Water quality prediction model based on improved long short-term memory neural network and empirical mode decomposition
Abstract Water quality prediction and monitoring are crucial for environmental protection. This study proposes an improved long short-term memory neural network model for complex time-series water quality data. The model optimizes traditional long short-term memory structures to address the fluidity...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-08-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00454-y |
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| Summary: | Abstract Water quality prediction and monitoring are crucial for environmental protection. This study proposes an improved long short-term memory neural network model for complex time-series water quality data. The model optimizes traditional long short-term memory structures to address the fluidity of water quality data. Additionally, empirical mode decomposition is introduced to capture water pollution characteristics and identify trends and fluctuations. Simulation results indicate that the optimal configuration included a sliding window size of 4 and 20 hidden layer nodes, converging after 22 training iterations with a loss value of approximately 0.027. The improved model achieved a 31% reduction in mean absolute error and a 50% reduction in mean squared error for different ammonia nitrogen concentrations. Overall, the enhanced long short-term memory and preprocessing methods significantly boost prediction accuracy and reliability, aiding effective water management and timely responses for decision-makers. |
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| ISSN: | 2731-0809 |