Genetic Algorithm Support Vector Regression Research Articles

A data-driven combined prediction method for the demand for intensive care unit healthcare resources in public health emergencies

BackgroundPublic health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the “last line of defense” for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies.ObjectiveThis study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves.MethodsA demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients.ResultsData on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic.ConclusionThe first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies.

Single-point curved fiber optic pulse sensor for physiological signal prediction based on the genetic algorithm-support vector regression model

For hypertensive patients, a real-time human physiological signal monitoring system helps to track blood pressure status and provides valuable data to clinicians for early diagnosis and timely intervention. Optical fiber pulse sensing has superior conditions such as resistance to electromagnetic interference and richness of measured pulse characteristics. In this study, a monitoring system based on a single-point curved fiber pulse sensor (CFPS) was used to collect the pulse wave signal of a human radial artery. The features of the pulse wave signal were used to estimate the pulse wave transit time (PTT) and Blood Pressure—systolic blood pressure (SBP) and diastolic blood pressure (DBP)—based on support vector regression (SVR) optimized by a genetic algorithm (GA) (GA-SVR model). The results show that the root mean square error (RMSE) of SBP, DBP and PTT were 1.571 mmHg, 3.250 mmHg and 5.719 ms, respectively, and the results met the Advancement of Medical Instrumentation (AAMI) criteria. Therefore, a real-time pulse wave monitoring system based on a single-point CFPS can well predict human physiological signals such as PTT, SBP and DBP.

Cementing Quality Prediction in the Shunbei Block Based on Genetic Algorithm and Support Vector Regression

There are a number of factors that can affect the quality of cementing, and they constrain each other. Current cementing quality prediction methods are still in the stage of development, and it is difficult to establish an analytical model for cementing quality prediction that meets the strict requirements of cementing design. In order to accurately predict the cementing quality in the Shunbei block of the Northwest Oilfield, in this study, we established a cementing quality prediction model based on support vector regression (SVR) and optimized the penalty parameter and kernel parameter by using grid search (GS), a Bayesian optimization algorithm (BOA), and a genetic algorithm (GA), which improve the prediction accuracy of SVR. The results show that the smallest root-mean-square error and average relative error (2.318% and 7.30%, respectively) and the highest accuracy are achieved when using GA–SVR as compared to SVR, GS–SVR, and BOA–SVR. Therefore, GA–SVR is suitable for cementing quality prediction in the Shunbei block.

Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 ∘C to 25 ∘C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles.

Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm.

The scientific layout design of the groundwater pollution monitoring network (GPMN) can provide high quality groundwater monitoring data, which is essential for the timely detection and remediation of groundwater pollution. The simulation optimization approach was effective in obtaining the optimal design of the GPMN. The ant colony optimization (ACO) algorithm is an effective method for solving optimization models. However, the parameters used in the conventional ACO algorithm are empirically adopted with fixed values, which may affect the global searchability and convergence speed. Therefore, a parameter-iterative updating strategy-based ant colony optimization (PIUSACO) algorithm was proposed to solve this problem. For the GPMN optimal design problem, a simulation-optimization framework using PIUSACO algorithm was applied in a municipal waste landfill in BaiCheng city in China. Moreover, to reduce the computational load of the design process while considering the uncertainty of aquifer parameters and pollution sources, a genetic algorithm-support vector regression (GA-SVR) method was proposed to develop the surrogate model for the numerical model. The results showed that the layout scheme obtained using the PIUSACO algorithm had a significantly higher detection rate than ACO algorithm and random layout schemes, indicating that the designed layout scheme based on the PIUSACO algorithm can detect the groundwater pollution occurrence timely. The comparison of the iteration processes of the PIUSACO and conventional ACO algorithms shows that the global searching ability is improved and the convergence speed is accelerated significantly using the iteration updating strategy of crucial parameters. This study demonstrates the feasibility of the PIUSACO algorithm for the optimal layout design of the GPMN for the timely detection of groundwater pollution.

