Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment
Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/12/7/1909 |
| _version_ | 1797416858359431168 |
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| author | Javier Estévez Juan Antonio Bellido-Jiménez Xiaodong Liu Amanda Penélope García-Marín |
| author_facet | Javier Estévez Juan Antonio Bellido-Jiménez Xiaodong Liu Amanda Penélope García-Marín |
| author_sort | Javier Estévez |
| collection | DOAJ |
| description | Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied. |
| first_indexed | 2024-03-09T06:10:17Z |
| format | Article |
| id | doaj.art-4128c9de77d94706b71d8de28158c37a |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T06:10:17Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-4128c9de77d94706b71d8de28158c37a2023-12-03T11:58:39ZengMDPI AGWater2073-44412020-07-01127190910.3390/w12071909Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid EnvironmentJavier Estévez0Juan Antonio Bellido-Jiménez1Xiaodong Liu2Amanda Penélope García-Marín3Engineering Projects Area, Department of Rural Engineering, University of Córdoba, 14071 Córdoba, SpainEngineering Projects Area, Department of Rural Engineering, University of Córdoba, 14071 Córdoba, SpainSchool of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UKEngineering Projects Area, Department of Rural Engineering, University of Córdoba, 14071 Córdoba, SpainAccurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied.https://www.mdpi.com/2073-4441/12/7/1909precipitationforecastingwaveletneural networks models |
| spellingShingle | Javier Estévez Juan Antonio Bellido-Jiménez Xiaodong Liu Amanda Penélope García-Marín Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment Water precipitation forecasting wavelet neural networks models |
| title | Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment |
| title_full | Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment |
| title_fullStr | Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment |
| title_full_unstemmed | Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment |
| title_short | Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment |
| title_sort | monthly precipitation forecasts using wavelet neural networks models in a semiarid environment |
| topic | precipitation forecasting wavelet neural networks models |
| url | https://www.mdpi.com/2073-4441/12/7/1909 |
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