PM2.5 Estimation using Supervised Learning Models

Present era of Urbanization, mechanization, and globalization has attracted more and more Air pollution problems. However, PM 2.5 (Particulate Matter) majorly present at air, having diameter below 2.5 μm. With its high concentration leading to health issues such as lung cancer, cardiovascular disease, respiratory disease etc. With respect to this, presented work approach is building of supervised learning models, XGBoost(Extreme Gradient Boosting) along with MLR(Multiple Linear Regression),RF(Random Forest) and MLP (Multilayer Perceptron) to estimate PM2.5 congregation. The air quality data of city Changping in Beijing is taken into consideration for Analaysis. The accuracy of prediction of the four approaches is measured by using contrasting discovered value verses predicted value of PM2.5 with the help of three measuring matrices. The consequences reveals that the Random Forest algorithm outperforms other data mining strategies for the considered data. Prediction of PM2.5 concentrations will assist governing bodies in warning people who are at peak risk, and taking right measures to reduce its quantity in air also to reduce its impact on human life.

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PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

Atmosphere ◽

10.3390/atmos10070373 ◽

2019 ◽

Vol 10 (7) ◽

pp. 373 ◽

Cited By ~ 23

Author(s):

Mehdi Zamani Joharestani ◽

Chunxiang Cao ◽

Xiliang Ni ◽

Barjeece Bashir ◽

Somayeh Talebiesfandarani

Keyword(s):

Deep Learning ◽

Random Forest ◽

Meteorological Data ◽

Fine Particulate Matter ◽

Model Performance ◽

Remote Sensing Data ◽

Health Concern ◽

Gradient Boosting ◽

High Concentration ◽

Extreme Gradient Boosting

In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

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A Comparative Analysis of Machine Learning Models for Prediction of Insurance Uptake in Kenya

10.20944/preprints202010.0186.v1 ◽

2020 ◽

Author(s):

Nelson Yego ◽

Juma Kasozi ◽

Joseph Nkrunziza

Keyword(s):

Machine Learning ◽

Random Forest ◽

Characteristic Curve ◽

Confusion Matrix ◽

Gradient Boosting ◽

Support Vector ◽

Sampled Data ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

The role of insurance in financial inclusion as well as in economic growth is immense. However, low uptake seems to impede the growth of the sector hence the need for a model that robustly predicts uptake of insurance among potential clients. In this research, we compared the performances of eight (8) machine learning models in predicting the uptake of insurance. The classifiers considered were Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, K Nearest Neighbors, Decision Tree, Random Forest, Gradient Boosting Machines and Extreme Gradient boosting. The data used in the classification was from the 2016 Kenya FinAccess Household Survey. Comparison of performance was done for both upsampled and downsampled data due to data imbalance. For upsampled data, Random Forest classifier showed highest accuracy and precision compared to other classifiers but for down sampled data, gradient boosting was optimal. It is noteworthy that for both upsampled and downsampled data, tree-based classifiers were more robust than others in insurance uptake prediction. However, in spite of hyper-parameter optimization, the area under receiver operating characteristic curve remained highest for Random Forest as compared to other tree-based models. Also, the confusion matrix for Random Forest showed least false positives, and highest true positives hence could be construed as the most robust model for predicting the insurance uptake. Finally, the most important feature in predicting uptake was having a bank product hence bancassurance could be said to be a plausible channel of distribution of insurance products.

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Performance of Statistical and Machine Learning-Based Methods for Predicting Biogeographical Patterns of Fungal Productivity in Forest Ecosystems

10.21203/rs.3.rs-122045/v1 ◽

2020 ◽

Author(s):

Albert Morera ◽

Juan Martínez de Aragón ◽

José Antonio Bonet ◽

Jingjing Liang ◽

Sergio de-Miguel

Keyword(s):

Machine Learning ◽

Random Forest ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Learning Approaches ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models ◽

