Improving Plot-Level Model of Forest Biomass: A Combined Approach Using Machine Learning with Spatial Statistics

Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. However, AGB estimates at the plot level suffer from many uncertainties. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates. To illustrate this issue, this study evaluates and compares the performance of different models for estimating plot-level forest AGB. These models include three different machine learning models [support vector machine (SVM), random forest (RF), and a radial basis function artificial neural network (RBF-ANN)], one spatial statistic model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, and RBF-ANN & P-BSHADE). The results show that the root mean square error, mean absolute error, and mean relative error of all combined models are substantially smaller than those of any individual model, with the RF & P-BSHADE combined method generating the smallest values. These results indicate that a combined approach using machine learning with spatial statistics, especially the RF & P-BSHADE model, improves the accuracy of plot-level AGB models. These research results contribute to the development of accurate large-forested-landscape AGB maps.

Download Full-text

Improving maps of forest aboveground biomass: A combined approach using machine learning with a spatial statistical model

10.5194/bg-2020-36 ◽

2020 ◽

Author(s):

Shaoqing Dai ◽

Xiaoman Zheng ◽

Lei Gao ◽

Chengdong Xu ◽

Shudi Zuo ◽

...

Keyword(s):

Machine Learning ◽

Spatial Statistics ◽

Aboveground Biomass ◽

Regional Scale ◽

Support Vector ◽

Environmental Covariates ◽

Eucalyptus Plantation ◽

Heterogeneous Landscapes ◽

Combined Models ◽

And Performance

Abstract. Aboveground biomass (AGB) estimates at the plot level plays a major part in connecting accurate single-tree AGB measurements to relatively difficult regional-scale AGB estimates. However, complex and spatially heterogeneous landscapes, where multiple environmental covariates (such as longitude, latitude, and forest structure) affect the spatial distribution of AGB, make upscaling of plot-level models more challenging. To address this challenge, this study proposes an approach that combines machine learning with spatial statistics to construct a more accurate plot-level AGB model. The study was conducted in a Eucalyptus plantation in Nanjing, China. We developed, evaluated, and compared the accuracy and performance of three different machine learning models [support vector machine (SVM), random forest (RF), and the radial basis function artificial neural network (RBF-ANN)], one spatial statistics model (P-BSHADE), and three combinations thereof (SVM & P-BSHADE, RF & P-BSHADE, RBF-ANN & P-BSHADE) for forest AGB estimates based on AGB data from 30 sample plots and their corresponding environmental covariates. The results show that the performance indices RMSE, nRMSE, MAE, and MRE of all combined models are substantially smaller than those of any individual models, with the RF & P-BSHADE combined method giving the smallest value. These results demonstrate clearly that combined models, especially the RF & P-BSHADE model, can improve the accuracy of plot-level AGB models and reduce uncertainty on plot-level AGB estimates or even on large-forested-landscape AGB estimates. These research results are important because they reduce the uncertainty in estimates of the regional carbon balance.

Download Full-text

MACHINE LEARNING METHODS IN MONITORING OPERATING BEHAVIOUR OF MARINE TWO-STROKE DIESEL ENGINE

Transport ◽

10.3846/transport.2020.14038 ◽

2020 ◽

Vol 35 (5) ◽

pp. 462-473

Author(s):

Aleksandar Vorkapić ◽

Radoslav Radonja ◽

Karlo Babić ◽

Sanda Martinčić-Ipšić

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Fuel Consumption ◽

Performance Monitoring ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Support Vector ◽

