SVR Model Research Articles

Predicting Motivation among GKP Recipients using Regression Techniques in Machine Learning: An Implementation of Rapid Miner

Recently, predictive analytics has found a place in many research areas. From business to healthcare, the revolutions of predictive analytics studies are constantly evolving to help decision makers identify the problem and make a wise decision. While a bigger impact has been reported on the development of predictive models in business studies, there has been very little effort that investigates the deployment of predictive models by using machine learning approaches specifically involving SMEs. Small business entrepreneurs (SMEs) are among the entities most affected because of the COVID-19 pandemic. Hence, this study aims to develop a model that could predict the motivation score of GKP recipients based on three factors: satisfaction, perceived value, and perceived expectation. Four different regression models, namely Linear, Ridge, Lasso, and SVR, were developed and evaluated as the best model by using Rapid Miner machine learning software tools. The GKIPP grant dataset has been used as a case study for estimating the motive of the GKP grant to evaluate the outcomes of various regression models. The findings indicate that linear and SVR models have produced highly accurate predictions about the motivation of GKP recipients. They have also produced a high proportion of R-square scores across all regression models, which is highly encouraging.

Design of Tool Wear Monitoring System in Bone Material Drilling Process

Biological bone materials, complex and anisotropic, require precise machining in surgeries. Bone drilling, a key technique, is susceptible to increased friction from tool wear, leading to excessive forces and high temperatures that can damage bone and surrounding tissues, affecting recovery. This study develops a monitoring platform to assess tool wear during bone drilling, employing an experimental setup that gathers triaxial force and vibration data. A recognition model using a bidirectional long short-term memory network (BI-LSTM) with a multi-head attention mechanism identified wear levels. This model, termed ABI-LSTM, was optimized and benchmarked against SVR, RNN, and CNN models. The results from implementing the ABI-LSTM-based monitoring system demonstrated its efficacy in detecting tool wear, thereby potentially reducing surgical risks such as osteonecrosis and drill breakage, and enhancing surgical outcomes.

Characterization of the volatile flavor profiles of black garlic using nanomaterial-based colorimetric sensor array, HS-SPME-GC/MS coupled with chemometrics strategies

This work investigated the feasibility of applying headspace solid phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC/MS) combining olfactory visualization for flavor characterization of black garlic. Volatile organic compounds (VOCs) analysis was performed to select important differential VOCs during black garlic processing. A multi-channels nanocomposite CSA assembled with two porous metal-organic frameworks was then developed to characterize flavor profiles changes during black garlic processing, and garlic samples during processing could be divided into five clusters, consistent with VOCs analysis. Artificial neural network (ANN) model outperformed other pattern recognition methods in discriminating processing stages. Furthermore, SVR model for odor sensory scores with the correlation coefficient for prediction set of 0.8919 exhibited a better performance than PLS model, indicating a preferable prediction ability for odor quality. This work demonstrated that the nanocomposite CSA combining appropriate chemometrics can offer an effective tool for objectively and rapidly characterizing flavor quality of black garlic or other food matrixes.

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

An accurate short-term passenger flow forecast of rural tourism can avoid accidents as much as possible. However, the short-term passenger flow of rural leisure tourism shows nonlinear, seasonal, random, and other complex characteristics. Meanwhile, the traditional forecasting methods are often difficult to achieve accurate forecasting. Therefore, this study now used the nonlinear mapping function in the support vector regression to convert the passenger traffic training sample into a high-dimensional feature space and established a linear decision function. Then the influence of periodicity on the prediction effect through seasonal index adjustment was reduced. Finally, event triggering combined with core embedding technology for tamper-proof detection was adopted to improve the security of the platform. The results showed that the minimum absolute error of prediction with improved SVM model was 0.27% compared with traditional model and autoregressive integrated moving average model. After the introduction of the Internet search factor, the traffic prediction result was more accurate, which was 0.0425 smaller than that without the introduction of the Internet search factor. When the concurrency was less than 100 times/s, the average response time difference before and after adding the core embedded program was small, indicating that the security of page tampering technology was high. This research method can effectively predict the passenger flow of rural leisure tourism industry and ensure the safety of the platform.

Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics

The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson’s product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction

In the field of data engineering in machine learning (ML), a crucial component is the process of scaling, normalization, and standardization. This process involves transforming data to make it more compatible with modeling techniques. In particular, this transformation is essential to ensure the suitability of the data for subsequent analysis. Despite the application of many conventional and relatively new approaches to ML, there remains a conspicuous lack of research, particularly in the geotechnical discipline. In this study, ML-based prediction models (i.e., RF, SVR, Cubist, and SGB) were developed to estimate the undrained shear strength (UDSS) of cohesive soil from the perspective of a wide range of data-scaling and transformation methods. Therefore, this work presents a novel ML framework based on data engineering approaches and the Cubist regression method to predict the UDSS of cohesive soil. A dataset including six different features and one target variable were used for building prediction models. The performance of ML models was examined considering the impact of the data pre-processing issue. For that purpose, data scaling and transformation methods, namely Range, Z-Score, Log Transformation, Box-Cox, and Yeo-Johnson, were used to generate the models. The results were then systematically compared using different sampling ratios to understand how model performance varies as various data scaling/transformation methods and ML algorithms were combined. It was observed that data transformation or data sampling methods had considerable or limited effects on the UDSS model performance depending on the algorithm type and the sampling ratio. Compared to RF, SVR, and SGB models, Cubist models provided higher performance metrics after applying the data pre-processing steps. The Box-Cox transformed Cubist model yielded the best prediction performance among the other models with an R2 of 0.87 for the 90% training set. Also, the UDSS prediction model generally yielded the best performance metrics when it was used with the transformed-based models (i.e., Box-Cox, Log, and Yeo-Johnson) than that of scaled-based (i.e., Range and Z-Score) models. The results show that the Cubist model has a higher potential for UDSS prediction, and data pre-processing methods have impacts on the predictive capacity of the evaluated regression models.

Predicting the Occurrence of Substituted and Unsubstituted, Polycyclic Aromatic Compounds in Coking Wastewater Treatment Plant Effluent using Machine Learning Regression

Organic contaminants such as polycyclic aromatic compounds (PACs) occurring in industrial effluents can not only persist in wastewater but transform into more toxic and mobile, substituted heterocyclic products during treatment. Thus, predicting the occurrence of PACs and their heterocyclic derivatives (HPACs) in coking wastewater is of utmost importance to reduce the environmental risks in water bodies that receive industrial effluents. Although HPACs can be monitored through sampling and analysis, the characterisation techniques used in their analyses are costly and time-consuming. In this study, we propose 3 distinct kernel-based machine learning (ML) models for predicting PACs including substituted HPACs and alkylated PACs occurring in coking wastewater. By using routinely measured wastewater quality data as input for our models, we predicted the occurrence of 14 HPACs in the final effluent of a coking wastewater treatment plant. Support Vector Machine based regression model (SVR) used for HPAC prediction showed the highest R2 of 0.83. Performance assessment of SVR model showed a mean absolute logarithmic error (MALE) of 0.46 and root mean square error (RMSE) of 0.073 ng/L. Comparatively, K-Nearest Neighbor and Random Forest models showed lower R2 of 0.75 and 0.76 respectively for HPAC prediction. Feature analysis attributed the superior predictability of SVR model likely to its higher weightage (81%) towards dissolved organic carbon and total ammonia as input variables. Both these variables could capture the underlying secondary PAC transformations likely occurring in the treatment plant. Partial dependence plots predicted that ammonia levels higher than 120 mg/L and DOC levels of 50–60 mg/L were likely linked to higher HPACs occurring in the final effluent. This work highlights the capability of kernel-based ML models in capturing nonlinear wastewater chemistry and offers a tool for monitoring trace organic contaminants released in coking effluents.

An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression.

Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

The content optimization of supplementary cementitious materials in coral sand-seawater cement mortar: A focus on mechanical properties and transportation costs

This study examines how supplementary cementitious materials (SCMs) such as fly ash (FA), blast furnace slag (BFS), and metakaolin (MK) affect the mechanical properties and transportation costs of coral sand-seawater cement mortar (CSSC). The Dinger-Funk equation was used to obtain the most densely packed design and preparation of coral sand and cementitious materials. The mechanical properties of CSSC were assessed utilizing the RMT testing apparatus. A thorough examination of the mechanical properties and transportation costs of CSSC was undertaken using the principle of value engineering and the effectiveness coefficient approach. Research results show that BFS10 has outstanding mechanical qualities in the binary cementitious system. Within the ternary cementitious system, the mechanical qualities typically exceed those of the binary system, with FA15BFS15 demonstrating the highest performance. The best appropriate mix ratio is FA15BFS15, as determined by a thorough examination and cost-benefit analysis. This ratio offers temporal sensitivity and maximized transit capacity. The SVR model is highly proficient in forecasting the compressive strength of CSSC, showcasing accurate prediction and data interpretation abilities. The research findings provide practical evidence for optimizing the proportion of CSSC in calcium sulfoaluminate cement, considering transportation expenses and algorithm prediction needs.

