PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations.

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Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete

Materials ◽

10.3390/ma13051072 ◽

2020 ◽

Vol 13 (5) ◽

pp. 1072 ◽

Cited By ~ 19

Author(s):

Dong Van Dao ◽

Hai-Bang Ly ◽

Huong-Lan Thi Vu ◽

Tien-Thinh Le ◽

Binh Thai Pham

Keyword(s):

Compressive Strength ◽

Civil Engineering ◽

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Dry Density ◽

Statistical Measures ◽

Foamed Concrete

Development of Foamed Concrete (FC) and incessant increases in fabrication technology have paved the way for many promising civil engineering applications. Nevertheless, the design of FC requires a large number of experiments to determine the appropriate Compressive Strength (CS). Employment of machine learning algorithms to take advantage of the existing experiments database has been attempted, but model performance can still be improved. In this study, the performance of an Artificial Neural Network (ANN) was fully analyzed to predict the 28 days CS of FC. Monte Carlo simulations (MCS) were used to statistically analyze the convergence of the modeled results under the effect of random sampling strategies and the network structures selected. Various statistical measures such as Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used for validation of model performance. The results show that ANN is a highly efficient predictor of the CS of FC, achieving a maximum R2 value of 0.976 on the training part and an R2 of 0.972 on the testing part, using the optimized C-ANN-[3–4–5–1] structure, which compares with previous published studies. In addition, a sensitivity analysis using Partial Dependence Plots (PDP) over 1000 MCS was also performed to interpret the relationship between the input parameters and 28 days CS of FC. Dry density was found as the variable with the highest impact to predict the CS of FC. The results presented could facilitate and enhance the use of C-ANN in other civil engineering-related problems.

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Attributiveness of a mass flow analysis model for integrated water resources assessment under data-scarce conditions

Water Science & Technology ◽

10.2166/wst.2012.497 ◽

2013 ◽

Vol 67 (2) ◽

pp. 261-270 ◽

Cited By ~ 1

Author(s):

B. Helm ◽

T. Terekhanova ◽

J. Tränckner ◽

M. Venohr ◽

P. Krebs

Keyword(s):

Water Quality Management ◽

Flow Analysis ◽

Model Performance ◽

Absolute Error ◽

Coefficient Of Determination ◽

Emission Sources ◽

Analysis Model ◽

River Systems ◽

Systems Model ◽

Nutrient Emissions

Nutrients in river systems originate from multiple emission sources, follow various pathways, and are subject to processes of conversion and fate. One approach to tackle this complexity is to apply balance-oriented models. Although these models operate on a coarse temporal and spatial scale, they are capable of assessing the significance of the different emission sources and their results can be the basis for developing integrated water quality management schemes. In this paper we propose and apply a methodology to evaluate the attributiveness of such model results with regard to the modelled emission pathways. The MONERIS (MOdelling Nutrient Emissions in RIver Systems) model is set up, assuming plausible ranges of emission levels from four principal sources. The sensitivity of model performance is computed and related to the contribution from the pathways. The approach is applied for a case study in the upper Western Bug catchment (Ukraine). Coefficient of determination (R²) is found insensitive against the model assumptions, at levels around 0.65 for nitrogen and 0.55 for phosphorous emissions. Relative mean absolute error is minimized around 0.2 for both nutrients, but with equifinal combinations of the varied emission pathways. Model performance is constrained by the ranges of the emission assumptions to a limited extent only.

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Simple and Low-Cost Procedure for Monthly and Yearly Streamflow Forecasts during the Current Hydrological Year

Water ◽

10.3390/w10081038 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1038 ◽

Cited By ~ 5

Author(s):

Fernando Delgado-Ramos ◽

Carmen Hervás-Gámez

Keyword(s):

Low Cost ◽

Model Performance ◽

Absolute Error ◽

Integrated Water Resources Management ◽

Coefficient Of Determination ◽

Water Volume ◽

Cumulative Probability ◽

Water Users ◽

The Mean ◽

The Right

Accurately forecasting streamflow values is essential to achieve an efficient, integrated water resources management strategy and to provide consistent support to water decision-makers. We present a simple, low-cost, and robust approach for forecasting monthly and yearly streamflows during the current hydrological year, which is applicable to headwater catchments. The procedure innovatively combines the use of well-known regression analysis techniques, the two-parameter Gamma continuous cumulative probability distribution function and the Monte Carlo method. Several model performance statistics metrics (including the Coefficient of Determination R2; the Root-Mean-Square Error RMSE; the Mean Absolute Error MAE; the Index of Agreement IOA; the Mean Absolute Percent Error MAPE; the Coefficient of Nash-Sutcliffe Efficiency NSE; and the Inclusion Coefficient IC) were used and the results showed good levels of accuracy (improving as the number of observed months increases). The model forecast outputs are the mean monthly and yearly streamflows along with the 10th and 90th percentiles. The methodology has been successfully applied to two headwater reservoirs within the Guadalquivir River Basin in southern Spain, achieving an accuracy of 92% and 80% in March 2017. These risk-based predictions are of great value, especially before the intensive irrigation campaign starts in the middle of the hydrological year, when Water Authorities have to ensure that the right decision is made on how to best allocate the available water volume between the different water users and environmental needs.

