Title: GAM-IDF: a web tool for fitting IDF equations from daily rainfall data

Authors: Marcelle Martins Vargas; Samuel Beskow; Maíra Martim de Moura; Zandra Almeida da Cunha; Tamara Leitzke Caldeira Beskow; João Pedro de Morais da Silveira

Addresses: Centre for Technological Development/ Post-Graduate Program in Water Resources, Federal University of Pelotas, Gomes Carneiro Street, Pelotas, RS, Brazil ' Centre for Technological Development/Water Resources Engineering, Federal University of Pelotas, Gomes Carneiro Street, Pelotas, RS, Brazil ' Centre for Technological Development/ Post-Graduate Program in Water Resources, Federal University of Pelotas, Gomes Carneiro Street, Pelotas, RS, Brazil ' Centre for Technological Development/ Post-Graduate Program in Water Resources, Federal University of Pelotas, Gomes Carneiro Street, Pelotas, RS, Brazil ' Engineering Centre/Civil Engineering, Federal University of Pelotas, 989 Benjamin Constant Street, Pelotas, RS, Brazil ' Centre for Technological Development/Computer Science, Federal University of Pelotas, Gomes Carneiro Street, Pelotas, RS, Brazil

Abstract: This paper aims to present the genetic algorithm methodology for IDF (GAM-IDF), addressing its motivation, conception, implementation and functionalities. GAM-IDF was coded in R for web and ideated to fit IDF equations from daily rainfall data, considering: 1) importation of a daily rainfall series or an annual maximum daily rainfall series; 2) trend analysis; 3) fit of simple and multiparameter probability density functions and calculation of quantiles; 4) robust goodness-of-fit tests; 5) disaggregation of daily rainfall for different sets of constants; 6) fit of IDF equations through a genetic algorithm. This tool provides the graph containing the IDF curves and the parameters of the fitted equation. In addition, GAM-IDF provides a calculator with the IDF equation along with its fitted parameters. GAM-IDF is free, has a friendly interface, can be used in computers and smartphones, and uses state-of-the-art techniques to fit IDF equations from daily rainfall data.

Keywords: heavy rainfall; intensity-duration-frequency; design rainfall; multiparameter probability density functions; rainfall disaggregation; artificial intelligence.

DOI: 10.1504/IJHST.2023.131882

International Journal of Hydrology Science and Technology, 2023 Vol.16 No.1, pp.37 - 60

Received: 05 Nov 2020
Accepted: 23 Jan 2022
Published online: 04 Jul 2023 *

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