An intelligent framework for prediction and forecasting of dissolved oxygen level and biofloc amount in a shrimp culture system using machine learning techniques

Abstract

The present study approaches towards the feasibility of prediction and forecasting of dissolved oxygen (DO) and biofloc amount using the state of art machine learning algorithms in a shrimp culture system. The study was carried out considering; average DO and biofloc amount as target parameters in a shrimp culture system. There were seventeen numbers of culture and meteorological parameters considered and three different feature selection techniques used to create twelve different data subsets for model development. The model development was carried out using three popular machine learning algorithms viz., Random Forest, Adaboost and Deep neural network. The totals of thirty-six different models were obtained and their accuracies were evaluated with seven model validation tests and results were obtained and discussed. Out of thirty-six models, Random Forest technique applied model for prediction of dissolved oxygen with combined culture and meteorological parameters (R2value – 0.709, prediction accuracy – 98.26%, score – 0.7381) was found to be the best one for predictive model development. Moreover, exploratory data analysis was carried out for prediction and a framework for prediction of DO and biofloc amount in a shrimp based biofloc culture system was developed. The dissolved oxygen was found to be more robust in the predictive model development. The Intelligent Framework was developed based on the study conducted to understand and carryout prediction in farming system in a scientific manner. The developed framework can help the literate farmers and new entrants of shrimp farming to devise their own prediction models suitable for the farming conditions.