Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India
Abstract
Although the variability and prediction of rainfall is an essential issue of the Santhal Pargana Division of the Jharkhand State but the issue is still far from its’ conclusive statement till date. Therefore, this study aimed to simulate the monthly rainfall from 1901 to 2020 using an eight-step procedure. After downloading the monthly rainfall for the Santhal Pargana Division from 1901 to 2020, the TBATS and Naive models were used to simulate the rainfall. The accuracy assessment of each model was done by using the MASE, MAE, RMSE, ME, and R. For the Naïve model, the Godda station was noticed with a comparatively high combined error. The lowest combined error was found for the Pakur station in case of Naïve models. Similar result was also obtained for the TBATS model. The TBATS was found with comparatively higher accuracy, as the combined error was less for the TBATS. The spatial assessment for the standardized rainfall varied from 84.419 mm. to 149.225 mm. For the Naïve predicted model, the rainfall was marked in between 8.133 mm. to 67.059 mm. For the TBATS fitted model, the rainfall fluctuated from the 37.127 mm. to 62.993 mm. Dumka station was noticed with comparatively low rainfall (i.e.,37.127 mm.). Deoghar and Jamtara stations were marked with a moderate rainfall. Remaining stations were marked with higher amount of rainfall for the TBATS fitted model. The Wilcoxon test proved that each model was significant at 95% confidence interval. The result produced in this research is fruitful enough to be utilized for agricultural planning in the Santhal Pargana Division of the Jharkhand state, India.
Keywords : TBATS model; Naive model; simulation; accuracy
Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
References
Abdelgawad, H., Abdulazim, T., Abdulhai, B., Hadayeghi, A., & Harrett, W. (2015). Data imputation and nested seasonality time series modelling for permanent data collection stations: Methodology and application to Ontario. Canadian Journal of Civil Engineering, 42(5), 287–302. https://doi.org/10.1139/cjce-2014-0087.
Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052.
Alizadeh, A., Rajabi, A., Shabanlou, S., Yaghoubi, B., & Yosefvand, F. (2021). Simulation of rainfall and runoff time series using robust machine learning*. Irrigation and Drainage, 70(1), 84–102. https://doi.org/10.1002/ird.2518.
Azad, Md. A. K., Islam, A. R. Md. T., Ayen, K., Rahman, Md. S., Shahid, S., & Mallick, J. (2022). Changes in monsoon precipitation patterns over Bangladesh and its teleconnections with global climate. Theoretical and Applied Climatology, 148(3), 1261–1278. https://doi.org/10.1007/s00704-022-03996-8.
Azad, S., Debnath, S., & Rajeevan, M. (2015). Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach. Environmental Processes, 2(4), 717–727. https://doi.org/10.1007/s40710-015-0108-0.
Bagirov, A. M., Mahmood, A., & Barton, A. (2017). Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach. Atmospheric Research, 188, 20–29. https://doi.org/10.1016/j.atmosres.2017.01.003.
Bahadur, R., Jaiswal, R. K., Nema, A. K., Gangwar, A., & Kumar, S. (2020). Trends Analysis of Rainfall and Temperature over Nagwan Watershed, Hazaribagh District, Jharkhand. Current Journal of Applied Science and Technology, 112–128. https://doi.org/10.9734/cjast/2020/v39i1930798.
Balan, S., M., Selvan, J. P., Bisht, H. R., Gadjil, Y. A., Khaladkar, I. R., & Lomte, V. M. (2019). Rainfall Prediction using Deep Learning on Highly Non-Linear Data. International Journal of Research in Engineering, Science and Management.
Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. https://doi.org/10.1016/j.mlwa.2021.100204.
Bauer, D. F. (1972). Constructing Confidence Sets Using Rank Statistics. Journal of the American Statistical Association, 67(339), 687–690. https://doi.org/10.2307/2284469.
Bhardwaj, K., Shah, D., Aadhar, S., & Mishra, V. (2020). Propagation of Meteorological to Hydrological Droughts in India. Journal of Geophysical Research: Atmospheres, 125(22), e2020JD033455. https://doi.org/10.1029/2020JD033455.
Bhatt, D., & Mall, R. K. (2015). Surface Water Resources, Climate Change and Simulation Modeling. Aquatic Procedia, 4, 730–738. https://doi.org/10.1016/j.aqpro.2015.02.094.
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T., von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., & Immerzeel, W. W. (2019). Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nature Sustainability, 2(7), Article 7. https://doi.org/10.1038/s41893-019-0305-3.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
Abdelgawad, H., Abdulazim, T., Abdulhai, B., Hadayeghi, A., & Harrett, W. (2015). Data imputation and nested seasonality time series modelling for permanent data collection stations: Methodology and application to Ontario. Canadian Journal of Civil Engineering, 42(5), 287–302. https://doi.org/10.1139/cjce-2014-0087.
Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052.
Alizadeh, A., Rajabi, A., Shabanlou, S., Yaghoubi, B., & Yosefvand, F. (2021). Simulation of rainfall and runoff time series using robust machine learning*. Irrigation and Drainage, 70(1), 84–102. https://doi.org/10.1002/ird.2518.
Azad, Md. A. K., Islam, A. R. Md. T., Ayen, K., Rahman, Md. S., Shahid, S., & Mallick, J. (2022). Changes in monsoon precipitation patterns over Bangladesh and its teleconnections with global climate. Theoretical and Applied Climatology, 148(3), 1261–1278. https://doi.org/10.1007/s00704-022-03996-8.
Azad, S., Debnath, S., & Rajeevan, M. (2015). Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach. Environmental Processes, 2(4), 717–727. https://doi.org/10.1007/s40710-015-0108-0.
Bagirov, A. M., Mahmood, A., & Barton, A. (2017). Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach. Atmospheric Research, 188, 20–29. https://doi.org/10.1016/j.atmosres.2017.01.003.
