Support Vector Machine Learning Algorithm Research Articles

Multi-step-ahead prediction of water levels using machine learning: A comparative analysis in the Vietnamese Mekong Delta

This study evaluates the efficacy of five machine learning algorithms Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), Light Gradient Boosting Machine Regressor (LGBM), and Linear Regression (LR) in predicting water levels in the Vietnamese Mekong Delta's tidal river system, a complex nonlinear hydrological phenomenon. Using daily maximum, minimum, and mean water level data from the Cao Lanh gauging station on the Tien River (2000-2020), models were developed to forecast water levels one, three, five, and seven days in advance. Performance was assessed using Nash-Sutcliffe Efficiency, coefficient of determination, Root Mean Square Error, and Mean Absolute Error. Results indicate that all models performed well, with SVR consistently outperforming others, followed by RF, DT, and LGBM. The study demonstrates the viability of machine learning in water level prediction using solely historical water level data, potentially enhancing flood warning systems, water resource management, and agricultural planning. These findings contribute to the growing knowledge of machine learning applications in hydrology and can inform sustainable water resource management strategies in delta regions.

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee

Abstract Civet coffee, or kopi luwak, has attracted significant attention within the coffee industry in certain regions due to its distinct flavor characteristics that arise from the digestive processes of the civet. The ability to discriminate between wild and feeding civet coffee is of major importance in upholding the industry’s established standards of quality and transparency. This study introduces an innovative method to differentiate between these two coffee types using Headspace Gas Chromatography-Mass Spectrometry (HS-GCMS) with advanced data analysis using machine-learning techniques. This study encompasses seven samples collected from various regions, all of which were subjected to analysis in both roasted and unroasted forms. The data analysis consisted of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), which revealed clear trends that were mostly influenced by processing, indicating how roasting affects the chemical profiles of various coffee types. Further classification was conducted using Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. SVM exhibited notable accuracy at 90%, effectively discriminating between wild and feeding civet coffee, whereas RF outperformed it with a remarkable 100% accuracy. This study contributes to the field of coffee characterization by presenting a robust approach to discriminate between roasted and unroasted wild and feeding civet coffee. This tool serves as a starting step for a valuable resource for both farmers and customers, as it promotes sustainable and ethical practices while retaining the distinct flavor characteristics of this exceptional specialty coffee.

Headspace gas chromatography with various sample preparation and chemometric approaches to improve discrimination of wild and feeding civet coffee

Abstract Civet coffee, or kopi luwak, has attracted significant attention within the coffee industry in certain regions due to its distinct flavor characteristics that arise from the digestive processes of the civet. The ability to discriminate between wild and feeding civet coffee is of major importance in upholding the industry’s established standards of quality and transparency. This study introduces an innovative method to differentiate between these two coffee types using Headspace Gas Chromatography-Mass Spectrometry (HS-GCMS) with advanced data analysis using machine-learning techniques. This study encompasses seven samples collected from various regions, all of which were subjected to analysis in both roasted and unroasted forms. The data analysis consisted of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), which revealed clear trends that were mostly influenced by processing, indicating how roasting affects the chemical profiles of various coffee types. Further classification was conducted using Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. SVM exhibited notable accuracy at 90%, effectively discriminating between wild and feeding civet coffee, whereas RF outperformed it with a remarkable 100% accuracy. This study contributes to the field of coffee characterization by presenting a robust approach to discriminate between roasted and unroasted wild and feeding civet coffee. This tool serves as a starting step for a valuable resource for both farmers and customers, as it promotes sustainable and ethical practices while retaining the distinct flavor characteristics of this exceptional specialty coffee.

High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction

Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 107 and the limit of detection (LOD) was as low as 1 × 10−10 M. Then, SERS technology combined with support vector machine (SVM), decision tree (DT), Naive Bayes (NB) and other machine learning algorithms were used to classify and identify the three TCM decoctions, and the classification accuracy rate was as high as 97.78 %. In summary, it is expected that the proposed method combining SERS and machine learning method will have a high development in the practical application of multi-component analytes in TCM.

Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

The performance of Logistic Regression, Decision Tree, KNN, Naive Bayes and SVM for identifying Automotive Cybersecurity Attack and Prevention: An Experimental Study

The automotive industry has witnessed a significant increase in cyber threats as vehicles become more connected and reliant on software-driven systems. To safeguard against these threats, effective cybersecurity measures must be implemented. This paper explores the use of Support Vector Machine (SVM) algorithms as a means of bolstering automotive cybersecurity attack prevention. SVM algorithms have demonstrated remarkable capabilities in various domains due to their ability to handle complex and high-dimensional data. By leveraging SVM algorithms, this research aims to enhance the detection and prevention of cybersecurity attacks targeting automotive systems.
 The proposed approach involves training SVM models using labeled datasets that include both normal and anomalous driving scenarios. By analyzing various features and patterns extracted from the data, the SVM models can learn to differentiate between normal and malicious behaviour. These models can then be used in real-time to identify and mitigate potential cyber threats.
 The results of this study is compared with other machine learning algorithm like Logistic Regression, Naive Bayes, KNN, Decision Tree, highlight the effectiveness of SVM algorithms in detecting and preventing automotive cybersecurity attacks. The models exhibit high accuracy rates in distinguishing between normal and anomalous behavior, providing a robust defense mechanism against potential threats.
 In conclusion, this research emphasizes the significance of leveraging SVM learning algorithms for automotive cybersecurity attack prevention. By harnessing the power of machine learning, automotive systems can be fortified against cyber threats, ensuring the safety and integrity of vehicles and their passengers.

Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms

To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms. The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-IV v2.0 (MIMIC-IV v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA). A total of 3 295 patients with septic shock were included, with 2 164 surviving and 1 131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2 307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95%CI) was 0.795-0.849], 0.823 (95%CI was 0.796-0.849), and 0.810 (95%CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95%CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95%CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score III (SAPS III), the highest WBC, acute physiology score III (APS III), the lowest Na+, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95%CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APS III score, SAPS III score, and sequential organ failure assessment (SOFA) score] with AUC (95%CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca2+, Hb, WBC, SAPS III score, APS III score, Na+, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

Comparing Breast Cancer Prediction Models

Abstract: In this research study, five machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and K-Nearest Neighbors (KNN)—were applied to the Breast Cancer Wisconsin Diagnostic dataset. The subsequent results underwent a thorough performance evaluation and comparison among these diverse classifiers. The primary objective was to predict and diagnose breast cancer using machine learning algorithms, determining the most effective approach based on factors such as the confusion matrix, accuracy, and precision. Notably, the findings highlight that the Support Vector Machine outperformed all other classifiers, achieving the highest accuracy at 97.2%.

Prediction of COVID-19 By Applying Supervised Machine Learning Techniques

In the era of modern lifestyle, humans going to modernize everything with innovative brains finding the solution for the latest pandemic of this century which is COVID-19. The diagnosis of COVID-19 is a critical and slow process, to tackle this we decided to diagnose COVID-19 quickly by using machine learning (ML) due to its prediction for time-saving. Therefore, in this research, a patient admitted to the intensive care unit (ICU) has vital signs such as heart rate, oxygen saturation (SPO2), blood pressure, and respiratory rate using wearable sensors which are the main vital signs of COVID-19. the predictive models using machine learning algorithms support vector machine (SVM), random forest (RF), and bagging tree (BG) for more accurate predictions. The proposed prediction model results used dataset, and models trained with Support Vector Machine in AUC= 0.9.1, ROC = 0.90 curve, Random Forest AUC = 0.920, and ROC = 0.91, and Bagging Tree AUC = 0.913, and ROC = 0.91 which shows the prediction accuracy. This research indicates machine learning's capability to predict COVID19 quickly instead of conventional methods that have previously been adopted in pandemics such as influenza.

Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks

Early diagnosis of plant diseases is essential in reducing economic losses for farmers and increasing production efficiency. Therefore, Computer-Aided Diagnosis (CAD) systems supported by artificial intelligence technologies can be developed to help diagnose diseases quickly and accurately by examining the symptoms and signs in plant leaves. In this study, Multi-head Attention Mechanism Depthwise Separable Convolution Inception Reduction Network (MDSCIRNet) architecture, an image-based deep convolutional neural network, is proposed for classifying potato leaf diseases. The main components of the MDSCIRNet architecture are depthwise separable convolution (DSC) and a multi-head attention mechanism. The proposed architecture has been compared with modern algorithms developed with DSC technology, such as Xception, MobileNet, and deep learning algorithms, such as ResNet101, InceptionV3, and EfficientNetB2, to evaluate its performance in the classification process. In addition, hybrid methods developed with the classical machine learning algorithms Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Adaptive Boosting (AdaBoost), and MDSCIRNet model, integrated deep learning model, hard voting ensemble learning model. Suggested methods such as these were also used in the experimental process. Moreover, techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), and Hypercolumn were used to improve the image quality of the data set. In the experimental process, the MDSCIRNet deep learning architecture achieved 99.24% accuracy in the study using the original dataset. While a 99.11% accuracy rate was achieved with the integrated deep learning model and hard voting ensemble learning model, the highest success rate of 99.33% was performed in the study conducted with the MDSCIRNet + SVM method. This study contributes to developing new and effective strategies in the agricultural industry for the early diagnosis and control of potato plant diseases. Machine learning-based approaches offer the potential to minimize economic losses and increase productivity in production by allowing farmers to intervene early.

