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Rainfall Forecasting using a Bayesian framework and Long Short-Term Memory Multi-model Estimation based on an hourly meteorological monitoring network. Case of study: Andean Ecuadorian Tropical City

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Abstract

Rainfall forecasting is a challenging task due to the time-dependencies of the variables and the stochastic behavior of the process. The difficulty increases when the zone of interest is characterized by a large spatio-temporal variability of its meteorological variables, causing large variations of rainfall even within a small zone such as the Tropical Andes. To address this problem, we propose a methodology for building a group of models based on Long Short-Term Memory (LSTM) neural networks using Bayesian optimization. We optimize the model hyperparameters using accumulated experience to reduce the hyperparameter search space over successive iterations. The result is a large reduction in modeling time that allows the building of specialized LSTM models for each zone and forecasting time. We evaluated the method by forecasting rain events in the urban zone of Cuenca City in Ecuador, a city with large spatio-temporal variability. The results show that our proposed model offers better performance over the trivial forecaster for up to 9 hours of future forecasts with an accuracy of up to 84.4%. The model was compared to its equivalent LSTM model without optimization.

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Data Availability

The data used in this work is available in ETAPA EP.

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Acknowledgements

The work was sponsored in part by the GIDTEC project (No. 003-002-2016-03-03). The signal acquisition work was developed by the hydrometeorological network of the Empresa Pública Municipal de Telecomunicaciones, Agua Potable, Alcantarillado y Saneamiento de Cuenca ETAPA EP. The computational work was developed at the GIDTEC Research Group Lab of the Universidad Politécnica Salesiana de Cuenca, Ecuador.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Universidad Politécnica Salesiana, Cuenca, 010102, Azuay, Ecuador

    Diego Cabrera, Mariela Cerrada & René-Vinicio Sánchez

  2. HydroLab, Universidad Católica de Cuenca, Cuenca, 010109, Azuay, Ecuador

    María Quinteros

  3. Subgerencia de Gestión Ambiental, Empresa Pública Municipal de Telecomunicaciones, Agua Potable y Alcantarillado de Cuenca (ETAPA EP), Cuenca, 010207, Azuay, Ecuador

    Mario Guallpa

  4. Computer Science and Artificial Intelligence Department, Seville University, Seville, 41012, Andalucía, Spain

    Fernando Sancho

  5. School of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523820, Dongguan, China

    Chuan Li

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  1. Diego Cabrera
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Contributions

Conceptualization- Diego Cabrera, Vinicio Sánchez, Mariela Cerrada, Chuan Li. Data collection- Mario Guallpa. Model creation- Diego Cabrera, María Quinteros. Writing original draft- Diego Cabrera, María Quinteros. Writing-Review and Editing- Fernando Sancho, Mariela Cerrada.

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Correspondence to Diego Cabrera.

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Communicated by: H. Babaie

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Cabrera, D., Quinteros, M., Cerrada, M. et al. Rainfall Forecasting using a Bayesian framework and Long Short-Term Memory Multi-model Estimation based on an hourly meteorological monitoring network. Case of study: Andean Ecuadorian Tropical City. Earth Sci Inform 16, 1373–1388 (2023). https://doi.org/10.1007/s12145-023-00958-0

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