scholarly journals Mapping Seasonal High-Resolution PM2.5 Concentrations with Spatiotemporal Bagged-Tree Model across China

2021 ◽  
Vol 10 (10) ◽  
pp. 676
Author(s):  
Junchen He ◽  
Zhili Jin ◽  
Wei Wang ◽  
Yixiao Zhang
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Fine Particulate Matter ◽  
Absolute Error ◽  
Superior Performance ◽  
Tree Model ◽  
Mixed Effect ◽  
Proposed Model ◽  
High Concentrations ◽  
Pm2.5 Concentration

High concentrations of fine particulate matter (PM2.5) are well known to reduce environmental quality, visibility, atmospheric radiation, and damage the human respiratory system. Satellite-based aerosol retrievals are widely used to estimate surface PM2.5 levels because satellite remote sensing can break through the spatial limitations caused by sparse observation stations. In this work, a spatiotemporal weighted bagged-tree remote sensing (STBT) model that simultaneously considers the effects of aerosol optical depth, meteorological parameters, and topographic factors was proposed to map PM2.5 concentrations across China that occurred in 2018. The proposed model shows superior performance with the determination coefficient (R2) of 0.84, mean-absolute error (MAE) of 8.77 μg/m3 and root-mean-squared error (RMSE) of 15.14 μg/m3 when compared with the traditional multiple linear regression (R2 = 0.38, MAE = 18.15 μg/m3, RMSE = 29.06 μg/m3) and linear mixed-effect (R2 = 0.52, MAE = 15.43 μg/m3, RMSE = 25.41 μg/m3) models by the 10-fold cross-validation method. The results collectively demonstrate the superiority of the STBT model to other models for PM2.5 concentration monitoring. Thus, this method may provide important data support for atmospheric environmental monitoring and epidemiological research.

scholarly journals Evaluating Evapotranspiration By Using Data Mining Instead of The Physical Based Model In Remote Sensing

2021 ◽  
Author(s):  
Lamya Neissi ◽  
Mona Golabi ◽  
Mohammad Albaji ◽  
Abd Ali Naseri
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Decision Tree ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Plant Production ◽  
Tree Model ◽  
Water Index ◽  
Input Variables ◽  
Using Data

Abstract Precise calculations for plant water requirements and evapotranspiration is very crucial in determining the volume of water consumption for plant production. In order to estimate evapotranspiration in the extended area, different remote sensing algorithms required many climatological variables. Climatological variable measurements will cover small limited areas which can cause an error in extended areas. By using data mining and remote sensing, the evapotranspiration process can be modeled. In this research, the physical-based SEBAL evapotranspiration algorithm was modeled by M5 decision tree equations in GIS. Input variables of the M5 decision tree consisted of albedo, emissivity, and Normalized Difference Water Index (NDWI) which are represented as absorbed light, transformed light, and plant moisture, respectively. After extracting the best equations in the M5 decision tree model for 8 April 2019, these equations were modeled in GIS by using python scripts for 8 April 2019 and 3 April 2020. The calculated correlation coefficient (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for 8 April 2019 were 0.92, 0.54, and 0.42 and for 3 April 2020 were 0.95, 0.31, and 0.23, respectively. Also, sensitivity and uncertainty analysis were considered for more model evaluation. Those analysis revealed that evapotranspiration is sensitive to albedo more than the two other model inputs and the estimated evapotranspiration obtained by data mining is in acceptable range of certainty.

scholarly journals Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7058
Author(s):  
Heesang Eom ◽  
Jongryun Roh ◽  
Yuli Sun Hariyani ◽  
Suwhan Baek ◽  
Sukho Lee ◽  
...  
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Pressure Sensors ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Proposed Model ◽  
Point Pressure

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.

scholarly journals A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

2020 ◽  
Author(s):  
Sohail Saif ◽  
Priya Das ◽  
Suparna Biswas
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Bees Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Hybrid Forecasting

Abstract In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

scholarly journals Analysis of canopy phenology in man-made forests using near-earth remote sensing

