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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.

The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by splitting data into three equal parts, where the models were evaluated using each part. The main aim of this study was to find an accurate prediction model for estimating the water quality of the Yamuna River. It is worth noting that the hybrid neuro-fuzzy and LSSVM methods have not been previously compared for this issue. Monthly water quality parameters, total kjeldahl nitrogen, free ammonia, total coliform, water temperature, potential of hydrogen, and fecal coliform were considered as inputs to model chemical oxygen demand (COD). The performance of hybrid neuro-fuzzy models in predicting COD was compared with classical neuro-fuzzy and least square support vector machine (LSSVM) methods. The results showed higher accuracy in COD prediction when free ammonia, total kjeldahl nitrogen, and water temperature were used as inputs. Hybrid neuro-fuzzy models improved the root mean square error of the classical neuro-fuzzy model and LSSVM by 12% and 4%, respectively. The neuro-fuzzy models optimized with harmony search provided the best accuracy with the lowest root mean square error (13.659) and mean absolute error (11.272), while the particle swarm optimization and teaching–learning-based optimization showed the highest computational speed (21 and 24 min) compared to the other models.

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.

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.

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).

A study of the assessment and management of air quality was carried out at 11 coal mines in India. Long-term observations (about 13 years, March 2000–December 2012) and modeling of aerosol loading over coal mines in India are analyzed in the present study. In this respect, the Box-Jenkins popular autoregressive integrated moving average (ARIMA) model was applied to simulate the monthly mean Terra Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD550 nm) over 11 sites in the coal mines region. The ARIMA model was found as the most suitable model with least normalized Bayesian information criterion (BIC) and root mean square error and high value of R 2. Estimation was done with the Ljung-Box test. Finally, a forecast for a 3-year period from January 2013 to December 2015 was calculated which showed that the model forecasted values are following the observed trend quite well over all mining areas in India. The average values of AOD for the next 3 years (2013–2015) at all sites are found to be 0.575 ± 0.13 (Raniganj), 0.452 ± 0.12 (Jharia), 0.339 ± 0.13 (Bokaro), 0.280 ± 0.09 (Bishrampur), 0.353 ± 0.13 (Korba), 0.308 ± 0.08 (Talcher), 0.370 ± 0.11 (Wardha), 0.35 ± 0.10 (Adilabad), 0.325 ± 0.09 (Warangal), 0.467 ± 0.09 (Godavari Valley), and 0.236 ± 0.07 (Cuddapah), respectively. In addition, long-term lowest monthly mean AOD550 values are observed over Bishrampur followed by Cuddapah, Talcher, Warangal, Adilabad, Korba, Wardha, Godavari Valley, Jharia, and Raniganj. Raniganj and Jharia exhibit the highest AOD values due to opencast mines and extensive mining activities as well as a large number of coal fires. Similarly, the highest AOD values are observed during the monsoon season among all four seasons over all the mining sites. Raniganj exhibits the highest AOD value at all seasons and at all sites. In contrast, the lowest seasonal AOD values are observed during the post-monsoon season over Raniganj, Talcher, Wardha, Adilabad, Warangal, and Godavari Valley. Similarly, over Jharia, Bokaro, Bishrampur, Korba, and Cuddapah, the lowest AOD values are found in the winter season. Increasing trends in AOD550 have been observed over Raniganj, Bokaro, Bishrampur, Korba, Talcher, and Wardha as well as over Adilabad and Godavari Valley, which is in agreement with previous works. Negative or decreasing AOD trend is found only over Jharia, Warangal, and Cuddapah without being statistically significant. Seasonal trends in AODs have also been studied in the present paper.

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.