Comparative Study of Statistical and Machine Learning Techniques for Fish Production Forecasting in Andhra Pradesh under Climate Change Scenario

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.