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

Miguel García, Richard Herrera

2023

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.

Paper Citation

in Harvard Style

García M. and Herrera R. (2023). An Analysis of AI Models for Making Predictions: Groundwater Case Study. In Proceedings of the 20th International Conference on Smart Business Technologies - Volume 1: ICSBT; ISBN 978-989-758-667-5, SciTePress, pages 176-185. DOI: 10.5220/0012120400003552

in Bibtex Style

@conference{icsbt23,
author={Miguel García and Richard Herrera},
title={An Analysis of AI Models for Making Predictions: Groundwater Case Study},
booktitle={Proceedings of the 20th International Conference on Smart Business Technologies - Volume 1: ICSBT},
year={2023},
pages={176-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012120400003552},
isbn={978-989-758-667-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Smart Business Technologies - Volume 1: ICSBT
TI - An Analysis of AI Models for Making Predictions: Groundwater Case Study
SN - 978-989-758-667-5
AU - García M.
AU - Herrera R.
PY - 2023
SP - 176
EP - 185
DO - 10.5220/0012120400003552
PB - SciTePress