Predictive Analytics For Groundwater Resources Using SVM And Deep Radial Basis Approaches In Smart City Planning For Feature Extraction And Prediction

The growth of smart cities is happening at the same time that there is an increasing demand for water resources. Controlling groundwater in a responsible manner is absolutely necessary in order to ensure sustainability. When it comes to groundwater forecast, traditional methods are frequently wrong, which in turn leads to inefficient use of resources and bad planning. Specifically, the research employs a two technique, which combines Support Vector Machines (SVMs) for efficient feature extraction with DRB Approaches (DRBAs) for accurate prediction. SVM is excellent at extracting significant properties from complicated datasets, whereas DRB takes use of neural network capabilities to produce variable predictions. DRB employs neural network capabilities. When compared to methods that are considered to be more conventional, the findings indicate a significant improvement in the accuracy of this prediction. It is exciting to see that the combined support vector machine and DRB model are producing positive results when it comes to estimating groundwater resources.

I. Introduction

At a time when the urban landscape is constantly shifting, the area of smart city planning is becoming an increasingly vital one for sustainable development. It is necessary to come up with novel and creative solutions in order to effectively manage groundwater, which is an essential resource that sustains urban ecosystems [1]. Because of the increased demand for water resources in smart cities, which are the epicentre of urban expansion, there is a growing need for improved predictive analytics in groundwater management. This is because smart cities are the epicentre of urban expansion [2].