Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

Chang, Fi‐John; Chen, Pin-An; Lu, Ying-Ray; Huang, Eric Yi-Hsiu; Chang, Kai-Shiun

doi:10.1016/j.jhydrol.2014.06.013

Cited by 211 publications

(101 citation statements)

References 35 publications

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“…Flooding is a natural disaster that causes damage to human lives, as well as the economy [3]. However, compared with plain catchments [4][5][6][7][8], less effort has been made in mitigating the flooding risks of polders [9][10][11]. Therefore, investigating measures to control polder flooding problems is of a significant value.…”

Section: Introductionmentioning

confidence: 99%

Application of CityDrain3 in Flood Simulation of Sponge Polders: A Case Study of Kunshan, China

Wei

Urich

Liu

et al. 2018

Water

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Abstract:The selection of sponge city facilities (e.g., pump, storage tank, wetland, or bioretention pond) to mitigate urban floods has been a crucial issue in China. This study aims to develop a conceptual flood-simulation model, which can take into account the effects of such facilities of a sponge city. Taking Jiangpu polder in Kunshan City as a case study, CityDrain3 was implemented to develop a baseline model and another three sponge polder models (pump only, storage tank only, pump, and storage tank). A sensitivity analysis was carried out to guarantee the robustness of the newly developed model. In the model application part, firstly, one-hour rainfall scenarios with different return periods (2a, 5a, 10a, 20a, 50a, 100a, with 'a' referring to a year) were employed as inputs to the conceptual baseline model. The growing trend of flood depth (from 12.69 mm to 17.16 mm) simulated by the baseline model under increased return periods (from 3a to 100a) demonstrated the feasibility of polder flood simulations using CityDrain3. Secondly, a one-hour rainfall scenario with a 10-year return period was employed on the baseline model and the three sponge polder models. The results showed that the effect rankings of the control strategies on the total flood volume, peak flow, flood yielding time, and the peak-flow occurrence time were comparable-combined strategies (pump and storage tank) > storage tank only > pump only. The conceptual, and hydrological model developed in this study can serve as a simulation tool for implementing a real-time urban storm water drainage control system in the Jiangpu polder.

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Section: Introductionmentioning

confidence: 99%

Application of CityDrain3 in Flood Simulation of Sponge Polders: A Case Study of Kunshan, China

Wei

Urich

Liu

et al. 2018

Water

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“…However, when SOM features were used, the lead time for very satisfactory forecasts increased to 5 days, due to the more comprehensive information of the time series data was extracted, revealing the important role of optimizer algorithms for better accuracy. Chang et al (2014) developed two neural networks including static and dynamic. In the first stage of the study, the historical hydrologic data are fully explored by statistical techniques to identify the time span of rainfall affecting the rise of the water level in the flood water storage pond (FSP) at the gauging station.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Yaseen

Deo

Ebtehaj

et al. 2018

Advances in Computational Intelligence and Robotics

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“…In recent years, the utility of ANN models in simulating complex hydrological conditions has provided various opportunities for flood prediction by focusing on the water level in inundation areas [19], peak flow prediction in urban areas [20], water levels in areas having insufficient data [21], and conditions of irregular rainfall and runoff [22].…”

Section: Introductionmentioning

confidence: 99%

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

Sung

Lee

Chung

et al. 2017

Water

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This study develops hourly water level forecasting models with lead-times of 1 to 3 h using an artificial neural network (ANN) for Anyangcheon stream, one of the major tributaries of the Han River, South Korea. To consider the backwater effect from this river, an enhanced tributary water level forecasting model is proposed by adding multiple water level data on the main river as input variables into the conventional ANN structure which often uses rainfall and upstream water level data. Four types of ANN models per each lead-time are built with increasing complexity of the input vector, and their performances are compared. The results indicate that the inclusion of multiple water level data on the main river to the network provides water level forecasts with greater accuracy at the Ogeumgyo gauging station of interest. The final best ANN models for water level forecasts with lead-times of 1 to 2 h show good performance with root mean square errors (RMSE) below 0.06 m and 0.12 m, respectively. However, the final best ANN model for forecasting 3 h ahead was unsatisfactory, showing underestimation at many rising parts of the hydrograph.

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Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control

Cited by 211 publications

References 35 publications

Application of CityDrain3 in Flood Simulation of Sponge Polders: A Case Study of Kunshan, China

Application of CityDrain3 in Flood Simulation of Sponge Polders: A Case Study of Kunshan, China

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Hourly Water Level Forecasting at Tributary Affected by Main River Condition

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