Time series forecasting model for fisheries in Chilika lagoon (a Ramsar site, 1981), Odisha, India: a case study

Raman, Rohan Kumar; Mohanty, S. K.; Bhatta, K. S.; Karna, S. K.; Sahoo, Amiya Kumar; Mohanty, Bimal Prasanna; Das, Basanta Kumar

doi:10.1007/s11273-018-9600-4

Cited by 12 publications

(14 citation statements)

References 21 publications

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“…The stochastic model of the fish catch of Chilika lagoon has already been done by SARIMAX model with < 10% errors and predicted rise in fish catch in the forthcoming years under the present lagoon status Raman, et al . [35].…”

Section: Ecosystem Diversity Of Chilikamentioning

confidence: 99%

“…The Hydraulic interventions dams, cuts and barrages like Hirakud, Naraj Barrage, Gabakund has with anthropologic activities within the lagoon like gherries have changed the hydrologic regime obstructing free flow. From 1985-86 to 2018-19 the Aqua catch statistics of the Chilika reveals that the average annual production is 9155.38MT constituting of 67.28% fish species, 35.15% of different prawn categories, and 1.6% are the different crabs. There were two major interventions were made in the hydraulic system i.e.…”

Section: Impact Of Major Interventionsmentioning

confidence: 99%

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Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Mishra

Ojha

2021

ARRB

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Introduction: The Chilika lagoon in south Odisha, India was ecologically degraded from 1985 onwards by reduction of its aquatic (fish + prawn + shrimp) catches along with reduction in salinity, hydraulic regime, water exchange, aquatic weeds invasion, and sediment influx. The aquatic catch was 8669MT in year 1985-1986 gradually reduced to 1274MT during 1995-1996 from Odisha Fisheries Dept. records which resulted in poor economic condition of ≈0,2million fishermen and they migrated to adopt other livelihood. One direct tidal inlet dredged (Sipakuda) and Naraj barrage in the apex of South Mahanadi Delta were the major hydraulic interventions made to regain hydraulic regime. After the hydraulic interventions, the eco system restored, and the aquatic catch surged but it was insufficient to livelihood sustenance for the fishermen community of the Chilika, so that they are forced for alternate occupation and migration. Methodologies: Fish catch data collected for 30 years and soft computing models linear regression, Multi Linear Perception (ANN), SMOorg (SVM) and the Random Forest algorithms (Weka Software) are used to predict the fish catch data of the lagoon for coming decade from 2020 to 2030. The effects of major hydraulic interventions are analyzed and the soft computing method of the fish and shrimp catch prediction of the Chilika has been attempted for the first time except some statistical approaches. Results: The Random Forest is found to be the preferred algorithm followed by the MLP model. The amount of catch remained around 12-13TMT if the variables and the present status of the lagoon is maintained. The combined effect of the Sipakuda Tidal inlet and the effective operation of the Naraj barrage have maintained the sustainable aqua catch. The present study shall be an immense help for the lake users and policy makers to augment aquatic catch, and alternate livelihood fishers community of the Chilika lagoon.

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Section: Ecosystem Diversity Of Chilikamentioning

confidence: 99%

Section: Impact Of Major Interventionsmentioning

confidence: 99%

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Mishra

Ojha

2021

ARRB

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“…The main objective of time series analysis in respect to the fishery fields is to describe the underlying structure using input data to provide short-term Figure 1. Map of Chilika lagoon (lake) consisting four ecological sectors (outer channel, central sector, northern sector and southern sector) (Source: [14]).…”

Section: Introductionmentioning

confidence: 99%

“…SARIMA model is composed of ARIMA model including seasonal component of the time series data and very frequently used for time series modelling. Applications of ARIMA and SARIMA models were used to analyse and forecast fisheries data [12][13][14]; illustration on catch and effort data for the skipjack tuna fleet at Hawaii [8], Saila [9] modelled on monthly average catch per day of rock lobster, Sathianandan and Srinath [10] modelled of all India marine fish landings using ARIMA model, while NunoPrista et al [11] described the application of SARIMA models for data-poor fisheries with a case study on sciaenid fishery of Portugal. Modelling and forecasting of marine fish production in Odisha, India, using the SARIMA Model [12].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, SARIMAX model is developed using well-known seasonal ARIMA model with external regressors associated with the time series data. SARIMAX model performs with better accuracy in fish catch prediction than the SARIMA model with minimum error variance [14]. However, the environmental factors' influence as a regressor in the time series ARIMA (SARIMA) model could be quantified by identifying the underlying patterns in periodic time series data using ARIMAX (SARIMAX) model.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Shrimp and Fish Catch in Chilika Lake over Time Series Analysis

Raman¹,

Das²

2019

Time Series Analysis - Data, Methods, and Applications

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Chilika lagoon (a Ramsar site) is a large source of fish production and biodiversity situated in the east coast of India, Odisha. Shrimp landings contribute an average of 4185 MT (Metric Ton) around 35% of total fish production. In this study, SARIMA (Seasonal Auto Regressive Integrated Moving Average) model has been developed on quarterly time series shrimp catch data during the year 2001-2015 and forecasted up to 2018. The best model was selected on Akaike Information Criteria (AIC) and Bayesian Information Criterion (SBC). Results showed that maximum average shrimp landings were observed in the first quarter period (summer season), whereas maximum variation in catch was observed during second quarter Q2 (monsoon season) and lowest variation in the fourth quarter Q4 (winter season) catch during the year 2001-2015. The developed time series SARIMA (0,1,1)(0,1,1) 4 model was found to be the best fitted model for the shrimp landings in the lagoon. This article also delineates the application of SARIMAX model (SARIMA with regressors) using monthly catch prediction of fisheries in the Chilika Lake. The developed model is validated with less than 10% errors showing increasing fish catch in the upcoming years by maintaining the present lake condition.

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An ecological‐economic fishery model: Maximizing the societal benefit through an integrated approach of fishing and ecotourism

Sarkar

Bhattacharya

Bairagi

2023

Math Methods in App Sciences

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One of the essential tasks of the modern fishery is to integrate fishery‐based tourism with it as a part of sustainable development. Here, we propose and study a two‐species fishery model where the ecological and economic concepts are integrated. The objectives are to demonstrate the effect of fishing tax and tourist entrance fees to stabilize fishery dynamics and maximize revenue generation. For this, we consider a predator–prey fishery model coupled with dynamic harvesting and time‐dependent fish price variation. The prey fish is commercially harvested, and the predatory fish is used for recreation purposes of the visitors with an entry fee. A fishing tax is levied on the fishers to restrict the overfishing of this renewable resource. We provide the local and global stability conditions of different equilibrium points of the system and unveil the broader dynamics through bifurcation analysis. Pontryagin's maximum principle shows the existence of an optimal fishing tax that maximizes overall revenue generation.

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Time series forecasting model for fisheries in Chilika lagoon (a Ramsar site, 1981), Odisha, India: a case study

Cited by 12 publications

References 21 publications

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Prediction by Soft Computing, Planning, and Strategy Building of Aquatic Catch: Chilika Lagoon, Odisha, India

Forecasting Shrimp and Fish Catch in Chilika Lake over Time Series Analysis

An ecological‐economic fishery model: Maximizing the societal benefit through an integrated approach of fishing and ecotourism

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