Search

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.

Almost every organization around the globe is working with uncertainty due to inevitable changes and growth in every sphere of life. These changes affect directly or indirectly the stock market prices which makes forecasting a challenging task. So, the need for reliable, cost-effective, and accurate forecasting models significantly arises to reduce risk and uncertainty in stock market investment. Different time series models have been proposed by data scientists and researchers for accurate prediction of the future with the least errors. Econometric autoregressive time series models such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models have established forecasting models capable of generating accurate forecasts. Wavelet methods, being capable of handling nonlinear data, combined with autoregressive models generate more accurate forecasts. In this present study, soft computing models of discreet wavelet transformation and wavelet denoising combined with autoregressive models are developed to forecast the weekly and daily closing prices of the BSE100 S&P Sensex index. Statistical error analysis of the forecasting outcomes of coupled models has been made to evaluate the performance of the prediction of these models. The prediction results reveal that soft computing methods coupled with autoregressive models (wavelet-ARIMA and wavelet denoise-ARIMA) generate considerably accurate forecasts as compared to baseline models (simple regression, ARMA and ARIMA models) and coupled models (wavelet-ARMA and wavelet denoise-ARMA models).

Accurate estimation of streamflow has a vital importance in water resources engineering, management and planning. In the present study, the abilities of group method of data handling-neural networks (GMDH-NN), dynamic evolving neural-fuzzy inference system (DENFIS) and multivariate adaptive regression spline (MARS) methods are investigated for monthly streamflow prediction. Precipitation, temperature and streamflows from Kalam and Chakdara stations at Swat River basin (mountainous basin), Pakistan, are used as inputs to the applied models in the form of different input scenarios, and models’ performances are evaluated on the basis of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and combined accuracy (CA) indexes. Test results of the Kalam Station show that the DENFIS model provides more accurate prediction results in comparison of GMDH-NN and MARS models with the lowest RMSE (18.9 m3/s), MAE (13.1 m3/s), CA (10.6 m3/s) and the highest NSE (0.941). For the Chakdara Station, the MARS outperforms the GMDH-NN and DENFIS models with the lowest RMSE (47.5 m3/s), MAE (31.6 m3/s), CA (26.1 m3/s) and the highest NSE (0.905). Periodicity (month number of the year) effect on models’ accuracies in predicting monthly streamflow is also examined. Obtained results demonstrate that the periodicity improves the models’ accuracies in general but not necessarily in every case. In addition, the results also show that the monthly streamflow could be successfully predicted using only precipitation and temperature variables as inputs.

In recent times, black carbon (BC) has attracted the interest of the researchers due to its adverse effect on human health, climate, rainfall, and global heating causing the melting of ice in the poles due to carbon deposition on it. Coal industry is the backbone of Indian economy and India being the world’s third largest producer of coal. Various mining activities are leading to spontaneous emission of black carbon in the atmosphere, especially in the IGP (Indo-Gangetic plain) region. Long-term studies related to black carbon emission in the coal regions of India are very rare. In the present study, a long-term datum of 38 years (1980–2018) for the amount of black carbon emission among the three important coal mines of India, namely Bokaro, Jharia, and Raniganj, is studied using correlation analysis, and time-series analysis along with a few other mathematical parameters. The comparison and forecast obtained using this study will be beneficial in the upcoming years, so as to gather the interest of the government, NGOs, and researchers in this area, so that new policies and preventive measures could be taken to curtail the black carbon concentration from the coal mines.

Water is the basic need for life to exist on this planet earth; rivers play a vital role to fulfill this need for the supply of freshwater. Due to spontaneous growth of industrialization and urbanization near the important rivers, most of them have been polluted to a severe extent and the future of these rivers and living organism depending on the water from them is on threat. Thus, various prediction models have been developed by researchers to build an accurate forecasting model to access the future quality of rivers with least forecasting error. Time series models have been developed to form such prediction, but most of them were unsuccessful in handling nonlinear problems. Artificial neural network (ANN) and adaptive neuro-fuzzy interface system have proven to be an efficient tool to handle such nonlinear situations. In this study, in addition to the above methods, wavelet transformation has been used to develop a forecasting model to generate forecasts close to actual values. The biochemical oxygen demand of river Yamuna at sample site of Nizamuddin (Delhi) is predicted using the past monthly averaged data. Statistical analysis has been used to study the nature of the wavelet domain constitutive series considered. The results obtained indicate that the neuro-fuzzy-wavelet-coupled model leads to considerably superior outcomes compared to neuro-fuzzy, ANN and regression models.

Air pollution is a major issue in all around world, it directly impact on human health, which affecting the lungs and respiratory system. This deposited on soil, plants and in the water, further contributing to human exposure and It mixes in the blood and pumped all-around the body. The most important air pollutants found over Delhi were sulfur dioxide (SO2), nitrogen dioxides (NO2) and suspended particulate matter (SPM). Statistical and wavelet analysis of these air pollutants at three different sample sites two residential namely Janakpuri, Nizamuddin, and one industrial namely Shahazada Bagh over Delhi for the more than 20 year period from 1987 to 2010 in India have been studied. The results shown that the mean concentration of SO2 decreased for both residential (Janakpuri, Nizamuddin) as well as industrial (Shahzada Bagh) area, whereas NO2 increased but it is under the prescribed limits of National Ambient Air Quality Standards (NAAQS). Janakpuri and Nizamuddin represent almost equal but lower mean values of SO2 concentration than Shahzada Bagh. SO2, NO2 and SPM at all sites depicts symmetrical and platykurtic behaviour except Shahzada bagh, for that it follows leptokurtic. Discrete wavelet analysis of air pollutants using Daubechies wavelet (level 5) have been calculated for the study. It is also observed that the values at five different levels of signal data for all air pollutants varies between −225 and +225.