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Peng Guan ◽  
Yili Zheng ◽  
Guannan Lei
Keyword(s):  
Remote Sensing ◽  
Color Index ◽  
Short Term Memory ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Percentage Error ◽  
Earth Remote Sensing ◽  
Squared Error ◽  
Highly Sensitive ◽  
Different Color

Abstract Background Forest canopies are highly sensitive to their growth, health, and climate change. The study aims to obtain time sequence images in mix foresters using a near-earth remote sensing method to track the seasonal variation in the color index and select the optimal color index. Three different regions of interest (RIOs) were defined and six color indexes (GRVI, HUE, GGR, RCC, GCC, and GEI) were calculated to analyze the microenvironment difference. The key phenological phase was identified using the double logistic model and the derivative method, and the phenology forecast of color indexes was performed based on the long short-term memory (LSTM) model. Results The results showed that the same color index in different RIOs and different color indexes in the same RIO present a slight difference in the days of growth and the days corresponding to the peak value, exhibiting different phenological phases; the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the LSTM model was 0.0016, 0.0405, 0.0334, and 12.55%, respectively, indicating that this model has a good forecast effect. Conclusions In different areas of the same forest, differences in the micro-ecological environment in the canopies were prevalent, with their internal growth mechanism being affected by different cultivation ways and the external environment. Besides, the optimal color index also varies with species in phenological response, that is, different color indexes are used for different forests. With the data of color indexes as the training set and forecast set, the feasibility of the LSTM model in phenology forecast is verified.

scholarly journals Soft Computing Techniques for Weather Change Predictions in Delhi

2019 ◽  
Vol 8 (4) ◽  
pp. 793-800
Keyword(s):  
Soft Computing ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Research Work ◽  
Absolute Error ◽  
Support Vector ◽  
Squared Error ◽  
Proposed Model ◽  
Soft Computing Techniques

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

scholarly journals Spatial–Temporal Variations in NO2 and PM2.5 over the Chengdu–Chongqing Economic Zone in China during 2005–2015 Based on Satellite Remote Sensing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3950 ◽  
Author(s):  
Kun Cai ◽  
Qiushuang Zhang ◽  
Shenshen Li ◽  
Yujing Li ◽  
Wei Ge
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Temporal Variations ◽  
Ozone Monitoring Instrument ◽  
Economic Zone ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
High Concentrations ◽  
Remote Sensing Techniques

The Chengdu–Chongqing Economic Zone (CCEZ), which is located in southwestern China, is the fourth largest economic zone in China. The rapid economic development of this area has resulted in many environmental problems, including extremely high concentrations of nitrogen dioxide (NO2) and fine particulate matter (PM2.5). However, current ground observations lack spatial and temporal coverage. In this study, satellite remote sensing techniques were used to analyze the variation in NO2 and PM2.5 from 2005 to 2015 in the CCEZ. The Ozone Monitoring Instrument (OMI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product were used to retrieve tropospheric NO2 vertical columns and estimate ground-level PM2.5 concentrations, respectively. Geographically, high NO2 concentrations were mainly located in the northwest of Chengdu and southeast of Chongqing. However, high PM2.5 concentrations were mainly located in the center areas of the basin. The seasonal average NO2 and PM2.5 concentrations were both highest in winter and lowest in summer. The seasonal average NO2 and PM2.5 were as high as 749.33 × 1013 molecules·cm−2 and 132.39 µg·m−3 in winter 2010, respectively. Over 11 years, the annual average NO2 and PM2.5 values in the CCEZ increased initially and then decreased, with 2011 as the inflection point. In 2007, the concentration of NO2 reached its lowest value since 2005, which was 230.15 × 1013 molecules·cm−2, and in 2015, the concentration of PM2.5 reached its lowest value since 2005, which was 26.43 µg·m−3. Our study demonstrates the potential use of satellite remote sensing to compensate for the lack of ground-observed data when quantitatively analyzing the spatial–temporal variations in regional air quality.

