Abstract
The main aim of the rain forecast is to determine rain occurrence conditions in a specific location. This is considered of vital importance to assess the availability of water resources in a basin. In this study, several methods are analyzed to forecast monthly rainfall totals in hydrological basins. The study region was the Almendares-Vento basin, Cuba. Based on Multi–Layer Perceptron (MLP), Convolutional Neural Network (CNN) and Long Short–Term Memory (LSTM) neural networks, and Autoregressive Integrated Moving Average (ARIMA) models, we developed a hybrid model (ANN + ARIMA) for rainfall prediction. The input data were the one year lagged rainfall records in gauge stations within the basin, sunspots, the sea surface temperature and time series of nine climate indices up to 2014. The predictions were also compared with the rainfall records of a gauge station network from 2015 to 2019 provided by the Cuban National Institute of Hydraulic Resources. Based on several statistical metrics such as mean absolute error, Pearson correlation, BIAS, Nash–Sutcliffe efficiency and Kling–Gupta efficiency, the CNN model showed higher ability to forecast monthly rainfall. Nevertheless, the hybrid model was notably better than individual models. Overall, our findings have proved the reliability of using the hybrid model to predict rainfall time series for water management and can be extensively applied to this sort of application. In addition, this work proposes a new approach to enhance the planning and management of water availability in watershed for agriculture, industry and population through improving rainfall forecasting.
Article Highlights
Convolutional Neural Network model is able to forecast monthly rainfall amounts.
Our methodology allows the models to learn the seasonal variations of the rainfall.
The hybrid model is skillful to forecast rainfall time series for water management.
The findings are promising to enhance water management systems.
The method can be easily applied to predict rainfall in other watersheds.
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Data Availability
The python source codes developed to train the ANN models and MATLAB codes for ARIMA models are accessible to be downloaded for free at https://github.com/apalarcon/ANNs_train. The climatic indices databases utilized in the article were retrieved online paying no fees. The AMO index can be obtained from the NOAA/Physical Sciences Laboratory (https://www.psl.noaa.gov/data/timeseries/AMO/), the NAO from the Climatic Research Unit (https://crudata.uea.ac.uk/cru/data/nao/nao.dat), the SOI was taken from the Bureau of Meteorology (http://www.bom.gov.au/climate/current/soihtm1.shtml), the mean SST values in the Niño 1.2, Niño 3.0, Niño 4.0 and Niño 3.4 regions were obtained from the Global Climate Observing System (https://psl.noaa.gov/gcoswgsp/), while sunspots were retrieved from the Solar Influences Data Analysis Center (http://sidc.oma.be/silso/datafiles). Additionally, the Centennial Time Scale (COBE SST2) dataset of the National Oceanic and Atmospheric Administration (NOAA) was used, which can be found at https://psl.noaa.gov/data/gridded/data.cobe2.html.
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Acknowledgements
The authors thank the National Institute of Hydraulic Resources (INRH, Spanish acronym) of Cuba for providing the rainfall records of each gauge station in the Almendares-Vento basin. The authors also acknowledge the free availability of climatic indices used in this study. Besides, we appreciate the English writing corrections made by Oraily Madruga Rios, professor and translator from Instituto Superior de Tecnologías y Ciencias Aplicadas, Universidad de La Habana.
Funding
This work was supported by the “Expert system to support decision making during water supply management of the Albear aqueduct” project (grant No. PS113LH001-019) funded by the National Institute of Hydraulic Resources of Cuba.
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A. Pérez-Alarcón: Conceptualization, Data Curation, Methodology, Formal analysis, Software, Investigation, Validation, Visualization, Writing - original draft, Writing - review & editing. D. Garcia-Cortes: Conceptualization, Methodology, Investigation, Writing - review & editing, Supervision. J. C. Fernández-Alvarez: Conceptualization, Data Curation, Methodology, Investigation, Validation, Visualization, Writing - review & editing. Y. Martínez-González: Conceptualization, Data Curation, Methodology, Formal analysis, Software, Investigation, Validation, Writing - original draft, Writing - review & editing, Supervision.
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Pérez-Alarcón, A., Garcia-Cortes, D., Fernández-Alvarez, J.C. et al. Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models. Environ. Process. 9, 53 (2022). https://doi.org/10.1007/s40710-022-00602-x
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DOI: https://doi.org/10.1007/s40710-022-00602-x