Bahadur, R., Jaiswal, R. K., Nema, A. K., Gangwar, A., & Kumar, S. (2020). Trends Analysis of Rainfall and Temperature over Nagwan Watershed, Hazaribagh District, Jharkhand. Current Journal of Applied Science and Technology, 112–128. https://doi.org/10.9734/cjast/2020/v39i1930798.
Balan, S., M., Selvan, J. P., Bisht, H. R., Gadjil, Y. A., Khaladkar, I. R., & Lomte, V. M. (2019). Rainfall Prediction using Deep Learning on Highly Non-Linear Data. International Journal of Research in Engineering, Science and Management.
Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. https://doi.org/10.1016/j.mlwa.2021.100204.
Bauer, D. F. (1972). Constructing Confidence Sets Using Rank Statistics. Journal of the American Statistical Association, 67(339), 687–690. https://doi.org/10.2307/2284469.
Bhardwaj, K., Shah, D., Aadhar, S., & Mishra, V. (2020). Propagation of Meteorological to Hydrological Droughts in India. Journal of Geophysical Research: Atmospheres, 125(22), e2020JD033455. https://doi.org/10.1029/2020JD033455.
Bhatt, D., & Mall, R. K. (2015). Surface Water Resources, Climate Change and Simulation Modeling. Aquatic Procedia, 4, 730–738. https://doi.org/10.1016/j.aqpro.2015.02.094.
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T., von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., & Immerzeel, W. W. (2019). Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nature Sustainability, 2(7), Article 7. https://doi.org/10.1038/s41893-019-0305-3.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
Box George E.P., J. G. M., & Reincel G.C., L. G. M. (n.d.). Time Series Analysis: Forecasting and Control, 5th Edition | Wiley. Wiley.Com.
Casado-Vara, R., Martin del Rey, A., Pérez-Palau, D., de-la-Fuente-Valentín, L., & Corchado, J. M. (2021). Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training. Mathematics, 9(4). https://doi.org/10.3390/math9040421.
Government of India. (2022). Census of India. Retrieved October 25, 2022, from https://censusindia.gov.in/census.website/data/census-tables.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014.
Chang, Y., Xiao, J., Li, X., Middel, A., Zhang, Y., Gu, Z., Wu, Y., & He, S. (2021). Exploring diurnal thermal variations in urban local climate zones with ECOSTRESS land surface temperature data. Remote Sensing of Environment, 263, 112544. https://doi.org/10.1016/j.rse.2021.112544.
Chao, Z., Pu, F., Yin, Y., Han, B., & Chen, X. (2018). Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors. Journal of Sensors, 2018, e6184713. https://doi.org/10.1155/2018/6184713.
Chaturvedi, A. (2015). Rainfall Prediction using Back-Propagation Feed Forward Network. International Journal of Computer Applications, 119(4), 1–5.
Cosgrove, W. J., & Loucks, D. P. (2015). Water management: Current and future challenges and research directions. Water Resources Research, 51(6), 4823–4839. https://doi.org/10.1002/2014WR016869.
Dang, Q.-V. (2020). Understanding the Decision of Machine Learning Based Intrusion Detection Systems. In T. K. Dang, J. Küng, M. Takizawa, & T. M. Chung (Eds.), Future Data and Security Engineering (pp. 379–396). Springer International Publishing. https://doi.org/10.1007/978-3-030-63924-2_22.
Das, P., Maya, K., & Padmalal, D. (2022). Hydrogeochemistry of the Indian thermal springs: Current status. Earth-Science Reviews, 224, 103890. https://doi.org/10.1016/j.earscirev.2021.103890.
Das, S. (2019). Four decades of water and sediment discharge records in Subarnarekha and Burhabalang basins: An approach towards trend analysis and abrupt change detection. Sustainable Water Resources Management, 5(4), 1665–1676. https://doi.org/10.1007/s40899-019-00326-1.
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771.
Doorn, N. (2021). Artificial intelligence in the water domain: Opportunities for responsible use. Science of The Total Environment, 755, 142561. https://doi.org/10.1016/j.scitotenv.2020.142561.
Dubache, G., Ogwang, B. A., Ongoma, V., & Towfiqul Islam, A. R. Md. (2019). The effect of Indian Ocean on Ethiopian seasonal rainfall. Meteorology and Atmospheric Physics, 131, 1753–1761. https://doi.org/10.1007/s00703-019-00667-8.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773.
Ferrant, S., Selles, A., Le Page, M., Herrault, P.-A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., Saqalli, M., Dewandel, B., Caballero, Y., Ahmed, S., Maréchal, J.-C., & Kerr, Y. (2017). Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India. Remote Sensing, 9(11). https://doi.org/10.3390/rs9111119.
Franses, P. H. (2016). A note on the Mean Absolute Scaled Error. International Journal of Forecasting, 32(1), 20–22. https://doi.org/10.1016/j.ijforecast.2015.03.008.
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., & Stoffel, M. (2014). 21st century climate change in the European Alps—A review. Science of The Total Environment, 493, 1138–1151. https://doi.org/10.1016/j.scitotenv.2013.07.050.
Granger, C. W. J., & Andersen, A. (1978). On the invertibility of time series models. Stochastic Processes and Their Applications, 8(1), 87–92. https://doi.org/10.1016/0304-4149(78)90069-8.
Grubbs, F. E. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1–21. https://doi.org/10.1080/00401706.1969.10490657.
Gupta, C. K., & Kumar, R. (2018). Rainfall variation and trend analysis of Garhwa district, Jharkhand: An assessment of spatial and seasonal variability. Journal of Pharmacognosy and Phytochemistry, 7(5), 2143–2145.
