Spatio-temporal interpolation of rainfall data...
URL: https://doi.org/10.1109/enc53357.2021.9534803
One of the most common problems related to meteorological information is the missing registers. This lack of data generates uncertainties in the analysis of climate, hydrology, and natural disasters. In Mexico, very often, this problem is present in all the meteorological stations of the country. In this study, we apply two well-established spatial interpolation methods that have report competitive performance in the specialized literature: the Inverse Distance Weighting (IDW) and Modified Inverse Distance Weighting (MIDW); and they are compared with a proposal of spatio-temporal regression using an artificial neural network of the kind of multilayer perceptron (MLP). The results show that using a combination of spatial and temporal data with a low number of predictors is competitive with the comparing methods using a high number of predictors. We compare the methods through statistical measures of the error for 31 meteorological stations of the Jalisco state in the period of 2002-2006.
Información adicional
| Campo | Valor |
|---|---|
| Última actualización de los datos | 11 de octubre de 2025 |
| Última actualización de los metadatos | 11 de octubre de 2025 |
| Creado | 11 de octubre de 2025 |
| Formato | HTML |
| Licencia | No se ha provisto de una licencia |
| Id | f7fc170d-7747-45c4-a1b7-f7ef557cd3eb |
| Package id | 490d09f9-48ae-4c41-ba15-c8c979edea7c |
| State | active |