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Comparative study of statistical and machine learning techniques for fish production forecasting in Andhra Pradesh under climate change scenario

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Title Comparative study of statistical and machine learning techniques for fish production forecasting in Andhra Pradesh under climate change scenario
 
Creator Stephen, S K
Yadav, V K
Kumar, R R
 
Subject ARIMAX
EMD-ANN
Climate change
Marine fish production
NARX
 
Description 776-784
The present study emphasizes the forecast of Andhra Pradesh's total marine fish production and the catch of
commercially important fishes, viz., Indian Mackerel, Oil Sardine, Horse Mackerel, and Lesser Sardines for the next 5 years
by different statistical and machine learning approaches under climate change scenario. Forecasting is done with and
without the inclusion of climatic and environmental parameters in different models. Exogenous variables, i.e., climatic
parameters such as Sea Surface Temperature (SST), wind speed, and environmental parameters such as Chlorophyll-a,
diffusion attenuation coefficient, and Photo-synthetically Active Radiation (PAR), were used in the model. The following
models like Non-linear Autoregressive (NAR) Artificial Neural Network (ANN) (NNAR-ANN), Auto-Regressive
Integrated Moving Average (ARIMA), Empirical Mode Decomposition based Artificial Neural Network (EMD-ANN), are
used to predict the fish catch data using time series quarterly catch data of commercially important fishes and total fish catch
without the inclusion of climatic and environmental variables. Auto Regressive Integrated Moving Average method with
inclusion of exogenous variables (ARIMAX) and Non-Linear Auto Regression with exogenous variables (NARX) models
were used to forecast along with quarterly average data of environmental and climatic variables. The model developed
predicts the total fish catch and also the catch of commercially important fish for the next 20 quarters. The developed model
forecasts are compared based on the error measure, i.e., MAPE (Mean Absolute Percentage Error), and the results showed
that the NARX model outperformed other models like ARIMAX, ARIMA, NNAR-ANN, and EMD-ANN. Implementation
of management strategies considering the impact of climate change on fisheries will enhance sustainable fisheries and pave
a pathway for the mitigation of climate change.
 
Date 2023-06-07T09:10:08Z
2023-06-07T09:10:08Z
2023-06
 
Type Article
 
Identifier 2582-6727 (Online); 2582-6506 (Print)
http://nopr.niscpr.res.in/handle/123456789/62018
https://doi.org/10.56042/ijms.v51i09.2337
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source IJMS Vol.51(09) [September 2022]