Optimizing Climate Impact Assessment: Sloth-Inspired Optimization with Ridge Regression for Socio-economic Policy Insights

Global socio-economic systems are facing serious problems as a result of climate change, making it necessary for the development of reliable approaches for evaluating its effects and guiding policy decisions. When processing complicated climatic and financial statistics, traditional strategies often have accuracy and interpretability problems. The goal of this research is to optimize the evaluation of climate impact with the aid of integrating the Sloth-inspired Optimization algorithm (SIOA) with Ridge Regression (RR). This technique provides a novel framework for optimizing selecting features and enhancing the reliability of predictions in socio-economic modelling. This integration seeks to address the shortcomings of traditional strategies by using enhancing model interpretability and performance in weather effect assessment. The financial outcomes of weather alternate are then expected by way of integrating a subset of characteristics right into a Ridge Regression version, with an emphasis on growing model interpretability and decreasing prediction mistakes. A climate observe dataset employed to the included architecture produced effective outcomes. Ridge Regression's prediction accuracy become stepped forward, and metrics for mean Squared error (MSE) and mean Absolute errors (MAE) were reduced, owing to SIOA's a success identification of vital weather variability. The potential to capture elaborate linkages amongst climatic factors and economic repercussions became shown to be superior when compared to traditional techniques of study. This paradigm is beneficial for making plans sustainable development and making knowledgeable decisions because it now not most effective increases prediction abilities but also fosters a higher expertise of the dynamic interplay between climatic factors and financial outcomes.