Feature Analysis and Selection for Water Stream Modeling

Carlos Moises Chavez-Jimenez, Luis Armando Salazar-Lopez, Kenneth Chapman, Troy Gilmore, Gildardo Sanchez-Ante

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine Learning algorithms have been applied to a variety of problems in hydrology. In this work, the aim is to generate a model able to predict two values of a water stream: stage and discharge, for periods of time that could range several weeks. The input for the model are still images of the river. This paper analyzes features computed by hydrologists and use them to compare several machine learning models. The models tested are: Random Forest, Multilayer Perceptron, K-Nearest Neighbors and Support Vector Machine. The results show that is possible to generate a reasonably good model with all the features. It was also analyzed the selection of attributes with two methods. A simpler model, with a small decrease in accuracy was obtained by this means. The model was able to predict for longer periods of time than the ones reported previously.

Original languageEnglish (US)
Title of host publicationPattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings
EditorsAnsel Yoan Rodríguez-González, Humberto Pérez-Espinosa, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-12
Number of pages10
ISBN (Print)9783031337826
DOIs
StatePublished - 2023
Event15th Mexican Conference on Pattern Recognition, MCPR 2023 - Tepic, Mexico
Duration: Jun 21 2023Jun 24 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th Mexican Conference on Pattern Recognition, MCPR 2023
Country/TerritoryMexico
CityTepic
Period6/21/236/24/23

Keywords

  • Feature Analysis
  • Hydrology
  • Stream
  • Supervised Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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