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Evaluation of satellite precipitation products using HEC-HMS model

Modeling Earth Systems and Environment ◽

10.1007/s40808-020-00792-z ◽

2020 ◽

Vol 6 (4) ◽

pp. 2015-2032

Author(s):

Alemshet Belayneh ◽

Gashaw Sintayehu ◽

Kibrit Gedam ◽

Tirunesh Muluken

Keyword(s):

Hydrological Model ◽

Model Performance ◽

Coefficient Of Determination ◽

Percentage Error ◽

Objective Functions ◽

Runoff Generation ◽

Spatial And Temporal Patterns ◽

Satellite Precipitation ◽

Wide Range ◽

Satellite Rainfall

Abstract Accurate measurement of precipitation is vital to investigate the spatial and temporal patterns of precipitation at various scales for rainfall-runoff modeling. However, accurate and consistent precipitation measurement is relatively sparse in many developing countries like Ethiopia. Nevertheless, satellite precipitation products may serve as important inputs for modeling in an area with scarce field data for a wide range of hydrological applications. In this study we evaluate the high-resolution satellite rainfall products for hydrological simulation, the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA_3B42v7) satellite rainfall products for stream flow simulation at daily temporal and 0.25° × 0.25° spatial resolution. The study area is located in Dabus watershed, Abbay basin, Ethiopia. We applied a nonlinear power law to remove the systematic error of satellite precipitation estimates for input into HEC-HMS hydrological model for runoff generation. The performance of the satellite rainfall and hydrological model was evaluated using Nash–Sutcliffe efficiency (ENS), coefficient of determination (R2), relative volume error (RVE), and percentage error of peak flow objective functions. The result of HEC-HMS model performance revealed R2 of 0.78, ENS of 0.69 for CHIRPS_2 and R2 of 0.79, ENS of 0.76 for TMPA_3B42v7 satellite rainfall products during calibration periods. Our result indicated that the HEC-HMS model well predicated catchment runoff for both satellite precipitation products. The study shows that the model performance was significantly improved when bias-corrected satellite rainfall input replaced than the original uncorrected satellite products. Overall, our study showed that gauge-based simulation outperformed than satellite in terms of all objective functions over the study area.

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Calibration and validation of the AquaCrop model for the soybean crop grown under different levels of irrigation in the Motopiba region, Brazil

Ciência Rural ◽

10.1590/0103-8478cr20161118 ◽

2017 ◽

Vol 48 (1) ◽

Cited By ~ 2

Author(s):

Vicente de Paulo Rodrigues da Silva ◽

Roberta Araújo e Silva ◽

Girlene Figueiredo Maciel ◽

Célia Campos Braga ◽

José Luiz Cabral da Silva Júnior ◽

...

Keyword(s):

Model Performance ◽

Absolute Error ◽

Efficiency Index ◽

Water Levels ◽

Coefficient Of Determination ◽

Yield Response ◽

Statistical Parameters ◽

Yield Data ◽

Climate Conditions ◽

Aquacrop Model

ABSTRACT: The water-driven AquaCrop model to simulate yield response has been calibrated and validated for soybean cultivated under different water levels irrigation in Matopiba region, Brazil. The crop was submitted to seven irrigation treatments during the dry season and a dry treatment in the rainy season. The model was parameterized and calibrated by using soybean yield data collected at field level. Model performance was evaluated by using the following statistical parameters: prediction error (Pe), Nash-Sutcliffe efficiency index (E), coefficient of determination (R2), mean absolute error (MAE), root mean square error normalized (RMSEN) and Willmott’s index (d). The statistical analyses of the AquaCrop model calibrated for the Matopiba region disclosed error acceptable for yield prediction of soybean grown under tropical climate conditions. Results also indicated that the C2 soybean cultivar is more resistant to water stress than the C1 soybean grown in the Matopiba region, Brazil. In the treatments when the crop was well supplied with water, at least in one phase, the yield was greater than those with drought stress at last in one phase.

