Abstract
Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods and many gaps and failures at the daily scale. Thus, a hybrid model was developed based on an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) coupled with the maximum overlap discrete wavelet (MODWT) method to obtain precipitation estimates. Six rainfall gauge stations located in different biomes within the studied region were adopted, and satellite data (CMORPH) were used. The interval of data that was have used is 1998–2016. The precipitation data were evaluated by seasonal (wet and dry) periods. The results obtained demonstrated the good capacity of the MODWT-ANFIS model to simulate the daily precipitation. In this case, data entries lagged by 4 days and 5 days performed better, with Nash values close to 1.0 and mean square errors (MSE) below 0.1.
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Acknowledgements
The authors thank ANA and NOAA for providing the precipitation data.
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Coordination for the Improvement of Higher Education Personnel of Brasil (CAPES), Finance Code 001. CNPq for funding the research with a productivity grant (Process 303542/2018–7). CNPq for funding the research with a productivity grant (Process 309681/2019–7). Office for research (PROPESP) and Foundation for Research Development (FADESP) of the Federal University of Pará through grant nº PAPQ 2021.
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Gomes, E.P., Blanco, C.J.C., da Silva Holanda, P. et al. MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region. Environ Monit Assess 194, 296 (2022). https://doi.org/10.1007/s10661-022-09939-0
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DOI: https://doi.org/10.1007/s10661-022-09939-0