Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations

• Published in: The Science of the total environment 2024-06, Vol.927, p.172246-172246, Article 172246
• Main Authors: Kow, Pu-Yun, Liou, Jia-Yi, Yang, Ming-Ting, Lee, Meng-Hsin, Chang, Li-Chiu, Chang, Fi-John
• Format: Article
• Language: English
• Subjects: Deep learning; Disaster risk reduction; Flood forecast; Long short term memory neural network (LSTM); Transformer
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2024.172246

Proactive management of pumping stations using artificial intelligence (AI) technology is vital for effectively mitigating the impacts of flood events caused by climate change. Accurate water level forecasts are pivotal in advancing the intelligent operation of pumping stations. This study proposed a novel Transformer-LSTM model to offer accurate multi-step-ahead forecasts of the flood storage pond (FSP) and river water levels for the Zhongshan pumping station in Taipei, Taiwan. A total of 19,647 ten-minute-based datasets of pumping operation and storm sewer, FSP, and river water levels were collected between 2014 and 2020 and further divided into training (70 %), validation (10 %), and test (20 %) datasets for model construction. The results demonstrate that the proposed model dramatically outperforms benchmark models by producing more accurate and reliable water level forecasts at 10-minute (T + 1) to 60-minute (T + 6) horizons. The proposed model effectively enhances the connections between input factors through the Transformer module and increases the connectivity across consecutive time series using the LSTM module. This study reveals interconnected dynamics among pumping operation and storm sewer, FSP, and river water levels, enhancing flood management. Understanding these dynamics is crucial for effective execution of management strategies and infrastructure revitalization against climate impacts. The Transformer-LSTM model's forecasts encourage water practices, resilience, and disaster risk reduction for extreme weather events. [Display omitted] •Develop a novel hybrid deep learning forecast model using Transformer and LSTM•Transformer-LSTM explores the interaction between pumping operations and water levels.•Transformer-LSTM makes accurate and reliable multi-step-ahead water level forecasts.•Transformer-LSTM surpasses the benchmark by capturing key features and memory ability.•Transformer-LSTM forecasts promote sustainable water practices and resilience.