Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling

Climatology and model prediction of aerosol optical properties over the Indo-Gangetic Basin in north India

The current research focuses on the use of different simulation techniques in the future prediction of the crucial aerosol optical properties over the highly polluted Indo-Gangetic Basin in the northern part of India. The time series model was used to make an accurate forecast of aerosol optical depth (AOD) and angstrom exponent (AE), and the statistical variability of both cases was compared in order to evaluate the effectiveness of the model (training and validation). For this, different models were used to simulate the monthly average AOD and AE over Jaipur, Kanpur and Ballia during the period from 2003 to 2018. Further, the study was aimed to construct a comparative model that will be used for time series statistical analysis of MODIS-derived AOD₅₅₀ and AE_412-470. This will provide a more comprehensive information about the levels of AOD and AE that will exist in the future. To test the validity and applicability of the developed models, root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percent error (MAPE), fractional bias (FB), and Pearson coefficient (r) were used to show adequate accuracy in model performance. From the observation, the monthly mean values of AOD and AE were found to be nearly similar at Kanpur and Ballia (0.62 and 1.26) and different at Jaipur (0.25 and 1.14). Jaipur indicates that during the pre-monsoon season, the AOD mean value was found to be highest (0.32 ± 0.15), while Kanpur and Ballia display higher AOD mean values during the winter season (0.72 ± 0.26 and 0.83 ± 0.32, respectively). Among the different methods, the autoregressive integrated moving average (ARIMA) model was found to be the best-suited model for AOD prediction at Ballia based on fitted error (RMSE (0.22), MAE (0.15), MAPE (24.55), FB (0.05)) and Pearson coefficient r (0.83). However, for AE, best prediction was found at Kanpur based on RMSE (0.24), MAE (0.21), MAPE (22.54), FB (-0.09) and Pearson coefficient r (0.82).

Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.

Prediction of COVID-19 pervasiveness in six major affected states of India and two-stage variation with temperature

Coronavirus disease knocked in Wuhan city of China in December 2019 which spread quickly across the world and infected millions of people within a short span of time. COVID-19 is a fast-spreading contagious disease which is caused by SARS-CoV-2 (severe acute respiratory syndrome-coronavirus-2). Accurate time series forecasting modeling is the need of the hour to monitor and control the universality of COVID-19 effectively, which will help to take preventive measures to break the ongoing chain of infection. India is the second highly populated country in the world and in summer the temperature rises up to 50°, nowadays in many states have more than 40° temperatures. The present study deals with the development of the autoregressive integrated moving average (ARIMA) model to predict the trend of the number of COVID-19 infected people in most affected states of India and the effect of a rise in temperature on COVID-19 cases. Cumulative data of COVID-19 confirmed cases are taken for study which consists of 77 sample points ranging from 1st March 2020 to 16th May 2020 from six states of India namely Delhi (Capital of India), Madya Pradesh, Maharashtra, Punjab, Rajasthan, and Uttar Pradesh. The developed ARIMA model is further used to make 1-month ahead out of sample predictions for COVID-19. The performance of ARIMA models is estimated by comparing measures of errors for these six states which will help in understanding future trends of COVID-19 outbreak. Temperature rise shows slightly negatively correlated with the rise in daily cases. This study is noble to analyse the variation of COVID-19 cases with respect to temperature and make aware of the state governments and take precautionary measures to flatten the growth curve of confirmed cases of COVID-19 infections in other states of India, nearby countries as well.

Study of ARIMA and least square support vector machine (LS-SVM) models for the prediction of SARS-CoV-2 confirmed cases in the most affected countries

Discussions about the recently identified deadly coronavirus disease (COVID-19) which originated in Wuhan, China in December 2019 are common around the globe now. This is an infectious and even life-threatening disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has rapidly spread to other countries from its originating place infecting millions of people globally. To understand future phenomena, strong mathematical models are required with the least prediction errors. In the present study, autoregressive integrated moving average (ARIMA) and least square support vector machine (LS-SVM) models are applied to the data consisting of daily confirmed cases of SARS-CoV-2 in the most affected five countries of the world for modeling and predicting one-month confirmed cases of this disease. To validate these models, the prediction results were tested by comparing it with testing data. The results revealed better accuracy of the LS-SVM model over the ARIMA model and also suggested a rapid rise of SARS-CoV-2 confirmed cases in all the countries under study. This analysis would help governments to take necessary actions in advance associated with the preparation of isolation wards, availability of medicines and medical staff, a decision on lockdown, training of volunteers, and economic plans.