Global supply sustainability assessment of critical metals for clean energy technology

The enduring challenges posed by the supply sustainability of critical metals have long served as a barrier to the low carbon energy transition and the regulation of global climate issues. This study aims to determine whether the supply of essential minerals can keep pace with the global demand associated with the shift towards low-carbon energy sources. In this study, critical metal minerals required by clean energy technology are taken as the research object, the supply and demand background of critical minerals are sorted and compared. By utilizing the genetic algorithm support vector regression (GA-SVR) predictive model, the research scrutinized the supply trends and developed twelve different supply and demand schemes under various development scenarios. These facilitated the assessment of the future supply sustainability of critical metals for clean energy technology. Results demonstrate that under a basic scenario, the global supply of lithium, cobalt, and nickel is projected to reach 200,000 tons, 300,000 tons, and 4 million tons, respectively, by 2030. Under a high supply scenario, these figures rise to 350,000 tons, 500,000 tons, and 5 million tons. From a global supply trend perspective, lithium, cobalt, and nickel resources are projected to grow by 2–4, 1–3, and 1.6–2 times, respectively. Examining supply and demand, the highest risk of supply shortages lies with lithium resources, followed by cobalt. Meanwhile, nickel faces no imminent risk of supply shortage. By 2030, if the compound annual growth rate of global demand for lithium and cobalt hits 15%, supply sustainability will reach a critical juncture, leading to a supply deficit. Lithium resources may encounter this critical demand growth rate even earlier. Although the supply risks for cobalt and nickel currently appear minimal, questions around the long-term availability and sustainability of these resources persist. Ensuring the sustainable supply of these resources necessitates a well-structured and efficient system for recycling secondary resources, technological advancements in the mining industry, and improvements in material strength.

Intelligent identification algorithm and key point detection of abnormal vibration of transmission tower based on machine learning

Abstract In some special geological areas, the inclination and displacement of transmission line towers are relatively common, which should be analyzed from various factors. Transmission towers are the support structure for overhead transmission lines and play a pivotal role in the safe operation of the power grid. Transmission lines are widely distributed. Transmission poles and towers are generally built in areas with poor geological conditions such as goaf, river bed and slope. Under severe weather conditions, the force on the tower may be affected to a certain extent, causing the tower to tilt, deform, or even collapse, and thus causing a wide range of power system failures, which have a great impact on people’s production and life. Based on this objective problem, this paper has mainly studied the intelligent recognition methods and key technologies based on machine learning. In the experimental study of support vector machine (SVM) model based on machine learning, the average training time of genetic algorithm support vector regression (GA-SVR) was the longest, reaching 1.462 s. The average training duration of double chain quantum genetic algorithm-least squares support vector regression (DCQGA-LSSVR) was the shortest, with 0.156 s. The average pose error of double chain quantum genetic algorithm-support vector regression (DCQGA-SVR) was the smallest, only 0.136, while the average attitude error of genetic algorithm-least squares support vector regression (GA-LSSVR) was the highest, reaching 0.45. Therefore, it is of the great significance to analyze abnormal vibration of the transmission towers based on machine learning method.

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

Based on the counted power system emission factors of North China Power Grid, a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm (GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients. The time series SVR carbon emission prediction model is constructed, while the GA is improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to train and test the SVR model. The results show that the GA–SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed.

A high efficiency CO2 concentration interval optimization method for lettuce growth

The decline in carbon fertilization effects has shifted scientific focus toward the efficient and suitable regulation of CO2 concentration ([CO2]) for plant growth. In this study, the rapid A/CO2 response curve (RAC) data of lettuce were analyzed statistically under nine photosynthetic photon flux densities (PPFDs) and four temperatures. An efficient CO2 supplementation interval acquisition method based on the frequency distribution characteristics of RACs was proposed. The characteristic subsections of jumping were obtained depending on the frequency distribution of RACs. The cumulative contribution rate (CCR) of the characteristic subsections were >97 %, which showed the efficiency of the method. Additionally, U-chord curvature theory was used to simultaneously obtain the optimal regulated [CO2] for the same RAC curves, and the results showed that the [CO2] obtained by U-chord length were all within the interval obtained by the method, which proved the rationality of the method. The [CO2] interval supplement improved the daily CO2 exchange rate by 20.27 % and 21.64 % at 150 and 200 μmol·m−2·s−1, and increased the lettuce fresh biomass by 26.78 % at 150 μmol·m−2·s−1. Based on the interval of [CO2] efficient utilization regulation at various temperatures and PPFDs, a genetic algorithm-support vector regression model was built with R2 of the model was >0.84 and the root mean square error was <35.2256 μmol·mol−1. In conclusion, the [CO2] interval obtained by this method has a positive effect on lettuce growth. This work provides a new method for obtaining high-efficiency supplementary concentration of CO2 during the growth of lettuce.

