TY - GEN
T1 - Artificial Intelligence to Forecast Precipitation Levels in Urban Areas of Santo Domingo-Ecuador
AU - Sabando-García, Ángel Ramón
AU - Ugando-Peñate, Mikel
AU - Guerrero-Flores, Héctor Octavio
AU - Tarazona-Meza, Nestor Leopoldo
AU - Armas-Herrera, Reinaldo
AU - Higuerey-Gomez, Angel Alexander
AU - Quishpe-Meza, Danny Paul
AU - Sánchez-Armijos, Erik Rodrigo
AU - Montesdeoca-Saldarriaga, Marly Yicela
AU - Lima-Rojas, Byron Vinicio
AU - Ajila-Pinzon, Omar Enrique
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Precipitation predictions in urban areas are considered very relevant in the context of civil engineering, the economic and architecture, becoming a determinant of urban planning. This study aims to modeling and predicting nivel rainfall in the urban area of the city of Santo Domingo de Los Tsachilas in Ecuador with artificial intelligence. In this research, statistical techniques of simple linear regression and time series are used using the integrated autoregressive model of moving average with the Rstudio package, for the climatic factor of precipitation from meteorological stations the La Concordia and Puerto Ila, from the years 2015 to 2022. The results show that rainfall presents a non-significant decreasing trend for the two stations according to the Pearson tests and the Kendall Mann tests. The precipitation simulations for the La Concordia station showed an ARIMA (0,1,1) (2,1,0)[12] and the Puerto Ila station recorded an ARIMA (2,1,0) (2,1,0)[12] simulations obtained through 96 iterations provided by artificial intelligence. It is concluded in this investigation that the precipitation variable presents a decreasing trend and with a high variability of the observed and forecast data in the station closest to the urban area, a factor causing the climate impact.
AB - Precipitation predictions in urban areas are considered very relevant in the context of civil engineering, the economic and architecture, becoming a determinant of urban planning. This study aims to modeling and predicting nivel rainfall in the urban area of the city of Santo Domingo de Los Tsachilas in Ecuador with artificial intelligence. In this research, statistical techniques of simple linear regression and time series are used using the integrated autoregressive model of moving average with the Rstudio package, for the climatic factor of precipitation from meteorological stations the La Concordia and Puerto Ila, from the years 2015 to 2022. The results show that rainfall presents a non-significant decreasing trend for the two stations according to the Pearson tests and the Kendall Mann tests. The precipitation simulations for the La Concordia station showed an ARIMA (0,1,1) (2,1,0)[12] and the Puerto Ila station recorded an ARIMA (2,1,0) (2,1,0)[12] simulations obtained through 96 iterations provided by artificial intelligence. It is concluded in this investigation that the precipitation variable presents a decreasing trend and with a high variability of the observed and forecast data in the station closest to the urban area, a factor causing the climate impact.
KW - Artificial intelligence
KW - Data analysis
KW - Economic forecast
KW - Precipitation
KW - Scientific statistics
KW - Urban area
UR - https://www.scopus.com/pages/publications/105030232661
U2 - 10.1007/978-3-031-98768-7_5
DO - 10.1007/978-3-031-98768-7_5
M3 - Contribución a la conferencia
AN - SCOPUS:105030232661
SN - 9783031987670
T3 - Lecture Notes in Networks and Systems
SP - 67
EP - 84
BT - Proceedings of the International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2024 - Volume 1
A2 - Garcia, Marcelo V.
A2 - Reyes, John-Paul
A2 - Nuñez, Carlos
A2 - Gordón-Gallegos, Carlos
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Computer Science, Electronics and Industrial Engineering, CSEI 2024
Y2 - 21 October 2024 through 25 October 2024
ER -