Modelling Approaches

Abstract BackgroundThe prediction of biogeographical patterns from a large number of driving factors with complex interactions, correlations and non-linear dependences require advanced analytical methods and modelling tools. This study compares different statistical and machine learning models for predicting fungal productivity biogeographical patterns as a case study for the thorough assessment of the performance of alternative modelling approaches to provide accurate and ecologically-consistent predictions.MethodsWe evaluated and compared the performance of two statistical modelling techniques, namely, generalized linear mixed models and geographically weighted regression, and four machine learning models, namely, random forest, extreme gradient boosting, support vector machine and deep learning to predict fungal productivity. We used a systematic methodology based on substitution, random, spatial and climatic blocking combined with principal component analysis, together with an evaluation of the ecological consistency of spatially-explicit model predictions.ResultsFungal productivity predictions were sensitive to the modelling approach and complexity. Moreover, the importance assigned to different predictors varied between machine learning modelling approaches. Decision tree-based models increased prediction accuracy by ~7% compared to other machine learning approaches and by more than 25% compared to statistical ones, and resulted in higher ecological consistence at the landscape level.ConclusionsWhereas a large number of predictors are often used in machine learning algorithms, in this study we show that proper variable selection is crucial to create robust models for extrapolation in biophysically differentiated areas. When dealing with spatial-temporal data in the analysis of biogeographical patterns, climatic blocking is postulated as a highly informative technique to be used in cross-validation to assess the prediction error over larger scales. Random forest was the best approach for prediction both in sampling-like environments as well as in extrapolation beyond the spatial and climatic range of the modelling data.

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On-Ground Distributed COVID-19 Variant Intelligent Data Analytics for a Regional Territory

Wireless Communications and Mobile Computing ◽

10.1155/2021/1679835 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Umrah Zadi Khuhawar ◽

Isma Farah Siddiqui ◽

Qasim Ali Arain ◽

Mokhi Maan Siddiqui ◽

Nawab Muhammad Faseeh Qureshi

Keyword(s):

Random Forest ◽

Linear Regression ◽

Supervised Learning ◽

Prediction Models ◽

Learning Algorithm ◽

High Accuracy ◽

Estimation Methods ◽

Gradient Boosting ◽

Extreme Gradient Boosting ◽

Short Term Prediction

The onset of the COVID-19 pandemic and the subsequent transmission among communities has made the entire human population extremely vulnerable. Due to the virus’s contagiousness, the most powerful economies in the world are struggling with the inadequacies of resources. As the number of cases continues to rise and the healthcare industry is overwhelmed with the increasing needs of the infected population, there is a requirement to estimate the potential future number of cases using prediction methods. This paper leverages data-driven estimation methods such as linear regression (LR), random forest (RF), and XGBoost (extreme gradient boosting) algorithm. All three algorithms are trained using the COVID-19 data of Pakistan from 24 February to 31 December 2020, wherein the daily resolution is integrated. Essentially, this paper postulates that, with the help of values of new positive cases, medical swabs, daily death, and daily new positive cases, it is possible to predict the progression of the COVID-19 pandemic and demonstrate future trends. Linear regression tends to oversimplify concepts in supervised learning and neglect practical challenges present in the real world, often cited as its primary disadvantage. In this paper, we use an enhanced random forest algorithm. It is a supervised learning algorithm that is used for classification. This algorithm works well for an extensive range of data items, and also it is very flexible and possesses very high accuracy. For higher accuracy, we have also implemented the XGBoost algorithm on the dataset. XGBoost is a newly introduced machine learning algorithm; this algorithm provides high accuracy of prediction models, and it is observed that it performs well in short-term prediction. This paper discusses various factors such as total COVID-19 cases, new cases per day, total COVID-19 related deaths, new deaths due to the COVID-19, the total number of recoveries, number of daily recoveries, and swabs through the proposed technique. This paper presents an innovative approach that assists health officials in Pakistan with their decision-making processes.

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Performing technical analysis to predict Japan REITs' movement through ensemble learning

Journal of Property Investment and Finance ◽

10.1108/jpif-01-2020-0007 ◽

2020 ◽

Vol 38 (6) ◽

pp. 551-562

Author(s):

Wei Kang Loo

Keyword(s):

Random Forest ◽

Ensemble Learning ◽

Forecast Accuracy ◽

Gradient Boosting ◽

Learning Models ◽

Content Type ◽

Technical Indicators ◽

Back Testing ◽

Extreme Gradient Boosting ◽

Trading Model

PurposeThe purpose of this study is to evaluate the performance of the ensemble learning models, such as the Random Forest and Extreme Gradient Boosting models, in predicting the direction of the Japan real estate investment trusts (J-REITs) at different return horizons, based on input obtained from various technical indicators.Design/methodology/approachThis study measures the predictability of J-REITs with technical indicators by using different horizons of REITs' return and machine learning models. The ensemble learning models includes Random Forest and Extreme Gradient Boosting models while the return horizons of REITs ranging from 1 to 300 days. The results were further split into individual years to check for the consistency of the performance across time.FindingsThe Extreme Gradient Boosting appears to be the best method in improving forecast accuracy but not the trading return. A wider return horizons platform seemed to deliver a relatively better performance in both forecast accuracy and trading return, when compared to the return horizon of one.Practical implicationsIt is recommended that the Extreme Gradient Boosting and Random Forest model be considered by practitioners for back-testing trading model. In addition, selecting different return horizons so as to achieve a better performance in trading/investment should also be considered.Originality/valueThe predictability of J-REITs using technical indicators was compared among different returns horizons and the models (Extreme Gradient Boosting and Random Forest).

