Imputing Missing Data in Hydrology using Machine Learning Models
Fuente:
Zenodo
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Recurso digital |
| Veröffentlicht: |
Zenodo
2021
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901417190490112 |
|---|---|
| author | Vasker Sharma Kezang Yuden |
| author_facet | Vasker Sharma Kezang Yuden |
| contents | Missing data has been a common problem and has been confronted by many researchers in the field of hydrology. Rainfall and Temperature time series data are often found missing and such missingness have huge implication on hydrological modelling, flood frequency analysis, trend analysis and dam operation schemes. Owing to the presence of missing data it hinders the performance analysis of the data and inhibits in concluding the correct inferences from the data. In this study, missing data in the rainfall and temperature has been imputed using kNN model and Tree-based model and subsequently these imputed data have been used as predictors to predict the river flow data using Artificial Neural Network (ANN). Uncertainty from kNN imputation model has been found with bootstrapping techniques, while the tree based and ANN model were assessed by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18647002 |
| institution | Zenodo |
| language | |
| publishDate | 2021 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Imputing Missing Data in Hydrology using Machine Learning Models Vasker Sharma Kezang Yuden Missing values hydrology kNN ANN Regression Decision tree. Missing data has been a common problem and has been confronted by many researchers in the field of hydrology. Rainfall and Temperature time series data are often found missing and such missingness have huge implication on hydrological modelling, flood frequency analysis, trend analysis and dam operation schemes. Owing to the presence of missing data it hinders the performance analysis of the data and inhibits in concluding the correct inferences from the data. In this study, missing data in the rainfall and temperature has been imputed using kNN model and Tree-based model and subsequently these imputed data have been used as predictors to predict the river flow data using Artificial Neural Network (ANN). Uncertainty from kNN imputation model has been found with bootstrapping techniques, while the tree based and ANN model were assessed by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). |
| title | Imputing Missing Data in Hydrology using Machine Learning Models |
| topic | Missing values hydrology kNN ANN Regression Decision tree. |
| url | https://doi.org/10.5281/zenodo.18647002 |