Multi-objective optimal regulation model and system based on whole plant photosynthesis and light use efficiency of lettuce

Lettuce growth and light energy consumption in a plant factory with artificial lighting (PFAL) were studied, and whole plant photosynthetic rate (ACO2) and light use efficiency (LUE) data were obtained on different days after planting (DAP) under different photosynthetic photon flux densities (PPFDs). Genetic algorithm‐support vector regression (GA-SVR) was used to construct the ACO2 and LUE prediction models. The coefficient of determination (R2) between the predicted and measured values of the ACO2 model was 0.97 and the root mean square error (RMSE) was 0.42 μmol·mol−1·plant−1·min−1, and R2 between the predicted and measured values of the LUE model was 0.97 and RMSE was 0.36%. The ACO2 and LUE prediction models were used as the objective functions, and multi-objective search was performed by the non-dominated sorting genetic algorithm II (NSGA-II) and the distance-based knee point detection method were used to obtained the optimal equilibrium solution for different DAPs. The optimal equilibrium solutions were used as the basis to establish the light regulation model based on lettuce DAP with R2 of 0.99. To validate the effect of model regulation, a lettuce light regulation system was built using an artificial climate chamber for a 30-day system validation. The results showed that compared with the traditional quantitative light supplementation method, the dry matter of model regulation significantly increased by 39.23% and 29.48% compared with quantitative PPFD150 (μmol·m−2·s−1) and PPFD200 (μmol·m−2·s−1). Model regulation increased the number of total light quanta consumed by 1.39% over PPFD150 and decreased by 23.96% over PPFD200; however, plant productivity increased by 35.35% and 33.14%, respectively. Model regulation significantly reduced the number of light quanta consumed per unit mass of lettuce production by 24.35% for PPFD150 and 41.54% for PPFD200, and LUE of light-emitting diode energy into dry matter was significantly increased by 33.49% and 75.09%. Therefore, the light regulation model based on multi-objective optimization in this study could improve crop yield and increase LUE.

Analyzing the effect of light on lettuce Fv/Fm and growth by machine learning

Varied plant growth cultivations require different light conditions. To explore them with lettuce (Lactuca sativa L. cv. ‘Hong Kong glass’), five photosynthetic photon flux density (PPFD, 50, 150, 300, 450 and 600 μmol·m−2·s−1) levels were set in completely controlled circumstances. Using ordinary least squares (OLS), the appropriate PPFD was determined depending on the dark fluorescence characteristics. Following 14 h of treatment, a new PPFD of 200 μmol·m−2·s−1 was employed to monitor the adaptation process obtained using the genetic algorithm-support vector regression (GA-SVR) model. The results showed that Fv/Fm declined as PPFDs increased except for L50, and OLS with R2 and RMSE of 0.9710 and 0.0416 suggested 140.80 μmol·m−2·s−1 was the ideal PPFD. Little changed in the new PPFD adaptation procedure for, L50, L150, and L300. During 42 h of adaptation, the discretized curves of Fv/Fm using the GA-SVR model at various PPFDs revealed that Fv/Fm increasingly approached and eventually reached the same level. The quick change was in [290, 480] μmol·m−2·s−1 depending on the probability density distributions. The difference in change rates between the two PPFD treatments decreased, indicating that lettuce's light tolerance improved after the two phases. The largest sum Fv/Fm in L150 indicated a maintained growth state. Full-cycle cultivation revealed that 150 and 250 μmol·m−2·s−1 had the highest fresh biomass. In conclusion, in [290, 480] μmol·m−2·s−1, short-term high light could improve plant light adaptability in variable light. In the constant light of the plant factory with artificial lighting (PFAL), it was recommended to utilize the light intensity of 140.80 μmol·m−2·s−1, which can both maintain growth and save light energy. A more suitable solution might be chosen depending on the cultivation requirements.

