random Forest (RF) algorithm to predict delamination in ultrasonic-assisted drilling (UAD) of carbon fiber reinforced polymers (CFRPs). It performs a multi-dimensional analysis of factors including graphene nanoplatelet (GNP) addition, ultrasonic vibration, cutting tool type, and feed rate on delamination damage. The RF algorithm was chosen for its ability to handle both regression and categorical tasks. The model demonstrated strong predictive performance, achieving an R² value of 0.9445 on test data, with a root mean squared error (RMSE) of 0.32% and a mean absolute error (MAE) of 0.29% relative to the average values. Analysis of variance (ANOVA), Sobol sensitivity, and Shapley additive explanations (SHAP) analysis were used to assess the impact of input parameters. Sobol identified the cutting tool type and feed rate as the most influential factors, contributing 37.7% and 34.3% to delamination variance, aligning with ANOVA findings. SHAP further confirmed the tooling type and feed rate as key factors, with contributions of 48.75% and 32.61%. The analyses revealed that GNPs increased delamination due to higher thrust forces, while ultrasonic vibration and high-cobalt tools reduced delamination. Optimal conditions were a feed rate of 0.08 mm/rev with an 8% cobalt tool and ultrasonic vibration, excluding GNPs.

Evapotranspiration Estimation with the Budyko Framework for Canadian Watersheds

Actual evapotranspiration (AET) estimation plays a crucial role in watershed management. Hydrological models are commonly used to simulate watershed responses and estimate AET. However, their calibration heavily depends on station-based data, which is often limited in availability and frequently inaccessible, making the process challenging and time-consuming. In this study, the Budyko model framework, which effectively utilizes remote sensing data for hydrological modeling and requires the calibration of only one parameter, is adopted for AET estimation across Ontario, Canada. Four different parameter estimation methods were developed and compared, and an attribution analysis was also conducted to investigate the impacts of climate and vegetation factors on AET changes. Results show that the developed Budyko models performed well, with the best model achieving a Nash-Sutcliffe Efficiency (NSE) value of 0.74 and a Root Mean Square Error (RMSE) value of 55.5 mm/year. The attribution analysis reveals that climate factors have a greater influence on AET changes compared to vegetation factors. This study presents the first Budyko modeling attempt for Canadian watersheds. It demonstrates the applicability and potential of the Budyko framework for future case studies in Canada and other cold regions, providing a new, straightforward, and efficient alternative for AET estimation and hydrological modeling.

Extracting photovoltaic cells parameters for three diode model using HSDA algorithm

Developing the metaheuristic algorithms directly for extracting the photovoltaic cells and panels (PV) parameters or the implementation of algorithms for PV already used for other problems conduct to accurately obtain parameters and, generally, in a short time. The objective of the paper is to accurately extract the parameters of the photovoltaic cells and panels. To obtain this the hybrid successive discretization algorithm (HSDA) is applied to the triple diode model (TDM) for the extraction of photovoltaic cells and panels parameters. Using HSDA, nine parameters are extracted for two photovoltaic cells and four photovoltaic panels. The HSDA algorithm results for root mean square errors (RMSE) and mean absolute errors (MAE) are compared with other ones obtained with the best algorithms from research literature for prove the performance of the algorithm considered. The comparison proves that the HSDA algorithm has lower or equal RMSE. Therefore, the decrease in RMSE varies from 0.4 % to 38.5 %, in the case of the RTC France silicon photovoltaic cell (La Radiotechnique Compele) and for the Photowatt - PWP201 photovoltaic panel the variation is from 0.01 % to 813 %. The RMSE for STM6–40 is around 4 times lower. The root mean square error decreases 4 times for Kyocera KC200GT and for PVM 752 GaAs the decrease varies between 33 % and 6 times. The comparison between the models used for photovoltaic cells shows that the TDM has given lower values for RMSE and MAE proving it is the best solution to analyze them.

A comparative analysis of variants of machine learning and time series models in predicting women’s participation in the labor force

Labor force participation of Egyptian women has been a chronic economic problem in Egypt. Despite the improvement in the human capital front, whether on the education or health indicators, female labor force participation remains persistently low. This study proposes a hybrid machine-learning model that integrates principal component analysis (PCA) for feature extraction with various machine learning and time-series models to predict women’s employment in times of crisis. Various machine learning (ML) algorithms, such as support vector machine (SVM), neural network, K-nearest neighbor (KNN), linear regression, random forest, and AdaBoost, in addition to popular time series algorithms, including autoregressive integrated moving average (ARIMA) and vector autoregressive (VAR) models, have been applied to an actual dataset from the public sector. The manpower dataset considered gender from different regions, ages, and educational levels. The dataset was then trained, tested, and evaluated. For performance validation, forecasting accuracy metrics were constructed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), R-squared (R2), and cross-validated root mean squared error (CVRMSE). Another Dickey-Fuller test was performed to evaluate and compare the accuracy of the applied models, and the results showed that AdaBoost outperforms the other methods by an accuracy of 100%. Compared to alternative works, our findings demonstrate a comprehensive comparative analysis for predicting women’s participation in different regions during an economic crisis.

