1
|
Makkhan SJS, Singh S, Parmar KS, Kaushal S, Soni K. Comparison of hybrid machine learning model for the analysis of black carbon in air around the major coal mines of India. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07909-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
2
|
Singh A, Singh S, Srivastava AK, Payra S, Pathak V, Shukla AK. Climatology and model prediction of aerosol optical properties over the Indo-Gangetic Basin in north India. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:827. [PMID: 36156160 DOI: 10.1007/s10661-022-10440-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 08/30/2022] [Indexed: 06/16/2023]
Abstract
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 AE412-470. 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).
Collapse
Affiliation(s)
- Amarendra Singh
- Institute of Engineering and Technology, Lucknow, India.
- Ministry of Earth Sciences, Indian Institute of Tropical Meteorology, New Delhi, India.
| | - Sumit Singh
- Institute of Engineering and Technology, Lucknow, India
| | - A K Srivastava
- Ministry of Earth Sciences, Indian Institute of Tropical Meteorology, New Delhi, India.
| | - Swagata Payra
- Department of Remote Sensing, Birla Institute of Technology, Mesra, Ranchi, India
| | | | - A K Shukla
- Institute of Engineering and Technology, Lucknow, India
| |
Collapse
|
3
|
Xu Y, Long Z, Pan W, Wang Y. Low-cost sensor outlier detection framework for on-line monitoring of particle pollutants in multiple scenarios. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:52963-52980. [PMID: 34021450 DOI: 10.1007/s11356-021-14419-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/10/2021] [Indexed: 06/12/2023]
Abstract
Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high-performance algorithms. The framework proposed in this paper provides a general process including five basic tasks and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation.
Collapse
Affiliation(s)
- Yinyue Xu
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China
| | - Zhengwei Long
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China.
| | - Wuxuan Pan
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China
| | - Yukun Wang
- Tianjin Key Laboratory of Indoor Air Environmental Quality Control, School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China
| |
Collapse
|
4
|
Singh S, Parmar KS, Kumar J. Soft computing model coupled with statistical models to estimate future of stock market. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05506-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
5
|
Singh S, Parmar KS, Kaur J, Kumar J, Makkhan SJS. Prediction of COVID-19 pervasiveness in six major affected states of India and two-stage variation with temperature. AIR QUALITY, ATMOSPHERE, & HEALTH 2021; 14:2079-2090. [PMID: 34567282 PMCID: PMC8453038 DOI: 10.1007/s11869-021-01075-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 08/11/2021] [Indexed: 05/18/2023]
Abstract
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.
Collapse
Affiliation(s)
- Sarbjit Singh
- Guru Nanak Dev University College, Narot Jaimal Singh, Pathankot, Punjab 145026 India
- Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab 143005 India
| | - Kulwinder Singh Parmar
- Department of Mathematics, I.K. Gujral Punjab Technical University, Punjab, 144603 India
| | - Jatinder Kaur
- Department of Mathematics, I.K. Gujral Punjab Technical University, Punjab, 144603 India
- Guru Nanak Dev University College, Verka, Amritsar, Punjab 143501 India
| | - Jatinder Kumar
- Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab 143005 India
| | | |
Collapse
|
6
|
Singh S, Parmar KS, Makkhan SJS, Kaur J, Peshoria S, Kumar J. 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. CHAOS, SOLITONS, AND FRACTALS 2020; 139:110086. [PMID: 32834622 PMCID: PMC7345281 DOI: 10.1016/j.chaos.2020.110086] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2020] [Revised: 06/22/2020] [Accepted: 07/02/2020] [Indexed: 05/18/2023]
Abstract
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.
Collapse
Affiliation(s)
- Sarbjit Singh
- Guru Nanak Dev University College, Narot Jaimal Singh, Pathankot, Punjab, India
- Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, India
| | | | - Sidhu Jitendra Singh Makkhan
- Department of Mathematics, Sri Guru Angad Dev College, Khadoor Sahib, Tarn Taran, Punjab, India
- Department of Mathematics, Lovely Professional University, Punjab, India
| | - Jatinder Kaur
- Department of Mathematics, I.K. Gujral Punjab Technical University, Punjab, India
- Guru Nanak Dev University College, Verka, Amritsar, Punjab, India
| | - Shruti Peshoria
- Centre for Fire, Explosive and Environment Safety (CFEES), Defence Research and Development Organisation (DRDO), Timarpur, Delhi 110054, India
| | - Jatinder Kumar
- Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, India
| |
Collapse
|
7
|
Singh S, Parmar KS, Kumar J, Makkhan SJS. 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. CHAOS, SOLITONS, AND FRACTALS 2020; 135:109866. [PMID: 32395038 PMCID: PMC7211653 DOI: 10.1016/j.chaos.2020.109866] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/04/2020] [Indexed: 05/04/2023]
Abstract
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.