An improved pore-solid fractal model for predicting coastal saline soil hydraulic properties based on changepoints determined by genetic algorithm-support vector regression

Coastal soil is an important reserve land resource. The accurate prediction of soil hydraulic properties plays an important role in understanding the improvement of soil properties after reclamation in coastal areas. With the development of modern mathematical theory, the pore-solid fractal (PSF) model has become an important basis for simulating soil hydraulic properties. The accuracy of PSF model depends on the accurate acquisition of changepoints and fractal dimensions. Previous studies have found that micro-CT scanning combined with image processing could accurately obtain fractal dimensions. However, we still need soil water retention data to determine changepoints, which greatly limits the application of fractal models for estimating soil hydraulic properties. In this case, we determined the relationship between changepoints and soil physical and chemical properties and established a genetic algorithm-support vector regression (GA-SVR) model to predict changepoints. Then an improved PSF model was adopted to predict soil water retention curve in coastal areas of Jiangsu Province based on image processing. The results showed that soil physical and chemical properties changed the soil water movement and changepoints by affecting soil pore distribution. In coastal saline soil, four parameters (BD, silt, EC, SOM) were selected to predict changepoints. The mass fractal dimension (Dm) was mainly influenced by the porosity and the heterogeneity of pore space. The porosity, pore diameter and specific surface area were the determining factors of DS value. Reclamation activities in coastal reclamation area changed Dm value but had no clear effect on DS value. By combining predicted changepoints, measured water content at the suction of changepoints and fractal dimensions, the accuracy of the PSF model had been greatly improved.

Understanding the influencing factors and mechanisms (land use changes and check dams) controlling changes in the soil organic carbon of typical loess watersheds in China

AbstractStudying the distribution patterns and controlling mechanisms of soil organic carbon (SOC) based on the comprehensive performance of vegetation restoration and check dams at the watershed scale is important for understanding carbon cycling processes in nature. Two typical watersheds (Xinshui River and Zhujiachuan watershed) of the Loess Plateau were selected to evaluate the factors affecting the change in SOC content, and then the key factors were considered in the genetic algorithm‐support vector regression (GA‐SVR) model to predict SOC content. The results showed that the topography, vegetation, and soil characteristics had significant effects on the SOC content in the upland hillslopes, while the SOC content in the check dams was significantly affected by depth and soil characteristics. The soil organic carbon storage (TSOC) in the check dams could be evaluated and predicted by the vegetation index (NGRDI) and area of the subwatershed. The GA‐SVR model had good prediction accuracy and stable performance in predicting SOC content. According to the model simulation results, bulk density (BD), mean weight diameter (MWD), elevation, NGRDI, clay ratio (CR), and slope could be used to predict the surface SOC content of the Loess Plateau. Furthermore, depth, CR, MWD, BD, and median particle size (D50) could be applied in the model to predict the SOC content at different depths in the check dams. This study explored the potential control factors of SOC content and predicted SOC content from multiple angles, which can provide basic support for the study of the carbon sequestration on the Loess Plateau.

