Time-series modeling of fishery landings using ARIMA models and Fuzzy Expected Intervals software

Seasonal patterns of three fish species in a Caribbean coastal gill-net fishery: Biologically induced or climate-related aggregations?

“…Among the various methods used to describe temporal fishery dynamics, ARIMA modelling has proven to be an important tool for revealing hidden trends and seasonal patterns in time series with high inter-annual variability, such as artisanal landings and catch per unit effort (CPUE) series (e.g., Farley and Murphy, 1997;Stergiou et al, 1997;Park, 1998;Lloret et al, 2000;BecerraMuñoz et al, 2003;Koutroumanidis et al, 2006;Czerwinski et al, 2007, González Herraiz et al, 2009. ARIMA models were basically intended to identify and compare seasonal signals and trends in the three time series.…”

Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

“…Time series analysis of fishery landings plays a vital role in fisheries management and decision making due to its capacity for demonstrating the trends and seasonality patterns of the data (Koutroumanidis et al, 2006;Tsitsika et al, 2007). In the fishery field, time series analysis qualifies for forecasting because it expresses past patterns and projects into the future (Stergiou et al, 1997).…”

Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

“…The main components of a fuzzy system (adapted from [142]). [141], modelling fishery landings [98], identifying ecological conditions in a lagoon [111], identifying oil spills from satellite images [93], modelling solute transport [48], controlling wastewater treatment [173], combining rainfall-runoff forecasts [169], predicting soil erosion [113], assessing environmental risk of drilling waste discharge [135], estimating fire risk [78], predicting disease risk [59] and predicting regional drought [128].…”