TY - GEN
T1 - Feature Analysis and Selection for Water Stream Modeling
AU - Chavez-Jimenez, Carlos Moises
AU - Salazar-Lopez, Luis Armando
AU - Chapman, Kenneth
AU - Gilmore, Troy
AU - Sanchez-Ante, Gildardo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Feature Analysis
KW - Hydrology
KW - Stream
KW - Supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85164254719&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-33783-3_1
DO - 10.1007/978-3-031-33783-3_1
M3 - Conference contribution
AN - SCOPUS:85164254719
SN - 9783031337826
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 12
BT - Pattern Recognition - 15th Mexican Conference, MCPR 2023, Proceedings
A2 - Rodríguez-González, Ansel Yoan
A2 - Pérez-Espinosa, Humberto
A2 - Martínez-Trinidad, José Francisco
A2 - Carrasco-Ochoa, Jesús Ariel
A2 - Olvera-López, José Arturo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th Mexican Conference on Pattern Recognition, MCPR 2023
Y2 - 21 June 2023 through 24 June 2023
ER -