scholarly journals Dual Stage Bayesian Network with Dual-Tree Complex Wavelet Transformation for Image Denoising

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Venkata Lavanya P. ◽  
Venkata Narasimhulu C. ◽  
Satya Prasad K.
Keyword(s):  
Mean Squared Error ◽  
Structural Similarity ◽  
Absolute Error ◽  
Noise Spectrum ◽  
Percentage Error ◽  
Wavelet Coefficients ◽  
Complex Wavelet Transform ◽  
Proposed Model ◽  
Complex Wavelet ◽  
Image Type

Image de-noising always plays a vital role in various engineering bids. Moreover, in image processing technology, image de-noising statistics is persisted as a substantial dispute. Over the past decades, certain de-noising methods have been exposed incredible accomplishments. This paper intends to develop a de-noising algorithm for multimodal and heterogeneous images, while the conventional de-noising algorithms handle a specific image type. The filtered information is reversed to spatial domain to recover the de-noised image. Dual tree Complex Wavelet Transform (DT-CWT) is exploited for image transformation for which the wavelet coefficients are estimated using Bayesian Regularization (BR). To ensure the de-noising performance for heterogeneous images, the statistical and wavelet features are extracted. Subsequently, the image characteristics are combined with noise spectrum to develop BR model, which estimates the wavelet coefficients for effective de-noising. Hence, the proposed de-noising algorithm exploits two stages of BR. The first stage predicts the image type, whereas the second stage estimates appropriate wavelet coefficients to DT-CWT for de-noising. The performance of the proposed model is analysed in terms of Peak Signal to Noise Ratio (PSNR), Second derivative Measure of Enhancement (SDME), Structural Similarity (SSIM), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Pearson Coefficient (PC), and Symmetric Mean Absolute Percentage Error (SMAPE) respectively. The proposed model is compared to the conventional models, and the significance of the developed model is clearly described.

scholarly journals Monitoring Changes in the Transparency of the Largest Reservoir in Eastern China in the Past Decade, 2013–2020

2021 ◽  
Vol 13 (13) ◽  
pp. 2570
Author(s):  
Teng Li ◽  
Bozhong Zhu ◽  
Fei Cao ◽  
Hao Sun ◽  
Xianqiang He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Mean Squared Error ◽  
Eastern China ◽  
Water Environment ◽  
Relative Difference ◽  
Boreal Summer ◽  
Support Vector ◽  
Lake Area ◽  
The Past ◽  
Remote Sensing Reflectance

Based on characteristics analysis about remote sensing reflectance, the Secchi Disk Depth (SDD) in the Qiandao Lake was predicted from the Landsat8/OLI data, and its changing rates on a pixel-by-pixel scale were obtained from satellite remote sensing for the first time. Using 114 matchups data pairs during 2013–2019, the SDD satellite algorithms suitable for the Qiandao Lake were obtained through both the linear regression and machine learning (Support Vector Machine) methods, with remote sensing reflectance (Rrs) at different OLI bands and the ratio of Rrs (Band3) to Rrs (Band2) as model input parameters. Compared with field observations, the mean absolute relative difference and root mean squared error of satellite-derived SDD were within 20% and 1.3 m, respectively. Satellite-derived results revealed that SDD in the Qiandao Lake was high in boreal spring and winter, and reached the lowest in boreal summer, with the annual mean value of about 5 m. Spatially, high SDD was mainly concentrated in the southeast lake area (up to 13 m) close to the dam. The edge and runoff area of the lake were less transparent, with an SDD of less than 4 m. In the past decade (2013–2020), 5.32% of Qiandao Lake witnessed significant (p < 0.05) transparency change: 4.42% raised with a rate of about 0.11 m/year and 0.9% varied with a rate of about −0.09 m/year. Besides, the findings presented here suggested that heavy rainfall would have a continuous impact on the Qiandao Lake SDD. Our research could promote the applications of land observation satellites (such as the Landsat series) in water environment monitoring in inland reservoirs.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

Sign in / Sign up

Export Citation Format

Share Document