An Analysis of AI Models for Making Predictions: Groundwater Case Study

Abstract

The development and application of intelligent models assure continuous monitoring and improvement of quality processes that control most of our city's infrastructure. Regression models are a popular tool for making predictions in multiple fields, including finance, healthcare, and weather forecasting. However, the limitations of traditional regression models have prompted the development of more advanced techniques, such as Recurrent Neural Networks (RNNs), which have revolutionized the field of prediction modelling. This paper's main objective is to explore the possibilities that intelligent models offer to real-world problems, specifically the ones that require making predictions to operate, manage, and safeguard the resources and wellbeing of people. The study focuses on groundwater measurements and their applications in predicting reservoir levels, as well as the possibility and criticality of floods, droughts, and other natural phenomena. By analysing available public or open data, it is possible to uncover hidden insights that lead to pattern identification, system behaviours, and risk modelling. The goal is to raise awareness of the power of artificial intelligence and how to integrate them into modern business practices.