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Comparative Study of EMD based Modelling Techniques for Improved Agricultural Price Forecasting

Forecasting agricultural commodity prices is regarded as a challenging task due to its non-linear and non-stationary nature. As agriculture productionis highly reliant on various biological and agro-meteorological factors, traditional smoothing techniques as well as statistical models often fail tomodel such series satisfactorily. To capture such complex patterns effectively, different data-driven and self-adaptive techniques have been developedtime-to-time. Against this backdrop, in this paper, we have assessed the suitability of empirical mode decomposition (EMD)-based neural network andsupport vector regression (SVR) approaches for forecasting wholesale prices of three major potato markets namely, Agra, Bangalore, and Mumbai.As the benchmark models, autoregressive integrated moving average (ARIMA), time delay neural network (TDNN) and SVR models have beenemployed for the comparative evaluation. The experimental results clearly reveal the comparative superiority of the EMD-SVR model for the Agraand Bangalore markets and the EMD-TDNN model for the Mumbai market in terms of root mean squared error values and turning point predictions.Moreover, all the EMD-based models have performed better than the other competing models.

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Forecasting and extending streamflow is a critical aspect of hydrology, especially where the time series are locally unavailable for a variety of reasons. The necessity of preprocessing, model fine-tuning, feature selection, or sampling to enhance prediction outcomes for streamflow forecasting using ML techniques is evaluated in this study. In this regard, the monthly streamflow at Pol-Chehr station is analyzed using various monthly rainfall and streamflow time series data from different stations. The results of streamflow prediction in the k-folds cross-validator approach are generally better than those of the time series approach, except when raw data with no preprocessing or feature selection is used. Applying the simple SVR model to raw data leads to the weakest result, but using the GA-SVR model on raw data significantly increases the Nash coefficient by about 215% and 72%, decreases the NRMSE by about 48% and 36% in the k-fold and time series approaches, even with no feature selection. On the other hand, standardization produces highly accurate model predictions in both the k-fold and time series approaches, with a minimum Nash coefficient of 0.83 and 0.73 during the test period in the simple SVR model, respectively. Finally, using optimization algorithms like GA to fine-tune ML models and feature selection does not always yield improved prediction accuracy, but it depends on whether raw or preprocessed data is chosen. In conclusion, combining k-fold cross-validator and preprocessing typically yields highly accurate predictive results, with an R value exceeding 93.7% (Nash = 0.83, SI = 0.55, NRMSE = 0.09), without requiring any additional fine-tuning or optimization. Using feature selection is only significant when utilizing the TS approach as well.

Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Groundwater resources (GWR) play a crucial role in daily life, economic progress, and agricultural crop production, which makes groundwater level (GWL) prediction much more significant for enhanced management. However, one of the major challenges in investigating and estimating groundwater reduction and managing its resources is the absence of complete and reliable data. Therefore, the application of artificial intelligence (AI) models with the necessity for less data and better prediction accuracy is inevitable. The performances of hybrid models including the combination of particle swarm optimisation and grey wolf optimisation with extreme learning machine (ELM-PSOGWO) in estimating the GWL are investigated and evaluated against ELM-GWO, ELM-PSO, ELM, Twin-support vector regression (T-SVR) and SVR models based on the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutclife efficiency coefficient (NSC). Outcomes of this study showed that applied hybridised ELM-PSOGWO (R2 = 0.9969, RMSE = 0.0088, NSC = 0.9961, MAE = 0.0047) performed best, followed by ELM-GWO, ELM-PSO, ELM, TSVR, and SVR in predicting GWL. Based on the obtained results, it is evident that the ELM-PSOGWO model illustrates an excellent agreement with observed GWL data. To this end, this work reveals that hybrid optimisation algorithms can enhance performance of ELM model significantly in GWL prediction. Utilization of advanced AI techniques can well estimate groundwater systems, which saves resources and labor, employed conventionally.