Predicting Prefecture-Level Well-Being Indicators in Japan Using Search Volumes in Internet Search Engines: Infodemiology Study

Background In recent years, the adoption of well-being indicators by national governments and international organizations has emerged as an important tool for evaluating state governance and societal progress. Traditionally, well-being has been gauged primarily through economic metrics such as gross domestic product, which fall short of capturing multifaceted well-being, including socioeconomic inequalities, life satisfaction, and health status. Current well-being indicators, including both subjective and objective measures, offer a broader evaluation but face challenges such as high survey costs and difficulties in evaluating at regional levels within countries. The emergence of web log data as an alternative source of well-being indicators offers the potential for more cost-effective, timely, and less biased assessments. Objective This study aimed to develop a model using internet search data to predict well-being indicators at the regional level in Japan, providing policy makers with a more accessible and cost-effective tool for assessing public well-being and making informed decisions. Methods This study used the Regional Well-Being Index (RWI) for Japan, which evaluates prefectural well-being across 47 prefectures for the years 2010, 2013, 2016, and 2019, as the outcome variable. The RWI includes a comprehensive approach integrating both subjective and objective indicators across 11 domains, including income, job, and life satisfaction. Predictor variables included z score–normalized relative search volume (RSV) data from Google Trends for words relevant to each domain. Unrelated words were excluded from the analysis to ensure relevance. The Elastic Net methodology was applied to predict RWI using RSVs, with α balancing ridge and lasso effects and λ regulating their strengths. The model was optimized by cross-validation, determining the best mix and strength of regularization parameters to minimize prediction error. Root mean square errors (RMSE) and coefficients of determination (R2) were used to assess the model’s predictive accuracy and fit. Results An analysis of Google Trends data yielded 275 words related to the RWI domains, and RSVs were collected for 211 words after filtering out irrelevant terms. The mean search frequencies for these words during 2010, 2013, 2016, and 2019 ranged from −1.587 to 3.902, with SDs between 3.025 and 0.053. The best Elastic Net model (α=0.1, λ=0.906, RMSE=1.290, and R2=0.904) was built using 2010-2016 training data and 2-13 variables per domain. Applied to 2019 test data, it yielded an RMSE of 2.328 and R2 of 0.665. Conclusions This study demonstrates the effectiveness of using internet search log data through the Elastic Net machine learning method to predict the RWI in Japanese prefectures with high accuracy, offering a rapid and cost-efficient alternative to traditional survey approaches. This study highlights the potential of this methodology to provide foundational data for evidence-based policy making aimed at enhancing well-being.

Predicting Green Water Footprint of Sugarcane Crop Using Multi-Source Data-Based and Hybrid Machine Learning Algorithms in White Nile State, Sudan

Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m3 ton−1, followed closely by RF-SVR at 5.05 m3 ton−1, while the minimum MBE was 0.03 m3 ton−1 in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R2 values throughout all scenarios. Notably, the R2 values for dual hybrid models surpassed those of triple hybrid models. The highest Nash–Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP.

Effects of carrier structure parameters on diesel particulate filter capture performance based on grey relational analysis

To reduce the pressure drop of diesel particulate filter (DPF) as well as improve its collection efficiency. A mathematical model of DPF pressure drop and capture efficiency was established. This research systematically investigates the effects of channels per square inch (CPSI), wall thickness, and carrier ratio on DPF performance through grey relational analysis (GRA) and polynomial approximation algorithm (PAA). The findings reveal that DPF exhibits lower pressure drop and higher collection efficiency in the case where the carrier ratio, wall thickness, and CPSI are within the ranges of 1.40 to 1.53, 9.8 to 10.5 mil, and 200 to 300, respectively. Particularly, at a carrier ratio of 1.52 and a wall thickness of 10.38 mil, the pressure drop reaches a minimum value of 4.23 kPa, with a collection efficiency of 90.28%. Meanwhile, at a carrier ratio of 1.52 and a CPSI of 200, the pressure drop reaches a minimum value of 4.17 kPa, with a collection efficiency of 90.21%. The model expressions for pressure drop and collection efficiency derived from the PAA are: y 1 = 51.19 + 3.815 x 1 − 8.726 x 2 + 3.794 x 1 2 − 1.735 x 1 x 2 + 0.524 x 2 2 , and y 2 = 0.383 + 0.105 x 1 + 0.085 x 2 + 0.032 x 1 2 − 0.016 x 1 x 2 − 0.0032 x 2 2 . The foregoing models exhibit excellent predictive accuracy and goodness of fit for the DPF performance. Under the interactive influence of carrier ratio and wall thickness, the root mean square errors (RMSE) for pressure drop and collection efficiency are 0.0110 and 0.0002, respectively, with corresponding goodness of fit values of 0.9996 and 0.9985. As revealed by the GRA results, wall thickness exerts the most significant impact on DPF performance, presenting a GRG of 0.84 for filtration efficiency and 0.79 for pressure drop. The overall influence on both collection efficiency and pressure drop follows the descending order: wall thickness > carrier ratio > CPSI. This work has important engineering significance for optimizing the DPF capture performance and extending its working time.