Operating Parameters ◽

Detection Model ◽

Modelling Framework

The aim of this article is to enhance performance monitoring of a two-stroke electronically controlled ship propulsion engine on the operating envelope. This is achieved by setting up a machine learning model capable of monitoring influential operating parameters and predicting the fuel consumption. Model is tested with different machine learning algorithms, namely linear regression, multilayer perceptron, Support Vector Machines (SVM) and Random Forests (RF). Upon verification of modelling framework and analysing the results in order to improve the prediction accuracy, the best algorithm is selected based on standard evaluation metrics, i.e. Root Mean Square Error (RMSE) and Relative Absolute Error (RAE). Experimental results show that, by taking an adequate combination and processing of relevant sensory data, SVM exhibit the lowest RMSE 7.1032 and RAE 0.5313%. RF achieve the lowest RMSE 22.6137 and RAE 3.8545% in a setting when minimal number of input variables is considered, i.e. cylinder indicated pressures and propulsion engine revolutions. Further, article deals with the detection of anomalies of operating parameters, which enables the evaluation of the propulsion engine condition and the early identification of failures and deterioration. Such a time-dependent, self-adopting anomaly detection model can be used for comparison with the initial condition recorded during the test and sea run or after survey and docking. Finally, we propose a unified model structure, incorporating fuel consumption prediction and anomaly detection model with on-board decision-making process regarding navigation and maintenance.

Download Full-text

Estimation of Soil Cohesion Using Machine Learning Method: A Random Forest Approach

Advances in Civil Engineering ◽

10.1155/2021/8873993 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Hai-Bang Ly ◽

Thuy-Anh Nguyen ◽

Binh Thai Pham

Keyword(s):

Machine Learning ◽

Random Forest ◽

Soil Properties ◽

Clay Content ◽

Absolute Error ◽

Experimental Methods ◽

Liquid Limit ◽

Support Vector ◽

Data Set ◽

Soil Cohesion

Soil cohesion (C) is one of the critical soil properties and is closely related to basic soil properties such as particle size distribution, pore size, and shear strength. Hence, it is mainly determined by experimental methods. However, the experimental methods are often time-consuming and costly. Therefore, developing an alternative approach based on machine learning (ML) techniques to solve this problem is highly recommended. In this study, machine learning models, namely, support vector machine (SVM), Gaussian regression process (GPR), and random forest (RF), were built based on a data set of 145 soil samples collected from the Da Nang-Quang Ngai expressway project, Vietnam. The database also includes six input parameters, that is, clay content, moisture content, liquid limit, plastic limit, specific gravity, and void ratio. The performance of the model was assessed by three statistical criteria, namely, the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrated that the proposed RF model could accurately predict soil cohesion with high accuracy (R = 0.891) and low error (RMSE = 3.323 and MAE = 2.511), and its predictive capability is better than SVM and GPR. Therefore, the RF model can be used as a cost-effective approach in predicting soil cohesion forces used in the design and inspection of constructions.

Download Full-text

Forecasting Of Covid-19 Cases Using Machine Learning Approach

Current Respiratory Medicine Reviews ◽

10.2174/1573398x17666210129131009 ◽

2021 ◽

Vol 17 ◽

Author(s):

Sachin Kumar ◽

Karan Veer

Keyword(s):

Machine Learning ◽

Regression Model ◽

Model Performance ◽

Real Data ◽

Absolute Error ◽

Viral Disease ◽

Support Vector ◽

Family Welfare ◽

Accuracy Score ◽

Learning Approaches

Aims: The objective of this research is to predict the covid-19 cases in India based on the machine learning approaches. Background: Covid-19, a respiratory disease caused by one of the coronavirus family members, has led to a pandemic situation worldwide in 2020. This virus was detected firstly in Wuhan city of China in December 2019. This viral disease has taken less than three months to spread across the globe. Objective: In this paper, we proposed a regression model based on the Support vector machine (SVM) to forecast the number of deaths, the number of recovered cases, and total confirmed cases for the next 30 days. Method: For prediction, the data is collected from Github and the ministry of India's health and family welfare from March 14, 2020, to December 3, 2020. The model has been designed in Python 3.6 in Anaconda to forecast the forecasting value of corona trends until September 21, 2020. The proposed methodology is based on the prediction of values using SVM based regression model with polynomial, linear, rbf kernel. The dataset has been divided into train and test datasets with 40% and 60% test size and verified with real data. The model performance parameters are evaluated as a mean square error, mean absolute error, and percentage accuracy. Results and Conclusion: The results show that the polynomial model has obtained 95 % above accuracy score, linear scored above 90%, and rbf scored above 85% in predicting cumulative death, conformed cases, and recovered cases.

