Real-Time Runoff Forecasting Characteristics of Urban Storage Function Model Using Particle Filter

Takasaki, Tadakatsu; Kawamura, Akira; Amaguchi, Hideo; Ishihara, Shigeyuki

doi:10.2208/jscejhe.68.i_511

Cited by 3 publications

(1 citation statement)

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“…Parameter identification can be almost automated using global search methods like SCE-UA, and hydrograph reproducibility at the target point is generally higher than that of a distributed model. Takasaki et al [97,98] applied both the particle filter method and the Kalman filter method to real-time flood runoff forecasting using the USF model in the upper Kanda River watershed and obtained accurate forecasting results.…”

Section: Application Of the Usf Model In An Urban Watershedmentioning

confidence: 99%

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

Kawamura

Amaguchi

Olsson

et al. 2023

Water

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Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss, and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modeling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modeling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modeling and assessment in Japan.

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Section: Application Of the Usf Model In An Urban Watershedmentioning

confidence: 99%

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

Kawamura

Amaguchi

Olsson

et al. 2023

Water

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Status Quo and Perspectives of Flood Runoff Analysis for Urban Watersheds

Kawamura

2018

J. Japan Soc. Hydrol. and Water Resour.

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This paper outlines the current status of flood runoff analysis for urban watersheds and presents its future prospects. First, I will describe the urban flood disaster, then briefly review the history of the runoff models before explaining the current state of application of flood runoff models for urban watersheds. Next, storage function models as the lumped conceptual model are outlined, and the urban storage function (USF) model is explained. Then, the necessity of a detailed distributed physical model in urban watershed is described by explaining the difference between mesh data and the urban landscape GIS delineation. I introduce TSR model which is a precise distributed physical model utilizing the urban landscape GIS delineation. Finally, I overlook the future issues to be solved for lumped conceptual and distributed physical models as well as making some recommendations for reduction of urban flood disaster.

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Real-Time Dam Inflow Forecasting Accuracy Improved by Internet of Things

Amakata¹,

Yasuno²,

Fujii³

et al. 2019

J. Japan Soc. Hydrol. and Water Resour.

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We have used a set of predictive rainfall and analytical models as main methods when predicting water levels or discharge levels. However, available flood forecasting technologies are inadequate and have not reached general utilization through practical operation. An important point is that fewer observation stations and less information exist for models in dam basins than for river management segments under dams. Therefore, predicting water levels or discharge levels based on physical models is difficult. As an alternative, the Internet of Things(IoT) is supported by the improvement and lessening costs of sensors and network technologies. Therefore, we set sensors artificially in the Digital Twin (A digital twin is a digital replica of a living or non-living physical entity. By bridging the physical and the virtual world, data is transmitted seamlessly allowing the virtual entity to exist simultaneously with the physical entity.(Saddik,2018)) basin produced using the distributed flow analysis model and using Long Short-Term Memory(LSTM), a sort of deep learning to confirm the accuracy of dam inflow forecasts obtained with different numbers and locations of sensors. Results demonstrate that the predictive precision fundamentally improves with increasing the number of sensors but that it doesn't improve with sensors which generate biased data. Furthermore, we can infer a sensor number limit. Results show that adequate sensor location improves the predictive precision.

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Real-Time Runoff Forecasting Characteristics of Urban Storage Function Model Using Particle Filter

Cited by 3 publications

References 1 publication

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

Status Quo and Perspectives of Flood Runoff Analysis for Urban Watersheds

Real-Time Dam Inflow Forecasting Accuracy Improved by Internet of Things

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