Collapse
Affiliation(s)
- Sarbjit Singh
- Guru Nanak Dev University College, Narot Jaimal Singh, Pathankot, Punjab,145026, India
- Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, 143005, India
| | - Kulwinder Singh Parmar
- Department of Mathematics, I.K. Gujral Punjab Technical University, Punjab, 144603, India
| | - Jatinder Kumar
- Department of Mathematics, Guru Nanak Dev University, Amritsar, Punjab, 143005, India
| | - Sidhu Jitendra Singh Makkhan
- Department of Mathematics, Sri Guru Angad Dev College, Khadoor Sahib, Tarn Taran, Punjab, 143117, India
- Department of Mathematics, Lovely Professional University, Punjab, 144411, India
| |
Collapse
|
8
|
Parmar KS, Makkhan SJS, Kaushal S. Neuro-fuzzy-wavelet hybrid approach to estimate the future trends of river water quality. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04560-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
9
|
Zeng B, Li S, Meng W, Zhang D. An improved gray prediction model for China's beef consumption forecasting. PLoS One 2019; 14:e0221333. [PMID: 31490952 PMCID: PMC6730899 DOI: 10.1371/journal.pone.0221333] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 08/06/2019] [Indexed: 12/01/2022] Open
Abstract
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.
Collapse
Affiliation(s)
- Bo Zeng
- College of Business Planning, Chongqing Technology and Business University, Chongqing, PR China
| | - Shuliang Li
- Collaborative Innovation Center for Chongqing‘s Modern Trade Logistics & Supply Chain, Chongqing Technology and Business University, Chongqing, PR China
- * E-mail:
| | - Wei Meng
- College of Business Planning, Chongqing Technology and Business University, Chongqing, PR China
- Collaborative Innovation Center for Chongqing‘s Modern Trade Logistics & Supply Chain, Chongqing Technology and Business University, Chongqing, PR China
| | - Dehai Zhang
- College of Business Planning, Chongqing Technology and Business University, Chongqing, PR China
| |
Collapse
|
10
|
Prijith SS, Rao PVN, Mohan M, Sai MVRS, Ramana MV. Trends of absorption, scattering and total aerosol optical depths over India and surrounding oceanic regions from satellite observations: role of local production, transport and atmospheric dynamics. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:18147-18160. [PMID: 29691752 DOI: 10.1007/s11356-018-2032-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 04/13/2018] [Indexed: 06/08/2023]
Abstract
The study examines trends of scattering, absorption and total aerosol optical depths (SAOD, AAOD and AOD) over India and surrounding oceanic regions and explores role of local production, long-range transport and atmospheric dynamics on observed trends. Long-term satellite observations are used to estimate trends and assess their statistical significance. Significant spatial and seasonal changes are observed in trends of SAOD, AAOD and AOD. AOD is observed to be increasing during post monsoon and winter over most of the land mass and surrounding oceanic regions, whereas decreasing trends over land and increasing trends over oceanic regions are observed in pre-monsoon and summer months. In general, SAOD and AAOD show similar trends (if there is any) as that of AOD over most of the regions in most of the months. Strongest positive trends over land regions are observed in November with trend of AOD greater than 0.01 year-1, especially over Indo-Gangetic Plain (IGP). Increase of AOD over IGP in post monsoon is contributed significantly by absorbing aerosols with rate of increase ~ 0.005 AAOD year-1. AAODs are observed to be increasing over Arabian Sea and Bay of Bengal (BoB) in December also, with rate ~ 0.003 AAOD year-1. Strongest positive trends over Arabian Sea and BoB are observed in June with rate of increase greater than 0.02 AOD year-1, whereas strong negative trends are observed over north-west India in the same period with rate of decrease greater than 0.02 AOD year-1. Over IGP, AOD, AAOD and SAOD show contrasting trends in winter and summer seasons. AAOD exhibits strongest decreasing trend over IGP during April-June. Positive trends of AOD over Arabian Sea and BoB are favoured significantly by changes in circulation dynamics. Atmospheric convergence is observed to be strengthening over these regions in April and June, leading to more accumulation and hence positive trends of AOD. Aerosol transport over to the Arabian Sea is observed to be enhancing and contributing significantly to AOD increase over the Arabian Sea in pre-monsoon and summer months. Enhancement in aerosol transport over to the Arabian Sea is observed in pre-monsoon at higher altitudes above 3 km, whereas it is observed in summer at lower levels. However, decreasing trends of AOD over north-west India and IGP during pre-monsoon and summer are observed to be due to decrease in aerosol transport from the continental regions at the west.
Collapse
Affiliation(s)
| | | | | | | | - Muvva Venkata Ramana
- National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad, India
| |
Collapse
|
11
|
Time-series analyses of water temperature and dissolved oxygen concentration in Lake Valkea-Kotinen (Finland) during ice season. ECOL INFORM 2016. [DOI: 10.1016/j.ecoinf.2015.06.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
12
|
Soni K, Parmar KS, Kapoor S, Kumar N. Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2016; 553:258-265. [PMID: 26925737 DOI: 10.1016/j.scitotenv.2016.02.075] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2015] [Revised: 02/10/2016] [Accepted: 02/11/2016] [Indexed: 05/11/2023]
Abstract
A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend.
Collapse
Affiliation(s)
- Kirti Soni
- CSIR-National Physical Laboratory, Delhi, India
| | - Kulwinder Singh Parmar
- Department of Mathematics, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India.
| | - Sangeeta Kapoor
- Laxmi Narayan College of Technology & Science (LNCTS), Bhopal, MP, India
| | | |
Collapse
|