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).

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.

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is proposed for the accurate estimation of the DO. Daily pH, temperature (T), electrical conductivity (EC), river discharge (Q) and DO data from Fountain Creek near Fountain, the United States, were used for the model development. Various combinations of pH, T, EC, and Q were used as inputs to the models to estimate the DO. The outcomes of the proposed SVM–FFAPSO model were compared with the SVM–PSO, SVM–FFA, and standalone SVM with respect to the root mean square errors (RMSE), the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2), and graphical methods, such as scatterplots, and Taylor and violin charts. The SVM–FFAPSO showed a superior performance to the other methods in the estimation of the DO. The best model of each method was also assessed in multistep-ahead (from 1- to 7-day ahead) DO, and the superiority of the proposed method was observed from the comparison. The general outcomes recommend the use of SVM–FFAPSO in DO modeling, and this method can be useful for decision-makers in urban water planning and management.

Long-term aerosol climatology is derived using Terra MODIS (Collection 6) enhanced Deep Blue (DB) AOD retrieval algorithm to investigate decadal trend (2006–2015) in columnar aerosol loading, future scenarios and potential source fields over the Indo-Gangetic Plain (IGP), South Asia. Satellite based aerosol climatology was analyzed in two contexts: for the entire IGP considering area weighted mean AOD and for nine individual stations located at upper (Karachi, Multan, Lahore), central (Delhi, Kanpur, Varanasi, Patna) and lower IGP (Kolkata, Dhaka). A comparatively high aerosol loading (AOD: 0.50 ± 0.25) was evident over IGP with a statistically insignificant increasing trend of 0.002 year−1. Analysis highlights the existing spatial and temporal gradients in aerosol loading with stations over central IGP like Varanasi (decadal mean AOD±SD; 0.67 ± 0.28) and Patna (0.65 ± 0.30) exhibit the highest AOD, followed by stations over lower IGP (Kolkata: 0.58 ± 0.21; Dhaka: 0.60 ± 0.24), with a statistically significant increasing trend (0.0174–0.0206 year−1). In contrast, stations over upper IGP reveal a comparatively low aerosol loading, having an insignificant increasing trend. Variation in AOD across IGP is found to be mainly influenced by seasonality and topography. A distinct “aerosol pool” region over eastern part of Ganges plain is identified, where meteorology, topography, and aerosol sources favor the persistence of airborne particulates. A strong seasonality in aerosol loading and types is also witnessed, with high AOD and dominance of fine particulates over central to lower IGP, especially during post-monsoon and winter. The time series analyses by autoregressive integrated moving average (ARIMA) indicate contrasting patterns in randomness of AOD over individual stations with better performance especially over central IGP. Concentration weighted trajectory analyses identify the crucial contributions of western dry regions and partial contributions from central Highlands and north-eastern India, in regulating AOD over stations across IGP. Although our analyses provide some attributes to the observed changes in aerosol loading, we conclude that the spatial and temporal pattern of aerosol properties is highly complex and dynamic over IGP, and require further investigation in order to reduce uncertainty in aerosol-climate model.

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.

The paper analyzes the long-term noise monitoring data using autoregressive integrated moving averages (ARIMA) modeling technique and artificial neural networks (ANNs) methodology. Box-Jenkins ARIMA and ANN approach have been utilized to simulate daily equivalent LDay (06-22h) and LNight (22-06h) in A and C weightings for a period of 1 year. The forecasting performance is ascertained using the statistical tests. The work draws a comparison of time-series ARIMA and ANN approach for ascertaining their suitability for traffic noise modeling and forecasting. It is observed that the artificial neural network (ANN) models outperform the ARIMA models so developed. The pattern of ARIMAf orecasting models is directional and as such the time-series predictive model utilizing ANN approach has demonstrated superior performance over the ARIMA model.

Summary This study investigates the accuracy of least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5Tree) in modeling river water pollution. Various combinations of water quality parameters, Free Ammonia (AMM), Total Kjeldahl Nitrogen (TKN), Water Temperature (WT), Total Coliform (TC), Fecal Coliform (FC) and Potential of Hydrogen (pH) monitored at Nizamuddin, Delhi Yamuna River in India were used as inputs to the applied models. Results indicated that the LSSVM and MARS models had almost same accuracy and they performed better than the M5Tree model in modeling monthly chemical oxygen demand (COD). The average root mean square error (RMSE) of the LSSVM and M5Tree models was decreased by 1.47% and 19.1% using MARS model, respectively. Adding TC input to the models did not increase their accuracy in modeling COD while adding FC and pH inputs to the models generally decreased the accuracy. The overall results indicated that the MARS and LSSVM models could be successfully used in estimating monthly river water pollution level by using AMM, TKN and WT parameters as inputs.

