Integrated Learning Paradigm for Ecological Predictive Modeling

Abstract

This study introduces the Integrated Learning Paradigm (ILP), a novel construct designed to improve the predictive capability of the biodiversity impact assessment model. The ILP brings together Support Vector Regression (SVR), Linear Regression, and Random Forest in a single model so that each of these algorithms can be used to its fullest. The intent behind this integration is to enhance the battle against overfitting within the model improving mainly R² and Root Mean Square Error (RMSE). Our study systematically evaluates the ILP relative to standalone models using ecological data datasets. The findings are rather surprising: the ILP specializes in R² which correlates with the amount of prediction error and fluctuations with standard deviation which create an RMSE of 688.2 which greatly surpasses that of SVR, Linear Regression and Random Forest. It implies a figure that explains the data more appropriately while making prediction errors in magnitude that is lower than anticipated. Supporting evidence is supplied through detailed visual analyses on the ILP, including residual and overlapped histogram plots, which showed differences in ILP’s performance on consistency and reliability of prediction. As these analyses indicate, the ILP is highly promising for the analysis of ecological data. In the end, the exceptional level of effective incidence of the ILP on the other hand, dimension, focuses more on the needs of the conservation methods and sustainable development processes in relation to policy provision and management strategies for the ecological system.