Hyndman, R. J., & Athanasopoulos, A. (2002). Forecasting: Principles and Practice (2nd ed). Retrieved October 23, 2022, from https://otexts.com/fpp2/.
India-WRIS. (2002). Retrieved October 25, 2022, from https://indiawris.gov.in/wris/#/rainfall.
Kayet, N., Pathak, K., Chakrabarty, A., Kumar, S., Chowdary, V. M., & Singh, C. P. (2022). Risk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region. Geocarto International, 37(11), 3091–3115. https://doi.org/10.1080/10106049.2020.1849413.
Kundu, S. K., & Mondal, T. K. (2019). Analysis of long-term rainfall trends and change point in West Bengal, India. Theoretical and Applied Climatology, 138(3), 1647–1666. https://doi.org/10.1007/s00704-019-02916-7.
Lee, D. K., In, J., & Lee, S. (2015). Standard deviation and standard error of the mean. Korean Journal of Anesthesiology, 68(3), 220–223. https://doi.org/10.4097/kjae.2015.68.3.220.
Li, H., Zhang, L., Zhou, X., & Huang, B. (2017). Cost-sensitive sequential three-way decision modeling using a deep neural network. International Journal of Approximate Reasoning, 85, 68–78. https://doi.org/10.1016/j.ijar.2017.03.008.
Liu, F., & Liu, R. (2019). China, the United States, and order transition in East Asia: An economy-security Nexus approach. The Pacific Review, 32(6), 972–995. https://doi.org/10.1080/09512748.2018.1526205.
Liu, Q., Zou, Y., Liu, X., & Linge, N. (2019). A survey on rainfall forecasting using artificial neural network. International Journal of Embedded Systems, 11(2), 240. https://doi.org/10.1504/IJES.2019.098300.
Livera, A. M. D. (2010). Automatic forecasting with a modified exponential smoothing state space framework. In Monash Econometrics and Business Statistics Working Papers (No. 10/10; Monash Econometrics and Business Statistics Working Papers). Monash University, Department of Econometrics and Business Statistics. https://ideas.repec.org/p/msh/ebswps/2010-10.html.
Mandal, A., & Ray, A. (2015). Petrological and geochemical studies of ultramafic–mafic rocks from the North Puruliya Shear Zone (eastern India). Journal of Earth System Science, 124(8), 1781–1799. https://doi.org/10.1007/s12040-015-0634-1.
Mehdiyev, N., & Fettke, P. (2021). Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring. In W. Pedrycz & S.-M. Chen (Eds.), Interpretable Artificial Intelligence: A Perspective of Granular Computing (pp. 1–28). Springer International Publishing. https://doi.org/10.1007/978-3-030-64949-4_1.
Menon, A., Levermann, A., Schewe, J., Lehmann, J., & Frieler, K. (2013). Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth System Dynamics, 4(2), 287–300. https://doi.org/10.5194/esd-4-287-2013.
Mie Sein, Z. M., Islam, A. R. M. T., Maw, K. W., & Moya, T. B. (2015). Characterization of southwest monsoon onset over Myanmar. Meteorology and Atmospheric Physics, 127(5), 587–603. https://doi.org/10.1007/s00703-015-0386-0.
Mohapatra, M., Srivastava, A. K., Balachandran, S., & Geetha, B. (2017). Inter-annual Variation and Trends in Tropical Cyclones and Monsoon Depressions Over the North Indian Ocean. In M. N. Rajeevan & S. Nayak (Eds.), Observed Climate Variability and Change over the Indian Region (pp. 89–106). Springer. https://doi.org/10.1007/978-981-10-2531-0_6.
Mohd, R., Butt, M. A., & Baba, M. Z. (2020). GWLM–NARX: Grey Wolf Levenberg–Marquardt-based neural network for rainfall prediction. Data Technologies and Applications, 54(1), 85–102. https://doi.org/10.1108/DTA-08-2019-0130.
Mora, C., Spirandelli, D., Franklin, E. C., Lynham, J., Kantar, M. B., Miles, W., Smith, C. Z., Freel, K., Moy, J., Louis, L. V., Barba, E. W., Bettinger, K., Frazier, A. G., Colburn IX, J. F., Hanasaki, N., Hawkins, E., Hirabayashi, Y., Knorr, W., Little, C. M., … Hunter, C. L. (2018). Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions. Nature Climate Change, 8(12), Article 12. https://doi.org/10.1038/s41558-018-0315-6.
Mukaka, M. M. (2012). A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3). https://doi.org/10.4314/mmj.v24i3.
Mukherjee, S., Mishra, A., & Trenberth, K. E. (2018). Climate Change and Drought: A Perspective on Drought Indices. Current Climate Change Reports, 4(2), 145–163. https://doi.org/10.1007/s40641-018-0098-x.
Myles, H., Wolfe, Douglas. A., & Checksen, E. (n.d.). Nonparametric Statistical Methods, 3rd Edition | Wiley. Wiley.Com.
Nourani, V., Uzelaltinbulat, S., Sadikoglu, F., & Behfar, N. (2019). Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation. Atmosphere, 10(2). https://doi.org/10.3390/atmos10020080.
Prakash, V., & Mishra, V. (2022). Soil Moisture and Streamflow Data Assimilation for Streamflow Prediction in the Narmada River Basin. Journal of Hydrometeorology, 1(aop). https://doi.org/10.1175/JHM-D-21-0139.1.
Raha, S., & Gayen, S. K. (2020). Simulation of meteorological drought using exponential smoothing models: A study on Bankura District, West Bengal, India. SN Applied Sciences, 2(5), 909. https://doi.org/10.1007/s42452-020-2730-3.
Rahman, Md. R., & Lateh, H. (2017). Climate change in Bangladesh: A spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theoretical and Applied Climatology, 128(1), 27–41. https://doi.org/10.1007/s00704-015-1688-3.