Long-term observations and modeling of aerosol loading over the Indo-Gangetic plains (IGP), the Indian desert region and Himalayan slopes are analyzed in the present study. The Box–Jenkins popular ARIMA (AutoRegressive Integrated Moving Average) model was applied to simulate the monthly-mean Terra MODIS (MODerate Resolution Imaging Spectroradiometer) Aerosol Optical Depth (AOD 550 nm) over eight sites in the region covering a period of about 13 years (March 2000–May 2012). The autocorrelation structure has been analyzed indicating a deterministic pattern in the time series that it regains its structure every 24 month period. The ARIMA models namely ARIMA(2,0,12), ARIMA(1,0,6), ARIMA(3,0,0), ARIMA(2,0,13) ARIMA(0,0,12), ARIMA(2,0,2), ARIMA(1,0,12) and ARIMA(0,0,1) have been developed as the most suitable for simulating and forecasting the monthly-mean AOD over the eight selected locations. The Stationary R-squared, R-squared, Root Mean Square Error (RMSE) and Normalized BIC (Bayesian Information Criterion) are used to test the validity and applicability of the developed ARIMA models revealing adequate accuracy in the model performance. The values of Hurst Exponent, Fractal Dimension and Predictability Index for AODs are about 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 (> 0.7) are observed over the industrialized and densely-populated IGP sites associated with low ones over the foothills/slopes of the Himalayas. The trends in AOD during the ~ 13-year period differentiates depending on season and site. During post-monsoon and winter accumulation of aerosols and increasing trends are shown over IGP sites, which are neutralized in pre-monsoon and become slightly negative in monsoon. The AOD over the Himalayan sites does not exhibit any significant trend and seems to be practically unaffected by the aerosol built-up over IGP.

Statistical modelling, analysis of physico-chemical parameters chemical oxygen demand (COD), biochemical oxygen demand (BOD), dissolved oxygen (DO), water temperature (WT), free ammonia (AMM), total Kjeldahl nitrogen (TKN), total coliform (TC), fecal coliform (FC) and potential of hydrogen (pH) monitored at the Hathnikund barrage (Haryana) sample site of river Yamuna in India have been studied. It has been observed that water-quality parameters such as COD-BOD, AMM-TKN, WT-pH and TC-FC are positively correlated whereas COD-DO, BOD-DO, TKN-FC and DO-WT are negatively correlated. For water-quality parameters such as pH, AMM, TC and FC no seasonal pattern is observed. Parameters such as COD, BOD, TKN, DO and WT follow a six-month seasonal pattern. All the parameters except DO and WT follow a positive trend for monthly and annual variations. BOD, AMM and TKN have anti-persistence behaviour for both monthly and yearly variations. For parameters COD (+27.83%), BOD (+42.36%), AMM (+49.63%), TKN (+22.71%)...

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.

Statistical and wavelet analysis of water quality parameters COD (chemical oxygen demand), BOD (biochemical oxygen demand), DO (dissolved oxygen), WT (water temperature), AMM (free ammonia), TKN (total kjeldahl nitrogen), TC (total coliform), FC (fecal coliform) and pH (potential of hydrogen) monitored at Nizamuddin bridge-mid stream (Delhi) of Yamuna River in India have been studied. It has been observed that COD is highly correlated with BOD, AMM and TKN. Daubechies wavelet at level 5 (Db"5) have been calculated for each water quality parameters as it gives the finer scale approximation and decomposition of each water parameters. Wavelet decomposition and approximation using Db"5 of each parameters presented in seven parts namely s, a"5, d"1, d"2, d"3, d"4 and d"5 where ''s'' represents signal or raw data; low frequency part ''a"5'' gives an approximate of signal at level 5; high frequency parts d"1, d"2, d"3, d"4 and d"5 contains the detail of ''s'' at different levels respectively.

Time series, trend and wavelet analysis of water quality parameters Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO) monitored at Nizamuddin bridge mid-stream of river Yamuna in India have been studied. In Auto Regressive Integrated Moving Average (ARIMA) model (p, d, q) value of ‘d’ is zero thus process is stationary. It is observed that Root Mean Square Error (RMSE) value are comparatively very low thus dependent series is closed with the model predicted level. The Mean Absolute Percentage Error (MAPE), MaxAPE, MAE, MaxAE, normalized Bayesian Information Criterion (BIC) are calculated and have low value. Trend is calculated by using Auto correlation function (ACF), Partial auto correlation function (PACF) and lag. The predictive model is useful at 95% confidence limits. The 1-D discrete and continuous Daubechies wavelet analysis explains that the parameters COD, BOD, DO have the maximum value 120, 50, 8; the value of a5 varies between 52 to 78, 10 to 30, 0.2 to 1.4 and the scale values of Db5, i.e., d5 ranges between −10 to 10; −5 to 5 and −0.5 to 0.5, respectively. It is concluded that as the value of COD, BOD increases and value of DO decreases, water is not fit for drinking, agriculture and industrial use.