Download Full-text

An Ensemble Machine Learning Technique for Functional Requirement Classification

Symmetry ◽

10.3390/sym12101601 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1601

Author(s):

Nouf Rahimi ◽

Fathy Eassa ◽

Lamiaa Elrefaei

Keyword(s):

Machine Learning ◽

Requirement Engineering ◽

Support Vector ◽

Functional Requirement ◽

Machine Learning Technique ◽

Engineering Software ◽

Ensemble Machine Learning ◽

Learning Technique ◽

Combined Models

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.

Download Full-text

Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

Sustainability ◽

10.3390/su12052022 ◽

2020 ◽

Vol 12 (5) ◽

pp. 2022 ◽

Cited By ~ 1

Author(s):

Kieu Anh Nguyen ◽

Walter Chen ◽

Bor-Shiun Lin ◽

Uma Seeboonruang

Keyword(s):

Machine Learning ◽

Random Forest ◽

Fuzzy Inference ◽

Absolute Error ◽

Organic Content ◽

Rank Test ◽

Support Vector ◽

Inference System ◽

Erosion Rates ◽

Northern Taiwan

This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

Download Full-text

Vibration Analysis of Shaft Misalignment Using Machine Learning Approach under Variable Load Conditions

Shock and Vibration ◽

10.1155/2020/1650270 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

A. M. Umbrajkaar ◽

A. Krishnamoorthy ◽

R. B. Dhumale

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Feature Selection ◽

Condition Monitoring ◽

Support Vector ◽

Discrete Wavelet ◽

Variable Load ◽

Combined Approach ◽

Shaft Misalignment ◽

Load Conditions

The Industry 4.0 revolution is insisting strongly for use of machine learning-based processes and condition monitoring. In this paper, emphasis is given on machine learning-based approach for condition monitoring of shaft misalignment. This work highlights combined approach of artificial neural network and support vector machine for identification and measure of shaft misalignment. The measure of misalignment requires more features to be extracted under variable load conditions. Hence, primary objective is to measure misalignment with a minimum number of extracted features. This is achieved through normalization of vibration signal. An experimental setup is prepared to collect the required vibration signals. The normalized time domain nonstationary signals are given to discrete wavelet transform for features extraction. The extracted features such as detailed coefficient is considered for feature selection viz. Skewness, Kurtosis, Max, Min, Root mean square, and Entropy. The ReliefF algorithm is used to decide best feature on rank basis. The ratio of maximum energy to Shannon entropy is used in wavelet selection. The best feature is used to train machine learning algorithm. The rank-based feature selection has improved classification accuracy of support vector machine. The result obtained with the combined approach are discussed for different misalignment conditions.

Download Full-text

Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

International Journal of Information Technology and Computer Science ◽

10.5815/ijitcs.2021.03.02 ◽

2021 ◽

Vol 13 (3) ◽

pp. 16-32

Author(s):

Ahmed Hassan Mohammed Hassan ◽

◽

Arfan Ali Mohammed Qasem ◽

Walaa Faisal Mohammed Abdalla ◽

Omer H. Elhassan

Keyword(s):

Machine Learning ◽

Polynomial Regression ◽

Mean Squared Error ◽

Absolute Error ◽

Future Perspective ◽

Support Vector ◽

Squared Error ◽

Vector Machines ◽

The World ◽

Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

Download Full-text

Evaluation on Sports Facility Resource in Primary School Using a Combined Approach of Unsupervised Machine Learning: a Case Study of Shanghai, China

10.20944/preprints201912.0015.v1 ◽

2019 ◽

Author(s):

Jun Xia ◽

Pei-Jie Chen ◽

Ji-Hong Wang ◽

Jie Zhuang ◽

Zhen-Bo Cao ◽

...