Saha, A., Singh, K. N., Ray, M., & Rathod, S. (2020). A hybrid spatio-temporal modelling: An application to space-time rainfall forecasting. Theoretical and Applied Climatology, 142(3), 1271–1282. https://doi.org/10.1007/s00704-020-03374-2.
Samantaray, S., Tripathy, O., Sahoo, A., & Ghose, D. K. (2020). Rainfall Forecasting Through ANN and SVM in Bolangir Watershed, India. In S. C. Satapathy, V. Bhateja, J. R. Mohanty, & S. K. Udgata (Eds.), Smart Intelligent Computing and Applications (pp. 767–774). Springer. https://doi.org/10.1007/978-981-13-9282-5_74.
Scheuerer, M. (2014). Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics. Quarterly Journal of the Royal Meteorological Society, 140(680), 1086–1096. https://doi.org/10.1002/qj.2183.
Shah, N. V., Patel, Y. S., & DBhangaonkar, P. (2021). Assessing Impact of Climate Change on Rainfall Patterns of Vadodara District, Gujarat, India. Journal of Physics: Conference Series, 1714(1), 012046. https://doi.org/10.1088/1742-6596/1714/1/012046.
Sharma, T., & Singh, S. (2021). Feto-maternal outcome in previous one cesarean section: A retrospective observational study at a district hospital of Jharkhand. International Journal of Reproduction, Contraception, Obstetrics and Gynecology, 10(10), 3834–3840.
Shmuel, A., Ziv, Y., & Heifetz, E. (2022). Machine-Learning-based evaluation of the time-lagged effect of meteorological factors on 10-hour dead fuel moisture content. Forest Ecology and Management, 505, 119897. https://doi.org/10.1016/j.foreco.2021.119897.
Shree, S., & Kumar, M. (2018). Analysis of seasonal and annual rainfall trends for Ranchi district, Jharkhand, India. Environmental Earth Sciences, 77(19), 693. https://doi.org/10.1007/s12665-018-7884-6.
Silva, L. da. (2021). Modelagem geométrica do par roda-trilho com descrição por meio de NURBS. [Text, Universidade de São Paulo]. https://doi.org/10.11606/D.3.2021.tde-25102021-145823.
Singh, D., Ghosh, S., Roxy, M. K., & McDermid, S. (2019). Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings. WIREs Climate Change, 10(2), e571. https://doi.org/10.1002/wcc.571.
Stefansky, W. (2012). Rejecting Outliers in Factorial Designs. Technometrics: 14(2). https://www.tandfonline.com/doi/abs/10.1080/00401706.1972.10488930.
Tirkey, A. S., Ghosh, M., Pandey, A. C., & Shekhar, S. (2018). Assessment of climate extremes and its long term spatial variability over the Jharkhand state of India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 49–63. https://doi.org/10.1016/j.ejrs.2016.12.007.
Tong, H., & Lim, K. S. (1980). Threshold Autoregression, Limit Cycles and Cyclical Data. Journal of the Royal Statistical Society. Series B (Methodological), 42(3), 245–292.
Upadhayay, U., Kumar, N., Kumar, R., & Kumari, P. (2019). Hydro-Geological Status of the Core and Buffer Zone of Beekay Steel Industries Limited, Adityapur Industrial Area, Saraikela, Kharsawan, Jharkhand. In Wastewater Reuse and Watershed Management. Apple Academic Press.
Urbanczyk-Wochniak, E., Luedemann, A., Kopka, J., & Selbig, J. (n.d.). Parallel analysis of transcript and metabolic profiles: A new approach in systems biology | EMBO reports. Retrieved October 25, 2022, from https://www.embopress.org/doi/full/10.1038/sj.embor.embor944.
Vuyyuru, V. Ankalu., Apparao, Giduturi., & Anuradha, S. (2021). Rainfall Prediction Using Machine Learning Based Ensemble Model. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 1–4. https://doi.org/10.1109/ISCON52037.2021.9702409.
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324(1), 012049. https://doi.org/10.1088/1757-899X/324/1/012049.
Wang, Y., Yuan, Z., Liu, H., Xing, Z., Ji, Y., Li, H., Fu, Q., & Mo, C. (2022). A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting. Expert Systems with Applications, 187, 115872. https://doi.org/10.1016/j.eswa.2021.115872.
Xiang, Y., Gou, L., He, L., Xia, S., & Wang, W. (2018). A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874–883. https://doi.org/10.1016/j.asoc.2018.09.018.
Xu, C., Liao, Z., Li, C., Zhou, X., & Xie, R. (2022). Review on Interpretable Machine Learning in Smart Grid. Energies, 15(12). https://doi.org/10.3390/en15124427.
Yadav, M. (2022). South Asian Monsoon Extremes and Climate Change. In P. Saxena, A. Shukla, & A. K. Gupta (Eds.), Extremes in Atmospheric Processes and Phenomenon: Assessment, Impacts and Mitigation (pp. 59–86). Springer Nature. https://doi.org/10.1007/978-981-16-7727-4_4.
Yaseen, Z. M., Ali, M., Sharafati, A., Al-Ansari, N., & Shahid, S. (2021). Forecasting standardized precipitation index using data intelligence models: Regional investigation of Bangladesh. Scientific Reports, 11(1), Article 1. https://doi.org/10.1038/s41598-021-82977-9.
Zamani, R., Mirabbasi, R., Nazeri, M., Meshram, S. G., & Ahmadi, F. (2018). Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand state, India. Stochastic Environmental Research and Risk Assessment, 32(4), 1085–1097. https://doi.org/10.1007/s00477-017-1447-3.
Zhang, T., Yang, S., Jiang, X., & Zhao, P. (2016). Seasonal–Interannual Variation and Prediction of Wet and Dry Season Rainfall over the Maritime Continent: Roles of ENSO and Monsoon Circulation. Journal of Climate, 29(10), 3675–3695. https://doi.org/10.1175/JCLI-D-15-0222.1.
Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052.
Alizadeh, A., Rajabi, A., Shabanlou, S., Yaghoubi, B., & Yosefvand, F. (2021). Simulation of rainfall and runoff time series using robust machine learning*. Irrigation and Drainage, 70(1), 84–102. https://doi.org/10.1002/ird.2518.
Azad, Md. A. K., Islam, A. R. Md. T., Ayen, K., Rahman, Md. S., Shahid, S., & Mallick, J. (2022). Changes in monsoon precipitation patterns over Bangladesh and its teleconnections with global climate. Theoretical and Applied Climatology, 148(3), 1261–1278. https://doi.org/10.1007/s00704-022-03996-8.
Azad, S., Debnath, S., & Rajeevan, M. (2015). Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach. Environmental Processes, 2(4), 717–727. https://doi.org/10.1007/s40710-015-0108-0.
Bagirov, A. M., Mahmood, A., & Barton, A. (2017). Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach. Atmospheric Research, 188, 20–29. https://doi.org/10.1016/j.atmosres.2017.01.003.
Bahadur, R., Jaiswal, R. K., Nema, A. K., Gangwar, A., & Kumar, S. (2020). Trends Analysis of Rainfall and Temperature over Nagwan Watershed, Hazaribagh District, Jharkhand. Current Journal of Applied Science and Technology, 112–128. https://doi.org/10.9734/cjast/2020/v39i1930798.
Balan, S., M., Selvan, J. P., Bisht, H. R., Gadjil, Y. A., Khaladkar, I. R., & Lomte, V. M. (2019). Rainfall Prediction using Deep Learning on Highly Non-Linear Data. International Journal of Research in Engineering, Science and Management.
Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. https://doi.org/10.1016/j.mlwa.2021.100204.
Bauer, D. F. (1972). Constructing Confidence Sets Using Rank Statistics. Journal of the American Statistical Association, 67(339), 687–690. https://doi.org/10.2307/2284469.
Bhardwaj, K., Shah, D., Aadhar, S., & Mishra, V. (2020). Propagation of Meteorological to Hydrological Droughts in India. Journal of Geophysical Research: Atmospheres, 125(22), e2020JD033455. https://doi.org/10.1029/2020JD033455.
Bhatt, D., & Mall, R. K. (2015). Surface Water Resources, Climate Change and Simulation Modeling. Aquatic Procedia, 4, 730–738. https://doi.org/10.1016/j.aqpro.2015.02.094.
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T., von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., & Immerzeel, W. W. (2019). Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nature Sustainability, 2(7), Article 7. https://doi.org/10.1038/s41893-019-0305-3.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
Abdelgawad, H., Abdulazim, T., Abdulhai, B., Hadayeghi, A., & Harrett, W. (2015). Data imputation and nested seasonality time series modelling for permanent data collection stations: Methodology and application to Ontario. Canadian Journal of Civil Engineering, 42(5), 287–302. https://doi.org/10.1139/cjce-2014-0087.
Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052.
Alizadeh, A., Rajabi, A., Shabanlou, S., Yaghoubi, B., & Yosefvand, F. (2021). Simulation of rainfall and runoff time series using robust machine learning*. Irrigation and Drainage, 70(1), 84–102. https://doi.org/10.1002/ird.2518.
Azad, Md. A. K., Islam, A. R. Md. T., Ayen, K., Rahman, Md. S., Shahid, S., & Mallick, J. (2022). Changes in monsoon precipitation patterns over Bangladesh and its teleconnections with global climate. Theoretical and Applied Climatology, 148(3), 1261–1278. https://doi.org/10.1007/s00704-022-03996-8.
Azad, S., Debnath, S., & Rajeevan, M. (2015). Analysing Predictability in Indian Monsoon Rainfall: A Data Analytic Approach. Environmental Processes, 2(4), 717–727. https://doi.org/10.1007/s40710-015-0108-0.
Bagirov, A. M., Mahmood, A., & Barton, A. (2017). Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach. Atmospheric Research, 188, 20–29. https://doi.org/10.1016/j.atmosres.2017.01.003.
Bahadur, R., Jaiswal, R. K., Nema, A. K., Gangwar, A., & Kumar, S. (2020). Trends Analysis of Rainfall and Temperature over Nagwan Watershed, Hazaribagh District, Jharkhand. Current Journal of Applied Science and Technology, 112–128. https://doi.org/10.9734/cjast/2020/v39i1930798.
Balan, S., M., Selvan, J. P., Bisht, H. R., Gadjil, Y. A., Khaladkar, I. R., & Lomte, V. M. (2019). Rainfall Prediction using Deep Learning on Highly Non-Linear Data. International Journal of Research in Engineering, Science and Management.
Barrera-Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. https://doi.org/10.1016/j.mlwa.2021.100204.
Bauer, D. F. (1972). Constructing Confidence Sets Using Rank Statistics. Journal of the American Statistical Association, 67(339), 687–690. https://doi.org/10.2307/2284469.
Bhardwaj, K., Shah, D., Aadhar, S., & Mishra, V. (2020). Propagation of Meteorological to Hydrological Droughts in India. Journal of Geophysical Research: Atmospheres, 125(22), e2020JD033455. https://doi.org/10.1029/2020JD033455.
Bhatt, D., & Mall, R. K. (2015). Surface Water Resources, Climate Change and Simulation Modeling. Aquatic Procedia, 4, 730–738. https://doi.org/10.1016/j.aqpro.2015.02.094.
Biemans, H., Siderius, C., Lutz, A. F., Nepal, S., Ahmad, B., Hassan, T., von Bloh, W., Wijngaard, R. R., Wester, P., Shrestha, A. B., & Immerzeel, W. W. (2019). Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nature Sustainability, 2(7), Article 7. https://doi.org/10.1038/s41893-019-0305-3.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1.