Machine learning-assisted wide-gamut fluorescence visual test paper for propazine determination in fish and seawater samples

Molecularly imprinted polymer (MIP-QDs) with fluorescence quenching ability toward propazine was synthesized for propazine detection. b(Blue)-MIP-QDs were prepared using ZnCdS/ZnS QDs via reverse micro-emulsion, whereas r(red)-MIP-QDs were synthesized using CdSe/ZnS QDs. By utilizing graphene quantum dots (GQDs) as a stable fluorescence intensity reference, the wide-gamut fluorescence test paper was constructed on the basis of mixing b-MIP-QDs, r-MIP-QDs, and GQDs under the optimal ratio. When analyzing spiked propazine in fish and seawater samples using a test paper, satisfactory recoveries of 104.0 %–114.6 % and 92.0 %–96.4 % were obtained, with corresponding limits of detection of 5.0 μg/kg and 1.0 μg/L, respectively. The RGB extractor was utilized to extract the actual fluorescence color and construct a dataset consisting of R, G, and B values, as well as concentration data from 400 samples. The SVR model of Python 3.9.7 was used to obtain and analyze the concentration and feature data. After optimization, the constructed model achieved a correlation coefficient of 0.98 and an RMSE of only 1.81, indicating high prediction accuracy and excellent generalization ability that meet quenching prediction requirements. As an intelligent and rapid detection method, this model holds significant practical significance.

Cepair: an AI-powered and fog-based predictive CEP system for air quality monitoring

Air pollution is one of the influential problems threatening the environment and human health today. Therefore, it is critical to develop predictive systems for proactive decisions in solving this problem. Since the prediction of air pollution depends on several complicated factors such as the accuracy of meteorology reports, air pollution accumulation, traffic flow, and industrial emissions, the contribution of historical or real-time predictions to the solution of the problem is limited. To address the existing limitations, we propose a novel AI-powered and Fog-based predictive complex event processing system (CepAIr) for the prediction of future air pollution rates. CepAIr predicts the future air quality of pollutant gases using RNN, LSTM, CNN, and SVR models. Then, it sends the prediction results to decision-makers in an understandable format, enabling them to take proactive actions. Finally, we evaluate the performance of the CepAIr with SVR and DL models. Additionally, we examine CepAIr in terms of end-to-end network delay and measure its impact on the network. The extensive simulation results demonstrate that the CepAIr predicts future pollutant gas concentrations with DL models (especially with CNN) with a high success rate while guaranteeing minimum end-to-end network delay.

Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine.

The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need. 1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction. A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models' performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors. The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features. These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.

Development of a Measurement System Using Infrared Spectroscopy-Attenuated Total Reflectance, Principal Component Analysis and Artificial Intelligence for the Safe Quantification of the Nucleating Agent Sorbitol in Food Packaging.

Sorbitol derivatives and other additives are commonly used in various products, such as packaging or food packaging, to improve their mechanical, physical, and optical properties. To accurately and precisely evaluate the efficacy of adding sorbitol-type nucleating agents to these articles, their quantitative determination is essential. This study systematically investigated the quantification of sorbitol-type nucleating agents in food packaging made from impact copolymers of polypropylene (PP) and polyethylene (PE) using attenuated total reflectance infrared spectroscopy (ATR-FTIR) together with analysis of principal components (PCA) and machine learning algorithms. The absorption spectra revealed characteristic bands corresponding to the C-O-C bond and hydroxyl groups attached to the cyclohexane ring of the molecular structure of sorbitol, providing crucial information for identifying and quantifying sorbitol derivatives. PCA analysis showed that with the selected FTIR spectrum range and only the first two components, 99.5% of the variance could be explained. The resulting score plot showed a clear pattern distinguishing different concentrations of the nucleating agent, affirming the predictability of concentrations based on an impact copolymer. The study then employed machine learning algorithms (NN, SVR) to establish prediction models, evaluating their quality using metrics such as RMSE, R2, and RMSECV. Hyperparameter optimization was performed, and SVR showed superior performance, achieving near-perfect predictions (R2 = 0.9999) with an RMSE of 0.100 for both calibration and prediction. The chosen SVR model features two hidden layers with 15 neurons each and uses the Adam algorithm, balanced precision, and computational efficiency. The innovative ATR-FTIR coupled SVR model presented a novel and rapid approach to accurately quantify sorbitol-type nucleating agents in polymer production processes for polymer research and in the analysis of nucleating agent derivatives. The analytical performance of this method surpassed traditional methods (PCR, NN).

