Performance evaluation of satellite precipitation estimation with ground monitoring stations over southern Himalayas in Bhutan

Adhikari, Kirtan; Choden, Yeshi; Cheki, Tshering; Gurung, Lily; Denka, Tshering; Gupta, Vivek

doi:10.1007/s11600-020-00434-z

Cited by 2 publications

(1 citation statement)

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“…Rainfall and temperature are the key environmental variables that are used to understand the different atmospheric, cryosphere and climatic processes within any region of interest. In absence of complete observed data, many studies [2] [3] resort to remote sensing-based data which are model based coupled with station data. However, such data has higher uncertainty and bias introduced when downscaling to station data.…”

Section: Introductionmentioning

confidence: 99%

Imputing Missing Data in Hydrology using Machine Learning Models

Sharma¹,

Yuden²

2021

IJERT

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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).

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Section: Introductionmentioning

confidence: 99%