Box George E.P., J. G. M., & Reincel G.C., L. G. M. (n.d.). Time Series Analysis: Forecasting and Control, 5th Edition | Wiley. Wiley.Com.
Casado-Vara, R., Martin del Rey, A., Pérez-Palau, D., de-la-Fuente-Valentín, L., & Corchado, J. M. (2021). Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training. Mathematics, 9(4). https://doi.org/10.3390/math9040421.
Government of India. (2022). Census of India. Retrieved October 25, 2022, from https://censusindia.gov.in/census.website/data/census-tables.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014.
Chang, Y., Xiao, J., Li, X., Middel, A., Zhang, Y., Gu, Z., Wu, Y., & He, S. (2021). Exploring diurnal thermal variations in urban local climate zones with ECOSTRESS land surface temperature data. Remote Sensing of Environment, 263, 112544. https://doi.org/10.1016/j.rse.2021.112544.
Chao, Z., Pu, F., Yin, Y., Han, B., & Chen, X. (2018). Research on Real-Time Local Rainfall Prediction Based on MEMS Sensors. Journal of Sensors, 2018, e6184713. https://doi.org/10.1155/2018/6184713.
Chaturvedi, A. (2015). Rainfall Prediction using Back-Propagation Feed Forward Network. International Journal of Computer Applications, 119(4), 1–5.
Cosgrove, W. J., & Loucks, D. P. (2015). Water management: Current and future challenges and research directions. Water Resources Research, 51(6), 4823–4839. https://doi.org/10.1002/2014WR016869.
Dang, Q.-V. (2020). Understanding the Decision of Machine Learning Based Intrusion Detection Systems. In T. K. Dang, J. Küng, M. Takizawa, & T. M. Chung (Eds.), Future Data and Security Engineering (pp. 379–396). Springer International Publishing. https://doi.org/10.1007/978-3-030-63924-2_22.
Das, P., Maya, K., & Padmalal, D. (2022). Hydrogeochemistry of the Indian thermal springs: Current status. Earth-Science Reviews, 224, 103890. https://doi.org/10.1016/j.earscirev.2021.103890.
Das, S. (2019). Four decades of water and sediment discharge records in Subarnarekha and Burhabalang basins: An approach towards trend analysis and abrupt change detection. Sustainable Water Resources Management, 5(4), 1665–1676. https://doi.org/10.1007/s40899-019-00326-1.
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771.
Doorn, N. (2021). Artificial intelligence in the water domain: Opportunities for responsible use. Science of The Total Environment, 755, 142561. https://doi.org/10.1016/j.scitotenv.2020.142561.
Dubache, G., Ogwang, B. A., Ongoma, V., & Towfiqul Islam, A. R. Md. (2019). The effect of Indian Ocean on Ethiopian seasonal rainfall. Meteorology and Atmospheric Physics, 131, 1753–1761. https://doi.org/10.1007/s00703-019-00667-8.
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773.
Ferrant, S., Selles, A., Le Page, M., Herrault, P.-A., Pelletier, C., Al-Bitar, A., Mermoz, S., Gascoin, S., Bouvet, A., Saqalli, M., Dewandel, B., Caballero, Y., Ahmed, S., Maréchal, J.-C., & Kerr, Y. (2017). Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India. Remote Sensing, 9(11). https://doi.org/10.3390/rs9111119.
Franses, P. H. (2016). A note on the Mean Absolute Scaled Error. International Journal of Forecasting, 32(1), 20–22. https://doi.org/10.1016/j.ijforecast.2015.03.008.
Gobiet, A., Kotlarski, S., Beniston, M., Heinrich, G., Rajczak, J., & Stoffel, M. (2014). 21st century climate change in the European Alps—A review. Science of The Total Environment, 493, 1138–1151. https://doi.org/10.1016/j.scitotenv.2013.07.050.
Granger, C. W. J., & Andersen, A. (1978). On the invertibility of time series models. Stochastic Processes and Their Applications, 8(1), 87–92. https://doi.org/10.1016/0304-4149(78)90069-8.
Grubbs, F. E. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1–21. https://doi.org/10.1080/00401706.1969.10490657.
Gupta, C. K., & Kumar, R. (2018). Rainfall variation and trend analysis of Garhwa district, Jharkhand: An assessment of spatial and seasonal variability. Journal of Pharmacognosy and Phytochemistry, 7(5), 2143–2145.
Hyndman, R. J., & Athanasopoulos, A. (2002). Forecasting: Principles and Practice (2nd ed). Retrieved October 23, 2022, from https://otexts.com/fpp2/.
India-WRIS. (2002). Retrieved October 25, 2022, from https://indiawris.gov.in/wris/#/rainfall.
Kayet, N., Pathak, K., Chakrabarty, A., Kumar, S., Chowdary, V. M., & Singh, C. P. (2022). Risk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region. Geocarto International, 37(11), 3091–3115. https://doi.org/10.1080/10106049.2020.1849413.
Kundu, S. K., & Mondal, T. K. (2019). Analysis of long-term rainfall trends and change point in West Bengal, India. Theoretical and Applied Climatology, 138(3), 1647–1666. https://doi.org/10.1007/s00704-019-02916-7.
Lee, D. K., In, J., & Lee, S. (2015). Standard deviation and standard error of the mean. Korean Journal of Anesthesiology, 68(3), 220–223. https://doi.org/10.4097/kjae.2015.68.3.220.
Li, H., Zhang, L., Zhou, X., & Huang, B. (2017). Cost-sensitive sequential three-way decision modeling using a deep neural network. International Journal of Approximate Reasoning, 85, 68–78. https://doi.org/10.1016/j.ijar.2017.03.008.