Predicting land cover driven ecosystem service value using artificial neural network model

Understanding the synergies and trade-offs of major cities' ecosystem services is vital to mitigating regional ecological and environmental risks and enhancing human well-being in this era of rapid urbanization and global climate change. This study aimed to assess and predict the land use- and land cover (LULC)-driven ecosystem service value (ESV) dynamics in Arkansas’s capital city, Little Rock. Historical LULC data were derived by applying support vector machine learning algorithms to Landsat satellite imagery. The benefit transfer method was utilized to identify nine types of ecosystem services and their corresponding economic values. A cellular automata artificial neural network model was used to simulate future potential LULC and ESV patterns. Vegetation accounted for more than 94% of total ESV over the past two decades. However, a 38.40% expansion of built-up areas resulted in a 45.28% decrease in vegetated areas, which reduced total ESV from $3619.73 × 106 to $2563.81 × 106 during 2003–2023. By 2033, the city’s urban area will expand to 72.75% of the total area and will witness further declines of 30.35 km2 in vegetation, 19.30 km2 in barren soil, and 1.69 km2 in waterbody areas. Consequently, the ESVs of these natural landscapes will decline by $708.58 × 106, $44.87 × 106, and $15.69 × 106, respectively. Provisioning services will be most affected, followed by supporting, regulating, and cultural services. The study findings provide reference information to policymakers and the local government for use in adopting sustainable land management policies, thereby promoting the ecological value of Little Rock.

Detection of fake news from social media using support vector machine learning algorithms

Never happened before in human history the spreading of fake news; now, the development of the Worldwide Web and the adoption of social media have given a pathway for people to spread misinformation to the world. Everyone is using the Internet, creating and sharing content on social media, but not all the information is valid, and no one is verifying the originality of the content. Identifying the content's essence is sometimes complicated for researchers and intelligent researchers. For example, during Covid-19, misinformation spread worldwide about the outbreak, and much false information spread faster than the virus. This misinformation will create a problem for the public and mislead people into taking the proper medicine. This work will help us to improve the prediction rate. Here we investigate the ability of machine learning classifiers and deep learning models: Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest and K-Nearest Neighbor. Deep learning models include Convolutional Neural Networks and Long Short-Term Memory (LSTM). The various types of machine learning and deep learning models will be trained and tested using the Covid-19 dataset (1,375,592 tweets).

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy

BackgroundThe incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis. PurposeRaman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach. MethodTumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data). ResultsThe AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes. ConclusionRaman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

Stock Volatility Prediction Using Machine Learning during Covid-19

Predicting the stock prices’ movement has been considered a tedious task due to the stock market's volatile behavior. This study is an attempt to predict stock prices based on 15 trading factors by applying deep learning algorithm support vector machine (SVM) on daily price data of three stocks of COVID-19, collected from secondary sources and processed through Python software. The finding of this study suggests that the linear kernel gives 53%, the polynomial kernel gives 46% and the radical basis function (RBF) kernel gives 62% validation accuracy, which shows the RBF kernel predictive ability is highest than others during high volatility. The current study contributes to the stock return literature-originated machine learning algorithms during the unprecedented market condition. The finding of this study is helpful for investors and traders who calculate stock return in their portfolio diversified decisions, regulators, and policymakers in the formulation of their regulations & implementation decisions while the market condition is unprecedented. Thus, the support vector machine (SVM) algorithm offers an accurate prediction of stock return to investors who invest during high volatility.