COMPARISON OF SARIMA, SVR, AND GA-SVR METHODS FOR FORECASTING THE NUMBER OF RAINY DAYS IN BENGKULU CITY

The number of rainy days is a calculation of the rainy days that occur in one month. In recent years, there has been a decrease in rainy days in some parts of Indonesia. One of the areas at risk of quite a high decreasing number of rainy days is the Bengkulu City area. The decrease in the number of rainy days is one of the impacts caused by climate change. The community will feel the impact of climate change-related to the season, especially those working in the agricultural sector. In compiling the planting calendar, it is necessary to consider the seasons to estimate water availability. This study aimed to forecast the data on the number of rainy days in Bengkulu City in the period January 2000 to December 2020 using the Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Genetic Algorithm Support Vector Regression (GA-SVR) methods. The criteria for selecting the best model used was Mean Absolute Deviation (MAD). The MAD value in the SARIMA method was 4,16, 5,07 in the SVR model, and 3,67 in the GA-SVR model. Based on these results, it can be concluded that the GA-SVR model is the best model for forecasting the number of rainy days in Bengkulu City.

Remaining Useful Life prediction of rolling bearings based on risk assessment and degradation state coefficient

Prediction of Remaining Useful Life (RUL) of bearings is very important for the condition-based maintenance of the rotating machinery. In order to predict the RUL more accurately, an approach of RUL prediction based on risk assessment and degradation state coefficient is proposed. The Mahalanobis Distance (MD) is calculated depending on the characteristics of vibration signals in the time domain and time–frequency domain. The features in the time–frequency domain are extracted by using Variational Mode Decomposition-Singular Value Decomposition (VMD-SVD). The monotonously increasing Health Indicator (HI) is obtained by using MD1-CUMSUM. The risk assessment is proposed to adaptively determine the thresholds of initial fault and failure, which is a trade-off between the false alarm rate and sensitivity. The RUL prediction of the testing bearing is completed based on the Genetic Algorithm-Support Vector Regression (GA-SVR) and Modified Health Indicator (MHI) with the degradation state coefficient. The proposed approach is verified by using two experimental vibration signal datasets. The results show that the proposed method has good capability to predict the RUL of rolling bearings.

A state of health estimation method for electric vehicle Li-ion batteries using GA-PSO-SVR

State of health (SOH) is the ratio of the currently available maximum capacity of the battery to the rated capacity. It is an important index to describe the degradation state of a pure electric vehicle battery and has an important reference value in evaluating the health level of the retired battery and estimating the driving range. In this study, the random forest algorithm is first used to find the most important health factors to lithium-ion batteries based on the dataset released by National Aeronautics and Space Administration (NASA). Then the support vector regression (SVR) algorithm is developed to predict the SOH of a lithium-ion battery. The genetic algorithm-particle swarm optimization (GA-PSO) algorithm is brought forward to optimize the parameter values of the SVR, which could improve the estimation accuracy and convergence speed. The proposed SOH estimation method is applied to four batteries and gets a root mean square error (RMSE) of 0.40% and an average absolute percentage error (MAPE) of 0.56%. In addition, the method is also compared with genetic algorithm-support vector regression (GA-SVR) and particle swarm optimization-support vector regression (PSO-SVR), respectively. The results show that (i) compared with the PSO-SVR method, the proposed method can decrease the average RMSE by 0.10%, and the average MAPE by 0.17%; (ii) compared with the GA-PSO method, number of iterations under the proposed method can be reduced by 7 generations.

Assessing Chilling Injury in Cucumber Seedlings Using Chlorophyll Fluorescence Based on a Quantum Genetic Algorithm and Support Vector Regression Model

HighlightsA method is proposed to analyze chilling injury in cucumber seedlings.A mutual information method was adopted to determine model output.Different modeling approaches were compared for improving accuracy.The optimal model input type was determined and discussed.Abstract. Low temperature limits photosynthesis and leads to reduced production in plants. Accurate monitoring of the physiological state of cucumber (Cucumis sativus L.) seedlings and assessing their chilling injury are of great importance for the off-season cucumber industry. Dark chlorophyll fluorescence parameters have been proven to be efficient for revealing environmental stress on plant growth. We used seedlings of cucumber cultivar Bonai 14-3 to test a nested set of fluorescence parameters to assess the response characteristics of dark chlorophyll fluorescence parameters during eight days at low temperatures. Mutual information analysis showed that the dark chlorophyll fluorescence parameter Fv/Fo, an indicator of the potential activity of photosystem II, gave the strongest correlation with stage of chilling injury. We compared three Fv/Fo prediction models with different inputs, constructed using a quantum genetic algorithm support vector regression method. The model showed the highest accuracy when the inputs were Fv and Fo measured before low-temperature treatment, coupled with temperature, and duration. The coefficient of determination of the training and testing sets were both greater than 0.98. The root mean square error and the mean absolute error were both less than 0.15. This model was able to achieve accurate prediction of Fv/Fo for seedlings in a low-temperature environment, providing a potential method for non-destructive analysis and diagnosis of cucumber chilling injury. Keywords: Chlorophyll fluorescence parameters, Low temperature, Mutual information, Prediction.

