Abstract
In this paper, a pipe water level prediction platform is proposed to address the low predictability issue of traditional drainage systems in combating waterlogging. Based on the drainage pipe network, the study integrates real-time data collected through the Internet of Things (IoT) sensor system and predictive data from the Storm Water Management Model (SWMM) and Long Short-Term Memory (LSTM) neural network models to enhance the accuracy and speed of waterlogging forecasts. Additionally, a web-based and WeChat Mini Program application platform has been developed. The platform was tested and validated in a specific region in Huzhou. Experimental results demonstrate the platform’s high prediction accuracy and its potential for urban waterlogging risk monitoring and early warning, offering a novel approach to smart water management.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sheng, Z., Zheng, M. (2024). Pipe Network Water Level Prediction Platform Coupled with SWMM and LSTM. In: Weng, CH. (eds) Proceedings of The 9th International Conference on Water Resource and Environment. WRE 2023. Lecture Notes in Civil Engineering, vol 468. Springer, Singapore. https://doi.org/10.1007/978-981-97-0948-9_13
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DOI: https://doi.org/10.1007/978-981-97-0948-9_13
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