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Earlier detected in Wuhan, Hubei province, in China in December 2019, the deadly virus engulfed China and some neighboring countries, which claimed thousands of lives in February 2020. The proposed hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths due to COVID-19, which splits the input data into component series and then applying an appropriate econometric model to each of the component series for making predictions of death cases in future. ARIMA models are well known econometric forecasting models capable of generating accurate forecasts when applied on wavelet decomposed time series. The input dataset consists of daily death cases from most affected five countries by COVID-19, which is given to the hybrid model for validation and to make one month ahead prediction of death cases. These predictions are compared with that obtained from an ARIMA model to estimate the performance of prediction. The predictions indicate a sharp rise in death cases despite various precautionary measures taken by governments of these countries.

A comparative time series analysis and modeling of aerosols in the contiguous United States and China

Long-term trend analysis and modeling of aerosol distribution is of paramount importance to study radiative forcing, climate change, and human health. Previous studies on spatiotemporal trend analysis have not fully considered the impact of spatial and temporal gaps of satellite-retrieved aerosol optical depth (AOD) on precise aerosol characterization. In addition, very few studies analyzed inter-country aerosol variations, trends, driving forces, and predictions at the regional level, which is important to draw lessons from the experiences of one another. This study is focused on comparative time series analyses and modeling of aerosols over the contiguous United States (U.S.) and China during 2003-2015 using MODIS Collection 6 retrievals. An econometric model, namely autoregressive integrated moving average (ARIMA), is employed to reproduce and predict AOD variability over U.S. and China. Results show that high AOD values are observed in the eastern part of U.S. and China. Temporal variations indicate that AODs reach their peak values in summer for both countries. A sustained negative AOD trend is present throughout the U.S. while a distinct spatial variation of AOD trend is exhibited in China. The large differences in variations and trends are closely linked to the energy strategies, economic and urban development, and lifestyle activities of these two countries. Time series modeling reveals that reasonably good performances are found in most parts of these two countries. In particular, the model replicates AOD time series that has clear seasonal variations with much more accuracy. The results suggest that areas most suitable for applying the model for prediction are those with high AOD quality, high completeness of AOD data, and low-AOD values. Overall, the satisfactory predicted results indicate the applicability and feasibility of the ARIMA modeling technique for accurately extracting AOD profiles, predicting future AOD values as well as extrapolating missed AOD values at the regional scale. The retrieved and predicted AOD values may serve as reliable estimates for air quality and epidemiological studies.

An improved gray prediction model for China's beef consumption forecasting

To balance the supply and demand in China's beef market, beef consumption must be scientifically and effectively forecasted. Beef consumption is affected by many factors and is characterized by gray uncertainty. Therefore, gray theory can be used to forecast the beef consumption, In this paper, the structural defects and unreasonable parameter design of the traditional gray model are analyzed. Then, a new gray model termed, EGM(1,1,r), is built, and the modeling conditions and error checking methods of EGM(1,1,r) are studied. Then, EGM(1,1,r) is used to simulate and forecast China’s beef consumption. The results show that both the simulation and prediction precisions of the new model are better than those of other gray models. Finally, the new model is used to forecast China’s beef consumption for the period from 2019–2025. The findings will serve as an important reference for the Chinese government in formulating policies to ensure the balance between the supply and demand for Chinese beef.

Development of a Regression Model for Estimating Daily Radiative Forcing Due to Atmospheric Aerosols from Moderate Resolution Imaging Spectrometers (MODIS) Data in the Indo Gangetic Plain (IGP)

The assessment of direct radiative forcing due to atmospheric aerosols (ADRF) in the Indo Gangetic Plain (IGP), which is a food basket of south Asia, is important for measuring the effect of atmospheric aerosols on the terrestrial ecosystem and for assessing the effect of aerosols on crop production in the region. Existing comprehensive analytical models to estimate ADRF require a large number of input parameters and high processing time. In this context, here, we develop a simple model to estimate daily ADRF at any location on the surface of the IGP through multiple regressions of AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) and atmospheric water vapour using data from 2002 to 2015 at 10 stations in the IGP. The goodness of fit of the model is indicated by an adjusted R2 value of 0.834. The Jackknife method of deleting one group (station data) was employed to cross validate and study the stability of the regression model. It was found to be robust with an adjusted R2 fluctuating between 0.813 and 0.842. In order to use the year-round ADRF model for locations beyond the AERONET stations in the IGP, AOD, and atmospheric water vapour products from MODIS Aqua and Terra were compared against AERONET station data and they were found to be similar. Using MODIS Aqua and Terra products as input, the year-round ADRF regression was evaluated at the IGP AERONET stations and found to perform well with Pearson correlation coefficients of 0.66 and 0.65, respectively. Using ADRF regression model with MODIS inputs allows for the estimation of ADRF across the IGP for assessing the aerosol impact on ecosystem and crop production.