Prediction of electric power performance of the exhaust waste heat recovery system of an automobile with thermoelectrical generator under real driving conditions by means of machine learning algorithms

In internal combustion engines, approximately 40% of the thermal power obtained from fuel burning is thrown out to the environment from the exhaust system. Implementations of waste heat recovery systems with thermoelectrical generator over exhaust system have become widespread as it is the waste heat resource with the highest temperature in a vehicle. During literature research, experiments related to waste heat recovery under real road conditions are very few and no study on estimation of system performance by machine learning algorithms has been found; therefore, Toyota Corolla has designed an air-cooled waste heat recovery system using 3 thermoelectrical generators for the exhaust system of the automobile. During the road tests, temperature and electric power generation values obtained as per gear, vehicle speed and engine speed have been recorded. In the study, a dataset consisting of 10 attributes was created with each record as a result of the path test. Using this dataset, Random Forest, Support Vector Machine (SVM) and Naive Bayes machine learning algorithms estimated the electrical power to be generated from the thermoelectric generator recovery system. In the study, 7 electric power classification estimates were made. In the estimation process, 76% of the dataset was used for training and 24% was used for testing. In terms of estimation success; an estimation success of 96.6% has been achieved by Random Forest method; 94.6% by Support Vector Machine (SVM) method; and 76.7% by Naive Bayes method. The results show that prospective electricity generation estimation can be achieved with high level of accuracy.

Integrative analysis of long noncoding RNAs dysregulation and synapse-associated ceRNA regulatory axes in autism

Autism spectrum disorder (ASD) is a complex disorder of neurodevelopment, the function of long noncoding RNA (lncRNA) in ASD remains essentially unknown. In the present study, gene networks were used to explore the ASD disease mechanisms integrating multiple data types (for example, RNA expression, whole-exome sequencing signals, weighted gene co-expression network analysis, and protein-protein interaction) and datasets (five human postmortem datasets). A total of 388 lncRNAs and five co-expression modules were found to be altered in ASD. The downregulated co-expression M4 module was significantly correlated with ASD, enriched with autism susceptibility genes and synaptic signaling. Integrating lncRNAs from the M4 module and microRNA (miRNA) dysregulation data from the literature identified competing endogenous RNA (ceRNA) network. We identified the downregulated mRNAs that interact with miRNAs by the miRTarBase, miRDB, and TargetScan databases. Our analysis reveals that MIR600HG was downregulated in multiple brain tissue datasets and was closely associated with 9 autism-susceptible miRNAs in the ceRNA network. MIR600HG and target mRNAs (EPHA4, MOAP1, MAP3K9, STXBP1, PRKCE, and SCAMP5) were downregulated in the peripheral blood by quantitative reverse transcription polymerase chain reaction analysis (false discovery rate <0.05). Subsequently, we assessed the role of lncRNA dysregulation in altered mRNA levels. Experimental verification showed that some synapse-associated mRNAs were downregulated after the MIR600HG knockdown. BrainSpan project showed that the expression patterns of MIR600HG (primate-specific lncRNA) and synapse-associated mRNA were similar in different human brain regions and at different stages of development. A combination of support vector machine and random forest machine learning algorithms retrieved the marker gene for ASD in the ceRNA network, and the area under the curve of the diagnostic nomogram was 0.851. In conclusion, dysregulation of MIR600HG, a novel specific lncRNA associated with ASD, is responsible for the ASD-associated miRNA-mRNA axes, thereby potentially regulating synaptogenesis.

SERS and machine learning based effective feature extraction for detection and identification of amphetamine analogs

Surface-enhanced Raman spectroscopy (SERS) is extensively researched in diverse disciplines due to its sensitivity and non-destructive nature. It is particularly considered a potential and promising technology for rapid on-site screening in drug detection. In this investigation, a technique was developed for fabricating nanocrystals of Ag@Au SNCs. Ag@Au SNCs, as the basic material of SERS, can detect amphetamine at concentrations as low as 1 μg/mL. The Ag@Au SNCs exhibits a strong surface plasmon resonance effect, which amplifies molecular signals. The SERS spectra of ten substances, including amphetamine and its analogs, showed a strong peak signal. To establish a qualitative distinction, we examined the Raman spectra and conducted density functional theory (DFT) calculations on the ten aforementioned species. The DFT calculation enabled us to determine the vibrational frequency and assign normal modes, thereby facilitating the qualitative differentiation of amphetamines and its analogs. Furthermore, the SERS spectrum of the ten mentioned substances was analysed using the support vector machine learning algorithm, which yielded a discrimination accuracy of 98.0 %.

