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Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method

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Abstract

Accurate flood runoff and water level predictions are crucial research topics due to their significance for early warning systems, particularly in improving peak flood level forecasts and reducing time lags. This study proposes a novel method, Trend Forecasting Method (TFM), to improve model accuracy and overcome the time lag problem due to data scarcity. The proposed method includes the following steps: (1) select appropriate input factors causing flood events, (2) determine the most suitable AI method as the basis for forecasting models, (3) a forecasting model using a multi-step-ahead approach and a forecasting model with variation in flood depth as input are developed as compared to the selected model in Step 2, and (4) according to the rising limb and falling limb of a flood hydrograph, the maximum and minimum values predicted by the models above are respectively selected as the final outputs. The application to demonstrate the advantages of the proposed method was conducted in the Annan District of Tainan City, Taiwan. Of all the models tested, the Gated Recurrent Unit (GRU) demonstrated superior accuracy in forecasting flood depths, followed by Long Short-Term Memory (LSTM) and Bidirectional LSTM, with the Back Propagation Neural Network falling behind. With a Nash–Sutcliffe efficiency coefficient (NSE) of 0.56 for the next hour’s forecast, the GRU model’s structure appears particularly fitting for flood depth forecast. However, all four models showed time lag issues. TFM substantially enhanced the GRU model’s forecast accuracy, mitigating the time lag. TFM achieved an NSE of 0.82 for forecasting 10-, 20-, 30-, 40-, 50-, and 60-min lead time. The observed flood depths had a 68% probability of consistent rise or fall, validating TFM’s underlying hypothesis. Furthermore, including an autoregressive model in TFM reduced the time lag problem.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

References

  • Berkhahn S, Fuchs L, Neuweiler I (2019) An ensemble neural network model for real-time prediction of urban floods. J Hydrol 575:743–754

    Article  Google Scholar 

  • Bollerslev T, Engle RF, Nelson DB (1994) ARCH models. Handb Econ 4:2959–3038

    MathSciNet  Google Scholar 

  • Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. John Wiley & Sons

    Google Scholar 

  • Brockwell PJ, Davis RA (2016) Introduction to time series and forecasting. Springer, New York

    Book  Google Scholar 

  • Chakrabortty R, Pal SC, Ruidas D, Roy P, Saha A, Chowdhuri I (2023) Living with floods using state-of-the-art and geospatial techniques: flood mitigation alternatives, management measures, and policy recommendations. Water 15:558

    Article  Google Scholar 

  • Chatfield C, Xing H (2019) The analysis of time series: an introduction with R. CRC Press

    Book  Google Scholar 

  • Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation arXiv preprint arXiv:1406.1078

  • Chowdhuri I, Pal SC, Chakrabortty R (2020) Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res 65:1466–1489

    Article  ADS  Google Scholar 

  • Chu H, Wu W, Wang QJ, Nathan R, Wei J (2020) An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions. Environ Modell Softw 124:104587

    Article  Google Scholar 

  • Cui Z, Zhou Y, Guo S, Wang J, Ba H, He S (2021) A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting. Hydrol Res 52:1436–1454

    Article  Google Scholar 

  • Dazzi S, Vacondio R, Mignosa P (2021) Flood stage forecasting using machine-learning methods: a case study on the Parma River (Italy). Water 13:1612

    Article  Google Scholar 

  • Ding Y, Zhu Y, Feng J, Zhang P, Cheng Z (2020) Interpretable spatio-temporal attention LSTM model for flood forecasting. Neurocomputing 403:348–359

    Article  Google Scholar 

  • Du S, Van Rompaey A, Shi P, Ja W (2015) A dual effect of urban expansion on flood risk in the Pearl River Delta (China) revealed by land-use scenarios and direct runoff simulation. Nat Hazards 77:111–128

    Article  Google Scholar 

  • Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18:602–610

    Article  PubMed  Google Scholar 

  • Guo Z, Leitão JP, Simões NE, Moosavi V (2021) Data-driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks. J Flood Risk Manag 14:e12684

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Article  CAS  PubMed  Google Scholar 

  • Hofmann J, Schüttrumpf H (2021) floodGAN: Using deep adversarial learning to predict pluvial flooding in real time. Water 13:2255

    Article  Google Scholar 

  • Hosseiny H (2021) A deep learning model for predicting river flood depth and extent. Environ Modell Softw 145:105186

    Article  Google Scholar 

  • Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts

  • Imteaz MA, Hossain I (2023) Climate change impacts on ‘seasonality index’and its potential implications on rainwater savings. Water Resour Manag 37:2593–2606

    Article  Google Scholar 

  • Jhong BC, Wang JH, Lin GF (2017) An integrated two-stage support vector machine approach to forecast inundation maps during typhoons. J Hydrol 547:236–252

    Article  Google Scholar 

  • Kang H, Yang S, Huang J, Oh J (2020) Time series prediction of wastewater flow rate by bidirectional LSTM deep learning. Int J Control Autom Syst 18:3023–3030

    Article  Google Scholar 

  • Kao IF, Liou JY, Lee MH, Chang FJ (2021) Fusing stacked autoencoder and long short-term memory for regional multistep-ahead flood inundation forecasts. J Hydrol 598:126371

    Article  Google Scholar 

  • Kao IF, Zhou Y, Chang LC, Chang FJ (2020) Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting. J Hydrol 583:124631

    Article  Google Scholar 

  • Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrol Earth Syst Sci 22:6005–6022