Keyword(s):

Machine Learning ◽

Primary Schools ◽

Analysis Of Covariance ◽

School Type ◽

Combined Approach ◽

Combined Method ◽

Unsupervised Machine Learning ◽

Individual School ◽

Indoor And Outdoor

The aim of this study is (a) to develop, test, and employ a combined method of unsupervised machine learning to objectively assess the condition of sports facility in primary schools (PSSFC) and (b) examine the examine the geographical and typological association with PSSFC. Based on the Sixth National Sports Facility Census (NSFC), six PSSFC indicators (indoor and outdoor facility included) were selected as the measurements and decomposed by using the t-stochastic neighbor embedding (t-SNE). Thereafter, the Fuzzy C-mean (FCM) algorithm was used to cluster the same type of PSSFC with selecting the optimum numbers of evaluation level. Overall 845 primary schools in Shanghai, China were recruited and tested by this combined approach of unsupervised machine learning. In addition, the two-way analysis of covariance was used to examine the location and types of school associated with PSSFC variables in each level. The combined method was found to have acceptable reliability and good interpretability, differentiating PSSFC into five gradient levels. The characteristics of PSSFC differ by the location and school type of individual school. Our findings are conducive to the regionalized and personalized intervention and promotion on the children’s physical activity (PA) upon the practical situation of particular schools.

Download Full-text

Design and analysis of an efficient machine learning based hybrid recommendation system with enhanced density-based spatial clustering for digital e-learning applications

Complex & Intelligent Systems ◽

10.1007/s40747-021-00509-4 ◽

2021 ◽

Author(s):

S. Bhaskaran ◽

Raja Marappan

Keyword(s):

Machine Learning ◽

Data Mining ◽

Decision Making ◽

Support Vector Machine ◽

Absolute Error ◽

Support Vector ◽

E Learning ◽

Public Datasets ◽

Hybrid Recommender ◽

New Strategies

AbstractA decision-making system is one of the most important tools in data mining. The data mining field has become a forum where it is necessary to utilize users' interactions, decision-making processes and overall experience. Nowadays, e-learning is indeed a progressive method to provide online education in long-lasting terms, contrasting to the customary head-to-head process of educating with culture. Through e-learning, an ever-increasing number of learners have profited from different programs. Notwithstanding, the highly assorted variety of the students on the internet presents new difficulties to the conservative one-estimate fit-all learning systems, in which a solitary arrangement of learning assets is specified to the learners. The problems and limitations in well-known recommender systems are much variations in the expected absolute error, consuming more query processing time, and providing less accuracy in the final recommendation. The main objectives of this research are the design and analysis of a new transductive support vector machine-based hybrid personalized hybrid recommender for the machine learning public data sets. The learning experience has been achieved through the habits of the learners. This research designs some of the new strategies that are experimented with to improve the performance of a hybrid recommender. The modified one-source denoising approach is designed to preprocess the learner dataset. The modified anarchic society optimization strategy is designed to improve the performance measurements. The enhanced and generalized sequential pattern strategy is proposed to mine the sequential pattern of learners. The enhanced transductive support vector machine is developed to evaluate the extracted habits and interests. These new strategies analyze the confidential rate of learners and provide the best recommendation to the learners. The proposed generalized model is simulated on public datasets for machine learning such as movies, music, books, food, merchandise, healthcare, dating, scholarly paper, and open university learning recommendation. The experimental analysis concludes that the enhanced clustering strategy discovers clusters that are based on random size. The proposed recommendation strategies achieve better significant performance over the methods in terms of expected absolute error, accuracy, ranking score, recall, and precision measurements. The accuracy of the proposed datasets lies between 82 and 98%. The MAE metric lies between 5 and 19.2% for the simulated public datasets. The simulation results prove the proposed generalized recommender has a great strength to improve the quality and performance.

Download Full-text