Abstract
Chilika, a Ramsar site and the largest brackish water lagoon in Asia, is situated in East Coast of India, endowed with rich fisheries resources. In this study, SARIMAX fisheries forecasting model was developed by using seasonal ARIMA (Auto Regressive Integrated Moving Average) model with three external physicochemical factors (factor 1 was dominated by the combined effect of salinity and temperature and factor 2 and factor 3 were dominated by alkalinity and transparency) in Chilika. Monthly fish catch data and physico-chemical parameters of water from 2001–2002 to 2015–2016 was used to develop model. The results showed SARIMAX model; SARIMA (1,0,0)(2,0,0)12 with factor 1, factor 2 and factor 3 was the best fitted model for the fish catch in Chilika. The factor 1 was found to be positive influence on catch at 10% level of significance (p = 0.089) while, factor 2 and factor 3 were found to be insignificant. The developed SARIMAX model was validated with actual annual fish catch for the years 2011–2015 with prediction error 3–7%. Further, the developed SARIMAX model was used to forecast fish catch for the period April 2016 to March 2018 indicating increasing 10% present catch in the lagoon. The developed SARIMAX model in the present case study is of the first time to forecast and visualise the positive influence of salinity and temperature on the fish catch in the Chilika lagoon.
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References
Adhikari R, Agrawal RK (2013) An introductory study on time series modeling and forecasting. Preprint arXiv:1302.6613
Agrawal A (2011) A new approach to spatio temporal kriging and its application. Thesis master of science. Graduate School of the Ohio State University, p 112
Akaike H (1972) Use of an information theoretic quantity for statistical model identification. In Proceedings of the 5th Hawaii International Conference on System Sciences, pp 249–250
Albertson K, Aylen J (1996) Modelling the great lakes freeze: forecasting and seasonality in the market for ferrous scrap. Int J Forecast 12(3):345–359. https://doi.org/10.1016/0169-2070(96)00669-3
Ali G (2015) Cointegration VAR and VECM and ARIMAX Econometric Approaches for Water Quality Variates. J Stat Econom Methods 4(1):1–38
Andrews B, Dean M, Swain R, Cole C (2013) Building ARIMA and Arimax models for predicting long-term disability benefit application rates in the public/private sectors, Society of Actuaries
Bernard P, Lhote A, Legube B (2004) Principal component analysis: an appropriate tool for water quality evaluation and management—application to a tropical lake system. Ecol Model 178:295–311
Biradar RS (1988) Fisheries statistics Course manual No-14. Central Institute of Fisheries Education (ICAR), Bombay 229
Blaber SJ (1997) Fish and fisheries in tropical estuaries, vol 22. Springer Science & Business Media
Blaber SJM (2000) Tropical estuarine fishes: ecology, exploitation and conservation. Blackwell Science, Oxford, p 372
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco
Bruce HA, Dean MD, Swain R, Cole C (2013) Building ARIMA and ARIMAX models for predicting long-term disability benefit application rates in the public/private sectors, University of Southern Maine. http://www.afriheritage.org/TTT/4. Building ARIMA and ARIMAX Model.pdf
Cyrus DP, McLean S (1996) Water temperature and the 1987 fish kill at Lake St Lucia on the South Eastern coast of Africa. S Afr J Aquat Sci 22:105–110
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431
Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072
Gibson RN (1982) Recent studies on the biology of intertidal fishes. Oceanogr Mar Biol Annu Rev 7:367–414
Gupta RA, Mandal SK, Paul S (1991). Methodology for collection and estimation of inland fisheries statistics in India. Central Inland Capture Fisheries Research Institute (ICAR), Barrackpore West Bengal Bull. No. 58 (Revised): 64
Hassanzadeh S, Hosseinibalam F, Alizadeh R (2009) Statistical models and time series forecasting of sulfur dioxide: a case study Tehran. Environ Monit Assess 155(1):149–155
Jhingran VG, Natarajan AV (1969) Study of the fishery and fish populations of the Chilika lake during the period 1957–65. J Inland Fish Soc India 1:47–126
Jolliffe I (2002) Principal component analysis. Wiley, Charlottesville
Lima ARA, Costa MF, Barletta M (2014) Distribution patterns of microplastic within the plankton of a tropical estuary. Environ Res 132:146–155