    Article  ADS  Google Scholar 

  • Meng M, Dąbrowski M, Tai Y, Stead D, Chan F (2019) Collaborative spatial planning in the face of flood risk in delta cities: A policy framing perspective. Environ Sci Policy 96:95–104

    Article  Google Scholar 

  • Najafabadipour A, Kamali G, Nezamabadi-Pour H (2022) Application of artificial intelligence techniques for the determination of groundwater level using spatio–temporal parameters. ACS Omega 7:10751–10764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nanda T, Sahoo B, Beria H, Chatterjee C (2016) A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products. J Hydrol 539:57–73

    Article  Google Scholar 

  • Nearing GS, Klotz D, Frame JM et al (2022) Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks. Hydrol Earth Syst Sci 26:5493–5513

    Article  ADS  Google Scholar 

  • Palmitessa R, Mikkelsen PS, Borup M, Law AW (2021) Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations. J Hydro-Environ Res 38:106–116

    Article  Google Scholar 

  • Plate EJ (2007) Early warning and flood forecasting for large rivers with the lower Mekong as example. J Hydro-Environ Res 1:80–94

    Article  Google Scholar 

  • Roy P, Pal SC, Chakrabortty R, Chowdhuri I, Malik S, Das B (2020) Threats of climate and land use change on future flood susceptibility. J Clean Prod 272:122757

    Article  Google Scholar 

  • Ruidas D, Chakrabortty R, Islam ARMT, Saha A, Pal SC (2022) A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed. Eastern India Environ Earth Sci 81:145

    Article  ADS  Google Scholar 

  • Ruidas D, Saha A, Islam ARMT, Costache R, Pal SC (2023) Development of geo-environmental factors controlled flash flood hazard map for emergency relief operation in complex hydro-geomorphic environment of tropical river, India. Environ Sci Pollut Res 30:106951–106966

    Article  Google Scholar 

  • Schuetze T, Chelleri L (2013) Integrating decentralized rainwater management in urban planning and design: Flood resilient and sustainable water management using the example of coastal cities in the Netherlands and Taiwan. Water 5:593–616

    Article  Google Scholar 

  • Sit M, Demiray BZ, Xiang Z, Ewing GJ, Sermet Y, Demir I (2020) A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 82:2635–2670

    Article  PubMed  Google Scholar 

  • Sun W, Bocchini P, Davison BD (2020) Applications of artificial intelligence for disaster management. Nat Hazards 103:2631–2689

    Article  Google Scholar 

  • Vatanchi SM, Etemadfard H, Maghrebi MF, Shad R (2023) A comparative study on forecasting of long-term daily streamflow using ANN, ANFIS, BiLSTM, and CNN-GRU-LSTM. Water Resour Manag 37:4769–4785

    Article  Google Scholar 

  • Wu J, Wang Z, Hu Y, Tao S, Dong J (2023) Runoff forecasting using convolutional neural networks and optimized bi-directional long short-term memory. Water Resour Manag 37:937–953

    Article  Google Scholar 

  • Xie H, Randall M, Chau K-w (2022) Green roof hydrological modelling with GRU and LSTM networks. Water Resour Manag 36:1107–1122

    Article  Google Scholar 

  • Yang SY, Jhong BC, Jhong YD, Tsai TT, Chen CS (2023) Long short-term memory integrating moving average method for flood inundation depth forecasting based on observed data in urban area. Nat Hazards 116:2339–2361

    Google Scholar 

  • Yang S, Yang D, Chen J, Zhao B (2019) Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model. J Hydrol 579:124229

    Article  Google Scholar 

  • Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: The state of the art. Int J Forecast 14:35–62

    Article  CAS  Google Scholar 

  • Zhu S, Luo X, Yuan X, Xu Z (2020) An improved long short-term memory network for streamflow forecasting in the upper Yangtze River. Stoch Environ Res Risk Assess 34:1313–1329

    Article  Google Scholar 

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Acknowledgements

We acknowledge the National Science and Technology Council of Taiwan (Grant numbers: MOST 109-2625-M-035-007-MY3 and NSTC 112-2625-M-011-001 -) for granting support. The authors thank the Water Resources Planning Branch, Water Resources Agency, Ministry of Economic Affairs for providing relevant data and Mr. George Chih-Yu Chen for his assistance in the English editing. The authors also thank Tsung-Tang Tsai for his invaluable work developing AI models. During the preparation of this work the authors used GPT-4 in order to English editing.

Funding

This research project is funded by the National Science and Technology Council, Taiwan (grant numbers: MOST 109–2625-M-035–007-MY3 and NSTC 112–2625-M-011–001 -).

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Authors and Affiliations

  1. Department of Urban Planning and Spatial Information, Feng Chia University, Taichung, 407102, Taiwan

    Song-Yue Yang & Yun-Yang Lin

  2. Department of Water Resources Engineering and Conservation, Feng Chia University, Taichung, 407102, Taiwan

    You-Da Jhong

  3. Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, 106335, Taiwan

    Bing-Chen Jhong

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  1. Song-Yue Yang
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  2. You-Da Jhong
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  4. Yun-Yang Lin
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Contributions

Song-Yue Yang: Conceptualization, Methodology, Visualization, Writing – original draft, Funding acquisition; You-Da Jhong: Writing – review & editing; Bing-Chen Jhong: Conceptualization, Supervision, Writing – review & editing; Yun-Yang Lin: Data curation, Software.

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Correspondence to Bing-Chen Jhong.

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Yang, SY., Jhong, YD., Jhong, BC. et al. Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method. Water Resour Manage 38, 1359–1380 (2024). https://doi.org/10.1007/s11269-023-03725-4

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