Vibration-based prediction of residual fatigue life for composite laminates through frequency measurements

Fiber reinforced polymer (FRP) structures may experience cumulative fatigue damage during their service life which can lead to structural failure. This paper focuses on predicting the residual fatigue life of FRP structures using vibration parameters. The relationship between residual fatigue life and natural frequencies is examined through modal testing and fatigue measurements on FRP beam specimens. Two prediction methods; semi-empirical models and machine learning (ML) algorithms, are utilized. The semi-empirical models are derived from existing “residual stiffness” models based on the relationship between bending stiffness and flexural frequencies. ML algorithms Support Vector Machine (SVM) and Artificial Neural Network (ANN), are developed for fatigue life prediction. Experimental validation is performed using measured frequencies during fatigue testing of FRP beams. The ML algorithms can use multimode frequencies unlike single mode of semi-empirical models. The verification results show that the ML algorithms can be used to predict the residual fatigue life with the selection of the appropriate mode of frequency. The results show that ML algorithms outperform single-mode frequency inputs, and the use of higher modes of measured frequencies improves the precision of fatigue life prediction. An inverse algorithm based on SVM exhibits higher prediction accuracy and stability, even with limited training samples.

Fusion of sentinel-1 SAR and sentinel-2 MSI data for accurate Urban land use-land cover classification in Gondar City, Ethiopia

Effective urban planning and management rely on accurate land cover mapping, which can be achieved through the combination of remote sensing data and machine learning algorithms. This study aimed to explore and demonstrate the potential benefits of integrating Sentinel-1 SAR and Sentinel-2 MSI satellite imagery for urban land cover classification in Gondar city, Ethiopia. Synthetic Aperture Radar (SAR) data from Sentinel-1A and Multispectral Instrument (MSI) data from Sentinel-2B for the year 2023 were utilized for this research work. Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms were utilized for the classification process. Google Earth Engine (GEE) was used for the processing, classification, and validation of the remote sensing data. The findings of the research provided valuable insights into the performance evaluation of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for image classification using different datasets, namely Sentinel 2B Multispectral Instrument (MSI) and Sentinel 1A Synthetic Aperture Radar (SAR) data. When applied to the Sentinel 2B MSI dataset, both SVM and RF achieved an overall accuracy (OA) of 0.69, with a moderate level of agreement indicated by the Kappa score of 0.357. For the Sentinel 1A SAR data, SVM maintained the same OA of 0.69 but showed an improved Kappa score of 0.67, indicating its suitability for SAR image classification. In contrast, RF achieved a slightly lower OA of 0.66 with Sentinel 1A SAR data. However, when the datasets of Sentinel 2B MSI and Sentinel 1A SAR were combined, SVM achieved an impressive OA of 0.91 with a high Kappa score of 0.80, while RF achieved an OA of 0.81 with a Kappa score of 0.809. These findings highlight the potential of fusing satellite data from multiple sources to enhance the accuracy and effectiveness of image classification algorithms, making them valuable tools for various applications, including land use mapping and environmental monitoring.

Estimating Canopy Resistance Using Machine Learning and Analytical Approaches

Canopy resistance is a key parameter in the Penman–Monteith (P–M) equation for calculating evapotranspiration (ET). In this study, we compared a machine learning algorithm–support vector machine (SVM) and an analytical solution (Todorovic, 1999) for estimating canopy resistances. Then, these estimated canopy resistances were applied to the P–M equation for estimating ET; as a benchmark, a constant (fixed) canopy resistance was also adopted for ET estimations. ET data were measured using the eddy-covariance method above three sites: a grassland (south Ireland), Cypress forest (north Taiwan), and Cryptomeria forest (central Taiwan) were used to test the accuracy of the above two methods. The observed canopy resistance was derived from rearranging the P–M equation. From the measurements, the average canopy resistances for the grassland, Cypress forest, and Cryptomeria forest were 163, 346, and 321 (s/m), respectively. Our results show that both methods tend to reproduce canopy resistances within a certain range of intervals. In general, the SVM model performs better, and the analytical solution systematically underestimates the canopy resistances and leads to an overestimation of evapotranspiration. It is found that the analytical solution is only suitable for low canopy resistance (less than 100 s/m) conditions.