Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India
Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-dri...
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-01-01
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| Series: | Journal of Hydroinformatics |
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| Online Access: | http://jh.iwaponline.com/content/24/1/16 |
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| author | Pavan Kumar Yeditha Maheswaran Rathinasamy Sai Sumanth Neelamsetty Biswa Bhattacharya Ankit Agarwal |
| author_facet | Pavan Kumar Yeditha Maheswaran Rathinasamy Sai Sumanth Neelamsetty Biswa Bhattacharya Ankit Agarwal |
| author_sort | Pavan Kumar Yeditha |
| collection | DOAJ |
| description | Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments. HIGHLIGHTS
Evaluated applicability of satellite rainfall products for rainfall - runoff modelling in Vamsadhara and Mahanadi river basins.;
GPM-IMERG and CHIRPS data could be reliably used with LSTM and ELM models for rainfall -runoff modelling and streamflow forecasting.;
Deep learning models with the CHIRPS products can lead to a new horizon to provide flood forecasting in flood-prone catchments.; |
| first_indexed | 2024-12-24T00:13:23Z |
| format | Article |
| id | doaj.art-2081784a7f364d4b937190fe965e658e |
| institution | Directory Open Access Journal |
| issn | 1464-7141 1465-1734 |
| language | English |
| last_indexed | 2024-12-24T00:13:23Z |
| publishDate | 2022-01-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Hydroinformatics |
| spelling | doaj.art-2081784a7f364d4b937190fe965e658e2022-12-21T17:24:48ZengIWA PublishingJournal of Hydroinformatics1464-71411465-17342022-01-01241163710.2166/hydro.2021.067067Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of IndiaPavan Kumar Yeditha0Maheswaran Rathinasamy1Sai Sumanth Neelamsetty2Biswa Bhattacharya3Ankit Agarwal4 Department of Civil Engineering, MVGR College of Engineering, Vizianagaram, Andhra Pradesh 535005, India Department of Civil Engineering, Indian Institute of Technology Hyderabad, Kandi 502285, India Department of Civil Engineering, MVGR College of Engineering, Vizianagaram, Andhra Pradesh 535005, India Water Science and Engineering, IHE-Delft, Delft, Netherlands Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India Rainfall–runoff models are valuable tools for flood forecasting, management of water resources, and drought warning. With the advancement in space technology, a plethora of satellite precipitation products (SPPs) are available publicly. However, the application of the satellite data for the data-driven rainfall–runoff model is emerging and requires careful investigation. In this work, two satellite rainfall data sets, namely Global Precipitation Measurement-Integrated Multi-Satellite Retrieval Product V6 (GPM-IMERG) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS), are evaluated for the development of rainfall–runoff models and the prediction of 1-day ahead streamflow. The accuracy of the data from the SPPs is compared to the India Meteorological Department (IMD)-gridded precipitation data set. Detection metrics showed that for light rainfall (1–10 mm), the probability of detection (POD) value ranges between 0.67 and 0.75 and with an increasing rainfall range, i.e., medium and heavy rainfall (10–50 mm and >50 mm), the POD values ranged from 0.24 to 0.45. These results indicate that the satellite precipitation performs satisfactorily with reference to the IMD-gridded data set. Using the daily precipitation data of nearly two decades (2000–2018) over two river basins in India's eastern part, artificial neural network, extreme learning machine (ELM), and long short-time memory (LSTM) models are developed for rainfall–runoff modelling. One-day ahead runoff prediction using the developed rainfall–runoff modelling confirmed that both the SPPs are sufficient to drive the rainfall–runoff models with a reasonable accuracy estimated using the Nash–Sutcliffe Efficiency coefficient, correlation coefficient, and the root-mean-squared error. In particular, the 1-day streamflow forecasts for the Vamsadhara river basin (VRB) using LSTM with GPM-IMERG inputs resulted in Nash-Sutcliffe Efficiency Coefficient (NSC) values of 0.68 and 0.67, while ELM models for Mahanadhi river basin (MRB) with the same input resulted in NSC values of 0.86 and 0.87, respectively, during training and validation stages. At the same time, the LSTM model with CHIRPS inputs for the VRB resulted in NSC values of 0.68 and 0.65, and the ELM model with CHIRPS inputs for the MRB resulted in NSC values of 0.89 and 0.88, respectively, in training and validation stages. These results indicated that both the SPPs could reliably be used with LSTM and ELM models for rainfall–runoff modelling and streamflow prediction. This paper highlights that deep learning models, such as ELM and LSTM, with the GPM-IMERG products can lead to a new horizon to provide flood forecasting in flood-prone catchments. HIGHLIGHTS Evaluated applicability of satellite rainfall products for rainfall - runoff modelling in Vamsadhara and Mahanadi river basins.; GPM-IMERG and CHIRPS data could be reliably used with LSTM and ELM models for rainfall -runoff modelling and streamflow forecasting.; Deep learning models with the CHIRPS products can lead to a new horizon to provide flood forecasting in flood-prone catchments.;http://jh.iwaponline.com/content/24/1/16artificial neural networks (ann)deep learningextreme learning machines (elm)long short time memory (lstm)rainfall-runoff modellingsatellite rainfall products |
| spellingShingle | Pavan Kumar Yeditha Maheswaran Rathinasamy Sai Sumanth Neelamsetty Biswa Bhattacharya Ankit Agarwal Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India Journal of Hydroinformatics artificial neural networks (ann) deep learning extreme learning machines (elm) long short time memory (lstm) rainfall-runoff modelling satellite rainfall products |
| title | Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India |
| title_full | Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India |
| title_fullStr | Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India |
| title_full_unstemmed | Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India |
| title_short | Investigation of satellite precipitation product driven rainfall-runoff model using deep learning approaches in two different catchments of India |
| title_sort | investigation of satellite precipitation product driven rainfall runoff model using deep learning approaches in two different catchments of india |
| topic | artificial neural networks (ann) deep learning extreme learning machines (elm) long short time memory (lstm) rainfall-runoff modelling satellite rainfall products |
| url | http://jh.iwaponline.com/content/24/1/16 |
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