1. Introduction

Coastal lagoon is a junction of fresh, brackish, and marine water system and known for its large fish species production and diversity among aquatic ecosystem [1, 2]. The fresh and saline water combination makes it more liable for attracting different finfish and shellfish species in the aquatic ecosystem. The Chilika lake (lagoon) (a Ramsar site of International importance) is the largest brackish water lagoon in the eastern coast of India situated between latitude 19°20<sup>0</sup> 13.06″–19° 54<sup>0</sup> 47.02″ N to longitude 85°06<sup>0</sup> 49.15″–85°35<sup>0</sup> 32.87″ E in the humid tropical climatic zone in the state of Odisha, India (Figure 1). The lagoon is composed of fresh, brackish and marine water ecosystems and divided into outer channel, central sector, northern sector and southern sector. The lagoon is a rich source of fisheries production and biodiversity, which harbours more than 29 species of shrimp from 8 families [3], providing livelihood around 0.2 million people around it. Fisheries resources in Chilika lake account more than 71% total valuation of the lake ecosystem [4]. The lake has been supporting around 200 million rupees to the state

economy and contributing to the earning of valuable foreign exchange. However, continued natural changes and unabated anthropogenic pressure fisheries suffered the most in terms of both yield and biodiversity in the last two decades [5]. Before restoration (opening of the new lake mouth, year 2000–2001) of the lagoon, the Penaeid prawns' population decreases due to the failure in breeding as the lake mouth was shifted far (about 30 Km) from the lake proper, and the confluence point of outer channel (recruitment route) at Magarmukh was silt-choked [5]. Average fisheries catch increased around 520% after four years of new lake mouth opening in 2000. Northern sector was found to have maximum catch percentage of total catch followed by central sector, southern sector and outer channel. The commercial fish catch of the lagoon is composed of 12 fish groups namely mullets, clupeoides, perches, threadfins, crockers, beloniformes, catfishes, tripod fishes, cichlids, murrels, featherbacks and others. After the opening of new lake mouth and desiltation of outer channel, the salinity of the lake increases due to high intake of sea water, resulting favourable for effective recruitment of all economic fishes except cichlids. Shrimp production individually contributed 35% of total fish production in the Chilika lagoon. Some dominating shrimps are Fenneropenaeus indicus, Penaeus monodon, Metapenaeus dobsoni, Metapenaeus monoceros, etc. in the lagoon. The annual landings of shrimp species fluctuated between 2347.78 Metric Ton (MT) and 6413.78 (MT) during last 15 years (2001–2015) in this lagoon after the opening of new lake mouth. Quarter wise shrimp landings fluctuated between 82.55 (MT) and 745.55 (MT) during the period 2001–2015 in the lagoon. To support the prediction of shrimp catch landing in the lagoon, domain specialists need to develop the forecasting models over time series analysis. Time series data are a sequence of data which are collected at successive equally spaced time interval, and it depends on its past value. Time series analysis built stochastic models based on time correlations of collected data. 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 forecasts of the output variables of a dynamic system [6]. Very well-known Box Jenkin's [7] Auto Regressive Integrated Moving Average (ARIMA) model is used to analyse and forecast the univariate time series data. 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 [12–14]; 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

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

DOI: http://dx.doi.org/10.5772/intechopen.85458

When exogenous variable is included in the ARIMA model, it is known as ARIMAX model. Similarly, SARIMAX model is developed using well-known seasonal

Chilika Lagoon (Figure 1) water spread area fluctuates between monsoon and

The water quality of lagoon is influenced by the influx of sea water from West Bengal, Mahanadi distributaries and from the western catchment rivers. The lagoon is an assemblage of shallow to very shallow marine, brackish and freshwater ecosystems [15] characterised by the lagoon with marine, brackish and freshwater fisheries. Shrimp landing plays a significant contributor in commercial landings after the opening of new lagoon mouth as a part of hydrological intervention for

, respectively [15, 16].

dry season at maximum of 1165 km<sup>2</sup> to minimum of 906 km<sup>2</sup>

eco-restoration of the lake in September 2000.

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. The influence of water quality parameters of Chilika lake on monthly catch of commercial fisheries (Beloniformes: order) and total fisheries of the lake was modelled using SARIMAX modelling approach [13, 14]. In this case study, SARIMA model for modelling and forecasting the 15 years quarterly time series shrimp catch data of Chilika lagoon of post restoration period since 2001 was developed as case study and further using this model to forecast shrimp catch landings up to 2018. Very few studies have been conducted based on time series model based forecasting of fish species in general and shrimp in particular of Chilika lagoon. The aim of this study is to develop the best fitted time series forecasting model for shrimp landing in Chilika lagoon and further model based catch forecast up to the period 2018. Since catch prediction in advance would be necessary for appropriate planning and designing of the national fishery development plan for sustainable exploitation of fisheries of the said water bodies, this shrimp forecasting study in the Chilika lagoon will be immensely useful in decision making for the policy makers and lagoon managers for sustainable fisheries production and management. In this chapter, a case study of shrimp prediction model using quarterly time series data of the Chilika lagoon was developed, and further prediction model of fisheries and commercial fish (Beloniformes: order) in the Chilika lagoon was also discussed.

fish production in Odisha, India, using the SARIMA Model [12].

2. Materials and methods

2.1 Study area

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#### Figure 1.

Map of Chilika lagoon (lake) consisting four ecological sectors (outer channel, central sector, northern sector and southern sector) (Source: [14]).