Liu, F., & Liu, R. (2019). China, the United States, and order transition in East Asia: An economy-security Nexus approach. The Pacific Review, 32(6), 972–995. https://doi.org/10.1080/09512748.2018.1526205.
Liu, Q., Zou, Y., Liu, X., & Linge, N. (2019). A survey on rainfall forecasting using artificial neural network. International Journal of Embedded Systems, 11(2), 240. https://doi.org/10.1504/IJES.2019.098300.
Livera, A. M. D. (2010). Automatic forecasting with a modified exponential smoothing state space framework. In Monash Econometrics and Business Statistics Working Papers (No. 10/10; Monash Econometrics and Business Statistics Working Papers). Monash University, Department of Econometrics and Business Statistics. https://ideas.repec.org/p/msh/ebswps/2010-10.html.
Mandal, A., & Ray, A. (2015). Petrological and geochemical studies of ultramafic–mafic rocks from the North Puruliya Shear Zone (eastern India). Journal of Earth System Science, 124(8), 1781–1799. https://doi.org/10.1007/s12040-015-0634-1.
Mehdiyev, N., & Fettke, P. (2021). Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring. In W. Pedrycz & S.-M. Chen (Eds.), Interpretable Artificial Intelligence: A Perspective of Granular Computing (pp. 1–28). Springer International Publishing. https://doi.org/10.1007/978-3-030-64949-4_1.
Menon, A., Levermann, A., Schewe, J., Lehmann, J., & Frieler, K. (2013). Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth System Dynamics, 4(2), 287–300. https://doi.org/10.5194/esd-4-287-2013.
Mie Sein, Z. M., Islam, A. R. M. T., Maw, K. W., & Moya, T. B. (2015). Characterization of southwest monsoon onset over Myanmar. Meteorology and Atmospheric Physics, 127(5), 587–603. https://doi.org/10.1007/s00703-015-0386-0.
Mohapatra, M., Srivastava, A. K., Balachandran, S., & Geetha, B. (2017). Inter-annual Variation and Trends in Tropical Cyclones and Monsoon Depressions Over the North Indian Ocean. In M. N. Rajeevan & S. Nayak (Eds.), Observed Climate Variability and Change over the Indian Region (pp. 89–106). Springer. https://doi.org/10.1007/978-981-10-2531-0_6.
Mohd, R., Butt, M. A., & Baba, M. Z. (2020). GWLM–NARX: Grey Wolf Levenberg–Marquardt-based neural network for rainfall prediction. Data Technologies and Applications, 54(1), 85–102. https://doi.org/10.1108/DTA-08-2019-0130.
Mora, C., Spirandelli, D., Franklin, E. C., Lynham, J., Kantar, M. B., Miles, W., Smith, C. Z., Freel, K., Moy, J., Louis, L. V., Barba, E. W., Bettinger, K., Frazier, A. G., Colburn IX, J. F., Hanasaki, N., Hawkins, E., Hirabayashi, Y., Knorr, W., Little, C. M., … Hunter, C. L. (2018). Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions. Nature Climate Change, 8(12), Article 12. https://doi.org/10.1038/s41558-018-0315-6.
Mukaka, M. M. (2012). A guide to appropriate use of Correlation coefficient in medical research. Malawi Medical Journal, 24(3). https://doi.org/10.4314/mmj.v24i3.
Mukherjee, S., Mishra, A., & Trenberth, K. E. (2018). Climate Change and Drought: A Perspective on Drought Indices. Current Climate Change Reports, 4(2), 145–163. https://doi.org/10.1007/s40641-018-0098-x.
Myles, H., Wolfe, Douglas. A., & Checksen, E. (n.d.). Nonparametric Statistical Methods, 3rd Edition | Wiley. Wiley.Com.
Nourani, V., Uzelaltinbulat, S., Sadikoglu, F., & Behfar, N. (2019). Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation. Atmosphere, 10(2). https://doi.org/10.3390/atmos10020080.
Prakash, V., & Mishra, V. (2022). Soil Moisture and Streamflow Data Assimilation for Streamflow Prediction in the Narmada River Basin. Journal of Hydrometeorology, 1(aop). https://doi.org/10.1175/JHM-D-21-0139.1.
Raha, S., & Gayen, S. K. (2020). Simulation of meteorological drought using exponential smoothing models: A study on Bankura District, West Bengal, India. SN Applied Sciences, 2(5), 909. https://doi.org/10.1007/s42452-020-2730-3.
Rahman, Md. R., & Lateh, H. (2017). Climate change in Bangladesh: A spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theoretical and Applied Climatology, 128(1), 27–41. https://doi.org/10.1007/s00704-015-1688-3.
Saha, A., Singh, K. N., Ray, M., & Rathod, S. (2020). A hybrid spatio-temporal modelling: An application to space-time rainfall forecasting. Theoretical and Applied Climatology, 142(3), 1271–1282. https://doi.org/10.1007/s00704-020-03374-2.
Samantaray, S., Tripathy, O., Sahoo, A., & Ghose, D. K. (2020). Rainfall Forecasting Through ANN and SVM in Bolangir Watershed, India. In S. C. Satapathy, V. Bhateja, J. R. Mohanty, & S. K. Udgata (Eds.), Smart Intelligent Computing and Applications (pp. 767–774). Springer. https://doi.org/10.1007/978-981-13-9282-5_74.
Scheuerer, M. (2014). Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics. Quarterly Journal of the Royal Meteorological Society, 140(680), 1086–1096. https://doi.org/10.1002/qj.2183.
Shah, N. V., Patel, Y. S., & DBhangaonkar, P. (2021). Assessing Impact of Climate Change on Rainfall Patterns of Vadodara District, Gujarat, India. Journal of Physics: Conference Series, 1714(1), 012046. https://doi.org/10.1088/1742-6596/1714/1/012046.