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Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

Remote Sensing ◽

10.3390/rs13051021 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1021

Author(s):

Hu Ding ◽

Jiaming Na ◽

Shangjing Jiang ◽

Jie Zhu ◽

Kai Liu ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Loess Plateau ◽

Water Conservation ◽

Nearest Neighbor ◽

Gradient Boosting ◽

K Nearest Neighbor ◽

The Loess Plateau ◽

Object Based ◽

Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

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Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease

BMC Cardiovascular Disorders ◽

10.1186/s12872-021-01925-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Moojung Kim ◽

Young Jae Kim ◽

Sung Jin Park ◽

Kwang Gi Kim ◽

Pyung Chun Oh ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Influenza Vaccination ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Age Group ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Abstract Background Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination Methods Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups. Results The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%). Conclusions The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

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Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

BMC Anesthesiology ◽

10.1186/s12871-021-01343-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jong Ho Kim ◽

Haewon Kim ◽

Ji Su Jang ◽

Sung Mi Hwang ◽

So Young Lim ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Confidence Interval ◽

Neck Circumference ◽

Difficult Laryngoscopy ◽

Gradient Boosting ◽

Test Set ◽

Equal Distribution ◽

Light Gradient ◽

Extreme Gradient Boosting

Abstract Background Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation. Methods Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set. Results The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]). Conclusions Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

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Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Journal of Healthcare Engineering ◽

10.1155/2021/4123471 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Wenzhi Zhang ◽

Runchuan Li ◽

Shengya Shen ◽

Jinliang Yao ◽

Yan Peng ◽

...

Keyword(s):

Myocardial Infarction ◽

Clinical Decision Making ◽

Human Life ◽

Principal Component ◽

Fusion Rule ◽

Clinical Decision ◽

Gradient Boosting ◽

Discrete Wavelet ◽

Extreme Gradient Boosting ◽

Ventricular Activity

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

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Estimation of Chlorophyll-a Concentrations in Small Water Bodies: Comparison of Fused Gaofen-6 and Sentinel-2 Sensors

Remote Sensing ◽

10.3390/rs14010229 ◽

2022 ◽

Vol 14 (1) ◽

pp. 229

Author(s):

Jiarui Shi ◽

Qian Shen ◽

Yue Yao ◽

Junsheng Li ◽

Fu Chen ◽

...

Keyword(s):

Machine Learning ◽

Chlorophyll A ◽

Water Bodies ◽

Gradient Boosting ◽

Learning Models ◽

Small Water ◽

Extreme Gradient Boosting ◽

Machine Learning Models ◽

Sentinel 2 ◽

Small Water Bodies

Chlorophyll-a concentrations in water bodies are one of the most important environmental evaluation indicators in monitoring the water environment. Small water bodies include headwater streams, springs, ditches, flushes, small lakes, and ponds, which represent important freshwater resources. However, the relatively narrow and fragmented nature of small water bodies makes it difficult to monitor chlorophyll-a via medium-resolution remote sensing. In the present study, we first fused Gaofen-6 (a new Chinese satellite) images to obtain 2 m resolution images with 8 bands, which was approved as a good data source for Chlorophyll-a monitoring in small water bodies as Sentinel-2. Further, we compared five semi-empirical and four machine learning models to estimate chlorophyll-a concentrations via simulated reflectance using fused Gaofen-6 and Sentinel-2 spectral response function. The results showed that the extreme gradient boosting tree model (one of the machine learning models) is the most accurate. The mean relative error (MRE) was 9.03%, and the root-mean-square error (RMSE) was 4.5 mg/m3 for the Sentinel-2 sensor, while for the fused Gaofen-6 image, MRE was 6.73%, and RMSE was 3.26 mg/m3. Thus, both fused Gaofen-6 and Sentinel-2 could estimate the chlorophyll-a concentrations in small water bodies. Since the fused Gaofen-6 exhibited a higher spatial resolution and Sentinel-2 exhibited a higher temporal resolution.

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