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Estimation of spatial soil depth and its application for shallow landslides and debris flow assessment: case study at Mt. Jiri, S. Korea

10.5194/egusphere-egu2020-4354 ◽

2020 ◽

Author(s):

Minseok Kim ◽

Jisu Kim ◽

Hyun-Joo Oh ◽

Jinkwan Kim

Keyword(s):

Debris Flow ◽

Depth Distribution ◽

Soil Depth ◽

Critical Role ◽

Model Performance ◽

Absolute Error ◽

Shallow Landslide ◽

Test Method ◽

Coefficient Of Determination ◽

Gps Rtk

<p>Soil depth plays critical role in prediction studies reflecting hydrologic mechanism such as shallow landslide and debris flow although there are many parameters. Thus, many researchers are studying the estimation of soil depth distribution using various methods such as a kriging and artificial neural networks (ANNs) since it is not easy to get a detailed soil depth distribution in field. The aims of this study are 1) to estimate detailed spatial distribution of soil depth (various methods such as ANNs, Kriging, s- and z-model, and c-model) and, 2) to apply them for assessment of shallow landslide instability and debris flow. To do this, soil depth of 760 points using knocking pole test method and elevation datasets using GPS-RTK were collected at Mt Jiri, South Korea. To analysis the accuracy of each estimated soil depth distribution, the lowest root mean square error (RMSE), mean absolute error (MAE) and the highest values of the coefficient of determination (R<sup>2</sup>) were applied and, ANNs method showed reasonable result better than did others. In the effect of shallow landslide instability and debris flow assessment with the each soil depth distribution results, soil depth distribution using an ANNs method also showed high simulated model performance by modified success ratio (MSR). These results indicated that ANNs can be one of the methods to estimate the soil depth distribution for improvement of accuracy of shallow landslide instability mapping and debris flow assessment.</p>

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Comparison between some meteorological elements measured by ECMWF and Earth stations in different regions in Iraq

EUREKA Physics and Engineering ◽

10.21303/2461-4262.2021.001941 ◽

2021 ◽

pp. 41-49

Author(s):

Rajaa A. Basheer ◽

Dr. Waleed I. AL-Rijabo

Keyword(s):

Linear Models ◽

Weather Forecasting ◽

Statistical Tests ◽

Absolute Error ◽

Coefficient Of Determination ◽

Power Models ◽

Political Economic ◽

Meteorological Elements ◽

Quadratic Models ◽

Very High

The aim of this paper is to validate the data of three meteorological elements Air Temperature (Ta) , Relative Humidity (RH), Wind Speed (WS) from the European Center For-Medium Range Weather Forecasting (ECMWF) , against ground stations data using several Models at six stations well distributed in Iraq (Mosul, Kirkuk, Baghdad, Kut, Nasiriya, and Basra). Due to the difficulties which experienced by the ground climate stations in Iraq from a shortage of devices and equipment for measuring the various climatic elements, which led to a huge shortage of data throughout time for political, economic and natural disasters. It is found that researchers can adopt the data of satellite stations to monitor the climate because let’s found that there is a highly significance Correlations between the data of these stations and the data of the ground stations for climate monitoring Five Mathematical Models were used for that [Linear Models, Quadratic Models, Exponential Models, Logarithmic Models, and Power Models]. The performance of these models were evaluated by comparing the calculated (Ta, RH, WS) from earth stations. Those mathematical correlations help to be able to calculate the ground data in state of there is no ground climate stations data. Several statistical tests Correlation Coefficient (R), Coefficient of Determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) were used to control the validation and goodness of these Models. The R2 obtained from these Models were very high in all stations. This means that, there is a highly significance Correlations between (Ta, RH, WS) estimated and [Ta, RH, WS] measured in all station

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An interpretable prediction method for university student academic crisis warning

Complex & Intelligent Systems ◽

10.1007/s40747-021-00383-0 ◽

2021 ◽

Author(s):

Zhai Mingyu ◽

Wang Sutong ◽

Wang Yanzhang ◽

Wang Dujuan

Keyword(s):

Academic Achievement ◽

Visual Analysis ◽

Prediction Method ◽

Absolute Error ◽

University Student ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Achievement Prediction ◽

Academic Achievement Prediction ◽

Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

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A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽

10.3390/atmos12050592 ◽

2021 ◽

Vol 12 (5) ◽

pp. 592

Author(s):

Mehdi Aalijahan ◽

Azra Khosravichenar

Keyword(s):

Regional Planning ◽

Interpolation Method ◽

Absolute Error ◽

Rain Gauge ◽

Annual Precipitation ◽

Coefficient Of Determination ◽

Distance Weighting ◽

Average Annual Precipitation ◽

Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

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Diabetes classification application with efficient missing and outliers data handling algorithms

Complex & Intelligent Systems ◽

10.1007/s40747-021-00349-2 ◽

2021 ◽

Author(s):

Hanaa Torkey ◽

Elhossiny Ibrahim ◽

EZZ El-Din Hemdan ◽

Ayman El-Sayed ◽

Marwa A. Shouman

Keyword(s):

Deep Learning ◽

Healthcare System ◽

Wearable Sensors ◽

Model Performance ◽

Analysis Model ◽

Human Errors ◽

Web Based ◽

Machine Learning Model ◽

Complete Dataset ◽

Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

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