Optimization of the thermophysical properties of the thermal barrier coating materials based on GA-SVR machine learning method: illustrated with ZrO2 doped DyTaO4 system

It is a critical issue to reduce the thermal conductivity and increase the thermal expansion coefficient of ceramic thermal barrier coating (TBC) materials in the course of their utilization. To synthesize samples with different composition and measure their thermal conductivity by the traditional experimental approaches is time-consuming and expensive. Most classic and empirical models work inefficiently and inaccurately when researchers attempt to predict the thermophysical properties of TBC materials. In this research project, we tentatively exploit a Genetic Algorithm-Support Vector Regression (GA-SVR) machine learning model to study the thermophysical properties, illustrated with the potential TBC materials ZrO2 doped DyTaO4, which has resulted in the lowest thermal conductivity in rare earth tantalates RETaO4 system. Meanwhile, we employ statistical parameters of correlation coefficient (R2) and mean square error (MSE) to evaluate the accuracy and reliability of the model. The results reveal that this model has brought about high correlation coefficients of thermal conductivity and thermal expansion coefficient (99.8% and 99.9%, respectively), while the MSE values are 0.00052 and 0.00019, respectively. The doping concentration of ZrO2 was optimized to reach as low as 0.085–0.095, so as to reduce their thermal conductivity further and increase their thermal expansion. This model provides an accurate and reliable option for researchers to design ceramic thermal barrier coating materials.

Research on prediction model of tractor sound quality based on genetic algorithm

The noise comfort of a tractor is a key factor in the evaluation of tractor comfort, and it has a significant impact on the evaluation of the overall performance evaluation of a tractor. This paper used the rating scale method to evaluate the sound quality of each operation state and position of the tractor, and obtain the evaluation value of the tractor noise. In addition, objective parameters of the sound (including the sound pressure level, A-weighted sound pressure level, loudness, sharpness, roughness, and fluctuation strength) were calculated. By considering objective parameters of sound quality as input and sound quality evaluation values as output, we established back propagation neural network (BPNN) model and support vector regression (SVR) model. Furthermore, the initial weights and thresholds of the BPNN model, penalty parameters, insensitive loss parameters, and kernel function parameters of the SVR model were optimized by genetic algorithm (GA). After verification of the experimental results, it was observed that the GA could improve the prediction accuracy of the model and significantly reduce the extreme errors. Compared with the prediction results of other prediction models, the GA–SVR model predicted the sound quality value of tractor noise with higher accuracy and stability.

Self-optimizer data-mining method for aquifer level prediction

Abstract Groundwater management requires accurate methods for simulating and predicting groundwater processes. Data-based methods can be applied to serve this purpose. Support vector regression (SVR) is a novel and powerful data-based method for predicting time series. This study proposes the genetic algorithm (GA)–SVR hybrid algorithm that combines the GA for parameter calibration and the SVR method for the simulation and prediction of groundwater levels. The GA–SVR algorithm is applied to three observation wells in the Karaj plain aquifer, a strategic water source for municipal water supply in Iran. The GA–SVR's groundwater-level predictions were compared to those from genetic programming (GP). Results show that the randomized approach of GA–SVR prediction yields R2 values ranging between 0.88 and 0.995, and root mean square error (RMSE) values ranging between 0.13 and 0.258 m, which indicates better groundwater-level predictive skill of GA-SVR compared to GP, whose R2 and RMSE values range between 0.48–0.91 and 0.15–0.44 m, respectively.