Ljung GM, Box GEP (1978) On a measure of lack of fit in time series models. Biometrika 65:297–303
Makwinja R, Phiri T, Kosamu IB, Kaonga CC (2017) Application of stochastic models in predicting Lake Malawi water levels. Int J Water Resour Environ Eng 9(9):191–200
Marshall S, Elliott M (1998) Environmental influences on the fish assemblage of the Humber Estuary, U.K. Estuar Coast Shelf Sci 46:175–184
Mathier L, Fagherazzi L, Rassam JC, Bobée B (1992) Great lakes net basin supply simulation by a stochastic approach. No. R362, INRS-Eau
Mohanty SK, Mishra SS, Khan M, Mohanty RK, Mohapatra A, Pattnaik AK (2015) Ichthyofaunal diversity of Chilika Lake, Odisha, India: an inventory, assessment of biodiversity status and comprehensive systematic checklist (1916–2014). Check List 11(6):1–19
Mohapatra A, Mohanty SK, Mishra SS (2015) Fish and shellfish fauna of Chilika lagoon: an updated checklist. In: Venkataraman K, Sivaperuman C (eds) Marine faunal diversity in India. Elsvier Publication, New York, pp 195–224
Noble A, Sathianandan TV (1991) Trend analysis in all India mackerel catches using ARIMA models. Indian J Fish 38(2):119–122
Noell C, Ye Q (2013) An investigation into the relationship between freshwater flow and production of key species in the South Australian Lakes and Coorong Fishery. South Australian Aquatic Sciences Centre, West Beach
Pajuelo JG, Lorenzo JM (1995) Analysis and forecasting of the demersal fishery of the Canary Islands using an ARIMA model. Sci Mar 59:155–164
Plisnier, PD, Poncelet N, Cocquyt C, De BH, Bompangue D, Naithani J, Jacobs J, Piarroux R, Moore S, Giraudoux P, Batumbo D (2015) Cholera outbreaks at Lake Tanganyika induced by climate change?. No. UCL-Université Catholique de Louvain
Prista N, Diawara N, Costa MJ, Jones C (2011) Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery of Portugal. Fish Bull 109:170–185
Romilly P (2005) Time series modelling of global mean temperature for managerial decision-making. J Environ Manag 76:61–70
Roy M (1981) Using Box–Jenkins models to forecast fishery dynamics: identification, estimation and checking. Fish Bull 78(4):887–896
Saila SB, Wighbout M, Lermit RJ (1980) Comparison of some time series models for the analysis of fisheries data. J Conseil 39:44–52
Satapathy D, Panda S (2009) Fish atlas of Chilika. Chilika Development Authority, Bhubaneswar
Sathianandan TV, Srinath M (1995) Time series analysis of marine fish landings in India. Mar Biol Assess 37:171–178
Schwartz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464
Solari A, Jaureguizar AJ, Milessi AC, García ML (2015) Fish assemblages in a small temperate estuary on the Argentinian coast: spatial variation, environmental influence and relevance as nursery area. Braz J Oceanogr 63(3):181–194
Stergiou KI (1991) Describing and forecasting the sardine-anchovy complex in the eastern Mediterranean using vector autoregressions. Fish Res 11:127–141
Stergiou KI, Chritou ED, Petrakis G (1997) Modelling and forecasting monthly fisheries catches: comparison of regression, univariate and multivariate time series methods. Fish Res 29:55–95
Sun H, Koch M (2001) Case study: analysis and forecasting of salinity in Apalachicola Bay, Florida, using Box-Jenkins ARIMA models. J Hydraul Eng 127(9):718–727
Whitfield AK (1999) Ichthyofaunal assemblages in estuaries: a South African case study. Rev Fish Biol Fish 9:151–186
Yidana SM, Ophori D, Banoeng-Yakubo B (2008) A multivariate statistical analysis of surface water chemistry data-The Ankobra Basin, Ghana. J Environ Manag 86:80–87
Acknowledgements
The data provided by the Chilika Development Authority (CDA) and the technical expertise received from Dr. K.K. Goswami, Retd. Principal Scientist, ICAR-CIFRI, Barrackpore to the first author at the initial stage of time series modeling are acknowledged.
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No funding. However, the research analysis was a part of Institutional (ICAR-CIFRI, Barrackpore, 700120, INDIA) activity.
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Raman, R.K., Mohanty, S.K., Bhatta, K.S. et al. Time series forecasting model for fisheries in Chilika lagoon (a Ramsar site, 1981), Odisha, India: a case study. Wetlands Ecol Manage 26, 677–687 (2018). https://doi.org/10.1007/s11273-018-9600-4
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DOI: https://doi.org/10.1007/s11273-018-9600-4