Streamflow prediction using support vector regression machine learning model for Tehri Dam

Accurate and reliable streamflow prediction is critical for optimising water resource management, reservoir flood operations, watershed management, and urban water management. Many researchers have published on streamflow prediction using techniques like Rainfall-Runoff modelling, Time series Models, Data-driven models, Artificial intelligence, etc. Still, there needs to be generalised method practise in the real world. The resolution of this issue lies in selecting different methods for a particular study area. This paper uses the Support vector regression machine learning model to predict the streamflow for the Tehri Dam, Uttarakhand, India, at the Daily and Ten Daily time steps. Two cases are considered in predicting daily and ten daily time steps. The first case includes four input variables: Discharge, Rainfall, Temperature, and Snow cover area. The second case comprises only three input variables: Rainfall, Temperature, and Snow cover area. Radial Kernel is used to overcome the space complexity in the datasets. The K-fold cross-validation is suitable for prediction as it averages the prediction error rate after evaluating the SVR model’s performance on various subsets of the training data. The streamflow data for daily and ten daily time steps have been collected from 2006 to 2020. The calibration period is from 2006 to 2016, and the validation period is from 2017 to 2020. Nash Sutcliffe Efficiency (NSE) and Coefficient of determination (R2) are used as the accuracy indicator in this manuscript. The lag has been observed in the daily prediction time series when three input variables are considered. For other scenarios, the respective model shows excellent results at both the temporal scale and the parametres, which play a vital role in prediction. The study also enhances the effect on the potential use of input parametres in the machine learning model.

Evaluation and analysis of water-regulation value of ecosystem in Qinghai area of Qilian Mountain National Park, China

Context Water-regulation services significantly affect local social and economic development in the Qinghai area of Qilian Mountain National Park, China, and also affect the role of a ‘solid reservoir’ in China. Aim The value and change trend of water regulation was assessed through the evaluation and prediction of the water-regulation value of ecosystems. Methods InVEST water yield model and SVR model are used to evaluate and predict the water-regulation value of ecosystem in this study. Key results Grassland plays an important role in water regulation, although the area of forestland is not large, its water-regulation monetary value in 2020 accounted for 13.92% of the total evaluation value. From 2010 to 2020, the physical value of water regulation increased by 2.24% and the monetary value increased ~0.97% annually. In 2030, the amount of physical and monetary value of water regulation will be increased. Conclusion Grassland plays an important role in water regulation. It is necessary to strengthen the asset management of water regulation and optimise the ecological resources. Implications The study findings are utilised to enhance monitoring and establish ecological big-data platform, thereby elevating the quality of national park development management and decision-making services.

Evaluation and machine learning prediction on thermal performance of energy walls in underground spaces as part of ground source heat pump systems

Energy walls (i.e. wall heat exchangers) exhibit more complex thermal performance than do energy piles and borehole heat exchangers in ground source heat pump systems, due to their individual geometry. This study conducted field tests of energy wall thermal performance to analyze the influence and interaction of the pipe burial depth, flow rate, heating load, and operation mode. To evaluate the energy wall thermal performance in multiple dimensions, three novel evaluation indices (energy utilization ratio, thermal saturation, and effectiveness) were defined. A thermal performance SVR prediction model of an energy wall was established based on 227 field-measured samples. The heat exchange power, energy utilization ratio, and effectiveness of the ground side pipe are approximately 15%, 8% and 64% greater than those of the excavated side pipe, respectively. Considering the interaction between the flow rate and heating load, blindly increasing the heating load is not the most effective way to improve the thermal performance. The influences of heating loads and operation modes on heat exchange power differs from that on the energy utility ratio and both side’s thermal saturations, indicating that there is a complex interaction between the heating loads and operation modes. The cross-verification and prediction results show that the optimized thermal performance of the SVR model exhibited satisfactory generalizability and prediction performance.