Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India
Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2020/8685724 |
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| author | B. S. Supreetha Narayan Shenoy Prabhakar Nayak |
| author_facet | B. S. Supreetha Narayan Shenoy Prabhakar Nayak |
| author_sort | B. S. Supreetha |
| collection | DOAJ |
| description | Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current research aimed at developing a groundwater level forecasting model by using hybrid long short-term memory-lion algorithm (LSTM-LA). The historical GWL and rainfall data from an observation well from Udupi district, located in Karnataka state, India, were used to develop the model. The prediction accuracy of the hybrid LSTM-LA model was better than that of the feedforward neural network (FFNN) and the isolated LSTM models. The hybrid LSTM-LA-based forecasting model is promising for a larger dataset. |
| format | Article |
| id | doaj-art-ec8ede1e265840dea6d93587757280e5 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-ec8ede1e265840dea6d93587757280e52025-02-03T01:27:05ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322020-01-01202010.1155/2020/86857248685724Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, IndiaB. S. Supreetha0Narayan Shenoy1Prabhakar Nayak2Department of ECE, Manipal Institute of Technology, Manipal 576104, IndiaManipal Institute of Technology, Manipal Academy of Higher Educatio, Manipal Academy of Higher Education (MAHE), Manipal, IndiaDepartment of ECE, Manipal Institute of Technology, Manipal 576104, IndiaGroundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current research aimed at developing a groundwater level forecasting model by using hybrid long short-term memory-lion algorithm (LSTM-LA). The historical GWL and rainfall data from an observation well from Udupi district, located in Karnataka state, India, were used to develop the model. The prediction accuracy of the hybrid LSTM-LA model was better than that of the feedforward neural network (FFNN) and the isolated LSTM models. The hybrid LSTM-LA-based forecasting model is promising for a larger dataset.http://dx.doi.org/10.1155/2020/8685724 |
| spellingShingle | B. S. Supreetha Narayan Shenoy Prabhakar Nayak Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India Applied Computational Intelligence and Soft Computing |
| title | Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India |
| title_full | Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India |
| title_fullStr | Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India |
| title_full_unstemmed | Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India |
| title_short | Lion Algorithm-Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India |
| title_sort | lion algorithm optimized long short term memory network for groundwater level forecasting in udupi district india |
| url | http://dx.doi.org/10.1155/2020/8685724 |
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