Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin
For flood control, hydropower operation, and agricultural planning, among other applications, flow discharge prediction is a critical first step toward the strong and dependable planning and management of water resources. Floods are destructive natural calamities that destroy human lives and infrast...
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KeAi Communications Co., Ltd.
2024-01-01
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| Series: | HydroResearch |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589757824000167 |
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| author | Abinash Sahoo Swayamshu Satyapragnya Parida Sandeep Samantaray Deba Prakash Satapathy |
| author_facet | Abinash Sahoo Swayamshu Satyapragnya Parida Sandeep Samantaray Deba Prakash Satapathy |
| author_sort | Abinash Sahoo |
| collection | DOAJ |
| description | For flood control, hydropower operation, and agricultural planning, among other applications, flow discharge prediction is a critical first step toward the strong and dependable planning and management of water resources. Floods are destructive natural calamities that destroy human lives and infrastructure across the world. Development of effective flood forecasting and prediction models is critical for minimising deaths and mitigating damages. This study employs hybrid deep learning Long Short Term Memory (LSTM) algorithms like LSTM, Convolution LSTM (Conv-LSTM) and Convolutional Neural Network LSTM (CNN-LSTM) to predict likelihood flood events using daily precipitation, daily temperature and daily relative humidity from two flood-forecasting stations i.e., Champua (Baitarani River, Odisha) and Jarikela (Brahmani River, Odisha) over a 20-year period. The results show that CNN-LSTM performed best followed by Conv-LSTM and LSTM in terms of R2 = 0.98055, 0.96564, and 0.93244, RMSE = 19.137, 35.635, and 49.347, MAE = 18.372, 33.766, and 47.058, NSE = 0.971, 0.9517 and 0.9257 respectively. The findings support the claim that machine learning models and algorithms, in particular CNN-LSTM model, can be applied to flood forecasting with high accuracy, thereby enhancing water and hazard management. |
| format | Article |
| id | doaj-art-bcb93e208f474fa3bacdaf304419fd5d |
| institution | Kabale University |
| issn | 2589-7578 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | KeAi Communications Co., Ltd. |
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| series | HydroResearch |
| spelling | doaj-art-bcb93e208f474fa3bacdaf304419fd5d2024-11-29T06:24:53ZengKeAi Communications Co., Ltd.HydroResearch2589-75782024-01-017272284Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basinAbinash Sahoo0Swayamshu Satyapragnya Parida1Sandeep Samantaray2Deba Prakash Satapathy3Department of Civil Engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha, IndiaDepartment of Civil Engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha, IndiaDepartment of Civil Engineering, National Institute of Technology, Srinagar, J&K, India; Corresponding author.Department of Civil Engineering, Odisha University of Technology and Research, Bhubaneswar, Odisha, IndiaFor flood control, hydropower operation, and agricultural planning, among other applications, flow discharge prediction is a critical first step toward the strong and dependable planning and management of water resources. Floods are destructive natural calamities that destroy human lives and infrastructure across the world. Development of effective flood forecasting and prediction models is critical for minimising deaths and mitigating damages. This study employs hybrid deep learning Long Short Term Memory (LSTM) algorithms like LSTM, Convolution LSTM (Conv-LSTM) and Convolutional Neural Network LSTM (CNN-LSTM) to predict likelihood flood events using daily precipitation, daily temperature and daily relative humidity from two flood-forecasting stations i.e., Champua (Baitarani River, Odisha) and Jarikela (Brahmani River, Odisha) over a 20-year period. The results show that CNN-LSTM performed best followed by Conv-LSTM and LSTM in terms of R2 = 0.98055, 0.96564, and 0.93244, RMSE = 19.137, 35.635, and 49.347, MAE = 18.372, 33.766, and 47.058, NSE = 0.971, 0.9517 and 0.9257 respectively. The findings support the claim that machine learning models and algorithms, in particular CNN-LSTM model, can be applied to flood forecasting with high accuracy, thereby enhancing water and hazard management.http://www.sciencedirect.com/science/article/pii/S2589757824000167FloodLong short term memoryConvolution-LSTMConvolutional neural network-LSTMBrahmani-Baitarani basin |
| spellingShingle | Abinash Sahoo Swayamshu Satyapragnya Parida Sandeep Samantaray Deba Prakash Satapathy Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin HydroResearch Flood Long short term memory Convolution-LSTM Convolutional neural network-LSTM Brahmani-Baitarani basin |
| title | Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin |
| title_full | Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin |
| title_fullStr | Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin |
| title_full_unstemmed | Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin |
| title_short | Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin |
| title_sort | daily flow discharge prediction using integrated methodology based on lstm models case study in brahmani baitarani basin |
| topic | Flood Long short term memory Convolution-LSTM Convolutional neural network-LSTM Brahmani-Baitarani basin |
| url | http://www.sciencedirect.com/science/article/pii/S2589757824000167 |
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