This study investigates the applicability of three different soft computing methods, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS), and M5 Model Tree (M5-Tree), in forecasting SO2 concentration. These models were applied to monthly data obtained from Janakpuri, Nizamuddin, and Shahzadabad, located in Delhi, India. The models were compared with each other using the cross validation method with respect to root mean square error, mean absolute error, and correlation coefficient. According to the comparison, LSSVR provided better accuracy than the other models, while the MARS model was found to be the second best model in forecasting monthly SO2 concentration. Results indicated that the applied models gave better forecasting accuracy in Janakpuri station than the other stations. The results were also compared with previous studies and satisfactory results were obtained from three methods in modeling SO2 concentrations.

Modeling pan evaporation (Epan) estimation is a vital issue in water resources management because it directly affects water reservoir and water supply systems. In the developing countries (e.g., India), Epan data are generally limited, and in such a circumstance, theoretical estimates from available climatic data could be beneficial. The study investigates the capability of three adaptive neuro-fuzzy methods, adaptive neuro-fuzzy inference system (ANFIS)–embedded grid partition (GP), subtractive clustering (SC), and fuzzy c-means clustering (FCM), in estimation of monthly pan evaporation using climatic inputs of minimum and maximum air temperatures, wind speed, sunshine hours, and relative humidity obtained from two stations, Uttarakhand, India. Cross validation method is applied by dividing data into three equal parts, and methods are tested using each part. Methods are evaluated by applying various combinations of inputs and using root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2) criteria. The ANFIS-FCM is found to be superior to the ANFIS-GP and ANFIS-SC methods in Epan modeling. Cluster-based proposed neuro-fuzzy method increases performance of the best ANFIS-GP and ANFIS-SC models with respect to RMSE by about 9–14% for the both stations. The three ANFIS methods are also compared with each other and Stephen Stewart (SS) method by dividing data into three stages, training, validation, and test. The results indicate the superior accuracy of the ANFIS methods to SS for the same input variables. The ANFIS-FCM generally produces better Epan estimates than the other two ANFIS methods.

In this paper, new prediction model introduced by coupling of neural networks model, fuzzy model and wavelet model for the water resources management. Artificial neural network (ANN), fuzzy, wavelet and adaptive neuro-fuzzy inference system (ANFIS) are found to be a sturdy tool to model many non-linear hydrological processes. Wavelet transformation will improve the ability of a prediction model by capturing valuable information on different resolution levels. The target of this research is to compare our model with other famous data-driven models for monthly forecasting of water quality parameter chemical oxygen demand (COD) level monitored at Nizamuddin station, New Delhi, India of river Yamuna based on the past history. The data has been decomposed into wavelet domain constitutive sub series using Daubechies wavelet at level 8 (Db8). Statistical behavior of wavelet domain constitutive series has been studied. The foretelling performance of the wavelet coupled model has been compared with classical neuro fuzzy, artificial neural network and regression models. The result shows that the wavelet coupled model produces considerably higher leads to comparison to neuro fuzzy, neural network, regression models.

River water is a major resource of drinking water on earth. Management of river water is highly needed for surviving. Yamuna is the main river of India, and monthly variation of water quality of river Yamuna, using statistical methods have been compared at different sites for each water parameters. Regression, correlation coefficient, autoregressive integrated moving average (ARIMA), box-Jenkins, residual autocorrelation function (ACF), residual partial autocorrelation function (PACF), lag, fractal, Hurst exponent, and predictability index have been estimated to analyze trend and prediction of water quality. Predictive model is useful at 95% confidence limits and all water parameters reveal platykurtic curve. Brownian motion (true random walk) behavior exists at different sites for BOD, AMM, and total Kjeldahl nitrogen (TKN). Quality of Yamuna River water at Hathnikund is good, declines at Nizamuddin, Mazawali, Agra D/S, and regains good quality again at Juhikha. For all sites, almost all parameters except potential of hydrogen (pH), water temperature (WT) crosses the prescribed limits of World Health Organization (WHO)/United States Environmental Protection Agency (EPA).

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung–Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

Natural and anthropogenic aerosols over coal mines regions in India play a significant role in influencing the regional radiation budget, causing climate implications to the overall hydrological cycle of India. In the reference of regional climate change and air quality, we discuss aerosol optical depth (AOD) variability and long-term trends (from Mar 2000–Dec 2012) over eastern, southeast, and south coalfield regions in India. The present work analyses the variations and trends in aerosol loading using Terra-MODIS (Moderate-Resolution Imaging Spectroradiometer) AOD550 data in the period 2000–2012. Overall, an increasing trend in AOD550 has been observed over all regions namely Raniganj (7.31 %) in eastern and Korba (5.0 %) in southeast, and Godavari Valley (32 %) in the south coalfield region in India. This increasing trend predominantly owes to a constant increase in the seasonal/monthly averaged AOD during the winter (Dec–Feb) and post-monsoon (Oct–Nov) seasons dominated by anthropogenic emissions. In contrast, a decreasing trend is observed during pre-monsoon (Mar–May) season over eastern coalfield region (−13 %), while at south coalfield region (44 %) and southeastern coalfield region (0.8 %), increasing trends are observed. Similarly, increasing trends is observed over all regions in monsoon (Jun–Sep) months. Furthermore, the values of Hurst exponent, fractal dimension, and predictability index for AODs are 0.5, 1.5, and 0, respectively suggesting that the AODs in all sites follow the Brownian time series motion (true random walk). High AOD values (0.59 ± 0.21) are observed over eastern region Raniganj.