Sharma, T., & Singh, S. (2021). Feto-maternal outcome in previous one cesarean section: A retrospective observational study at a district hospital of Jharkhand. International Journal of Reproduction, Contraception, Obstetrics and Gynecology, 10(10), 3834–3840.
Shmuel, A., Ziv, Y., & Heifetz, E. (2022). Machine-Learning-based evaluation of the time-lagged effect of meteorological factors on 10-hour dead fuel moisture content. Forest Ecology and Management, 505, 119897. https://doi.org/10.1016/j.foreco.2021.119897.
Shree, S., & Kumar, M. (2018). Analysis of seasonal and annual rainfall trends for Ranchi district, Jharkhand, India. Environmental Earth Sciences, 77(19), 693. https://doi.org/10.1007/s12665-018-7884-6.
Silva, L. da. (2021). Modelagem geométrica do par roda-trilho com descrição por meio de NURBS. [Text, Universidade de São Paulo]. https://doi.org/10.11606/D.3.2021.tde-25102021-145823.
Singh, D., Ghosh, S., Roxy, M. K., & McDermid, S. (2019). Indian summer monsoon: Extreme events, historical changes, and role of anthropogenic forcings. WIREs Climate Change, 10(2), e571. https://doi.org/10.1002/wcc.571.
Stefansky, W. (2012). Rejecting Outliers in Factorial Designs. Technometrics: 14(2). https://www.tandfonline.com/doi/abs/10.1080/00401706.1972.10488930.
Tirkey, A. S., Ghosh, M., Pandey, A. C., & Shekhar, S. (2018). Assessment of climate extremes and its long term spatial variability over the Jharkhand state of India. The Egyptian Journal of Remote Sensing and Space Science, 21(1), 49–63. https://doi.org/10.1016/j.ejrs.2016.12.007.
Tong, H., & Lim, K. S. (1980). Threshold Autoregression, Limit Cycles and Cyclical Data. Journal of the Royal Statistical Society. Series B (Methodological), 42(3), 245–292.
Upadhayay, U., Kumar, N., Kumar, R., & Kumari, P. (2019). Hydro-Geological Status of the Core and Buffer Zone of Beekay Steel Industries Limited, Adityapur Industrial Area, Saraikela, Kharsawan, Jharkhand. In Wastewater Reuse and Watershed Management. Apple Academic Press.
Urbanczyk-Wochniak, E., Luedemann, A., Kopka, J., & Selbig, J. (n.d.). Parallel analysis of transcript and metabolic profiles: A new approach in systems biology | EMBO reports. Retrieved October 25, 2022, from https://www.embopress.org/doi/full/10.1038/sj.embor.embor944.
Vuyyuru, V. Ankalu., Apparao, Giduturi., & Anuradha, S. (2021). Rainfall Prediction Using Machine Learning Based Ensemble Model. 2021 5th International Conference on Information Systems and Computer Networks (ISCON), 1–4. https://doi.org/10.1109/ISCON52037.2021.9702409.
Wang, W., & Lu, Y. (2018). Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model. IOP Conference Series: Materials Science and Engineering, 324(1), 012049. https://doi.org/10.1088/1757-899X/324/1/012049.
Wang, Y., Yuan, Z., Liu, H., Xing, Z., Ji, Y., Li, H., Fu, Q., & Mo, C. (2022). A new scheme for probabilistic forecasting with an ensemble model based on CEEMDAN and AM-MCMC and its application in precipitation forecasting. Expert Systems with Applications, 187, 115872. https://doi.org/10.1016/j.eswa.2021.115872.
Xiang, Y., Gou, L., He, L., Xia, S., & Wang, W. (2018). A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874–883. https://doi.org/10.1016/j.asoc.2018.09.018.
Xu, C., Liao, Z., Li, C., Zhou, X., & Xie, R. (2022). Review on Interpretable Machine Learning in Smart Grid. Energies, 15(12). https://doi.org/10.3390/en15124427.
Yadav, M. (2022). South Asian Monsoon Extremes and Climate Change. In P. Saxena, A. Shukla, & A. K. Gupta (Eds.), Extremes in Atmospheric Processes and Phenomenon: Assessment, Impacts and Mitigation (pp. 59–86). Springer Nature. https://doi.org/10.1007/978-981-16-7727-4_4.
Yaseen, Z. M., Ali, M., Sharafati, A., Al-Ansari, N., & Shahid, S. (2021). Forecasting standardized precipitation index using data intelligence models: Regional investigation of Bangladesh. Scientific Reports, 11(1), Article 1. https://doi.org/10.1038/s41598-021-82977-9.
Zamani, R., Mirabbasi, R., Nazeri, M., Meshram, S. G., & Ahmadi, F. (2018). Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand state, India. Stochastic Environmental Research and Risk Assessment, 32(4), 1085–1097. https://doi.org/10.1007/s00477-017-1447-3.
Zhang, T., Yang, S., Jiang, X., & Zhao, P. (2016). Seasonal–Interannual Variation and Prediction of Wet and Dry Season Rainfall over the Maritime Continent: Roles of ENSO and Monsoon Circulation. Journal of Climate, 29(10), 3675–3695. https://doi.org/10.1175/JCLI-D-15-0222.1.
Published
2022-12-24
How to Cite
RAHA, Shrinwantu; GAYEN, Shasanka Kumar.
Simulation of Rainfall Using Two Statistical Data Driven Models: A Study on Santhal Pargana Division of Jharkhand State, India.
Geosfera Indonesia, [S.l.], v. 7, n. 3, p. 236-263, dec. 2022.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/34487>. Date accessed: 24 nov. 2024.
doi: https://doi.org/10.19184/geosi.v7i3.34487.
Section
Original Research Articles
