A comparison of SAC-SMA and Adaptive Neuro-fuzzy Inference System for real-time flood forecasting in small urban catchments

Babak K. Roodsari, David G Chandler, Christa Kelleher, Charles N. Kroll

Research output: Contribution to journalArticle

Abstract

Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real-time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC-SMA) and an adaptive neuro-fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24-hr lead times show strong performance for SAC-SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.

Original languageEnglish (US)
Article numbere12492
JournalJournal of Flood Risk Management
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

flood forecasting
Fuzzy inference
Catchments
natural disaster
catchment
Hurricanes
extreme event
hurricane
performance
event
prediction
Precipitation (meteorology)
peak flow
comparison
time
hydrograph
Rain
streamflow
urbanization
flooding

Keywords

  • ANFIS
  • ensemble flood forecasting
  • hurricane Irene
  • hydrologic modelling
  • imperviousness
  • runoff peak flows
  • SAC-SMA
  • urbanisation

ASJC Scopus subject areas

  • Environmental Engineering
  • Geography, Planning and Development
  • Safety, Risk, Reliability and Quality
  • Water Science and Technology

Cite this

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title = "A comparison of SAC-SMA and Adaptive Neuro-fuzzy Inference System for real-time flood forecasting in small urban catchments",
abstract = "Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real-time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC-SMA) and an adaptive neuro-fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24-hr lead times show strong performance for SAC-SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.",
keywords = "ANFIS, ensemble flood forecasting, hurricane Irene, hydrologic modelling, imperviousness, runoff peak flows, SAC-SMA, urbanisation",
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AU - Roodsari, Babak K.

AU - Chandler, David G

AU - Kelleher, Christa

AU - Kroll, Charles N.

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N2 - Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real-time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC-SMA) and an adaptive neuro-fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24-hr lead times show strong performance for SAC-SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.

AB - Growing urbanisation and imperviousness have augmented magnitudes of peak flows, resulting in flooding especially during extreme events. Flood forecast of extreme events can rely on real-time ensemble flood forecasting systems. Such systems often use predictions from physical models and precipitation ensembles to predict downstream urban flood hydrographs. However, these methods are seldom used in small catchments, where flood predictions may assist emergency management. We explore the relative utility of two models, the Sacramento Model (SAC-SMA) and an adaptive neuro-fuzzy inference system (ANFIS) for ensemble flood prediction for nine small urban catchments located near New York City. The models were used to reforecast streamflow for Hurricane Irene (160 mm) and a 35 mm storm across lead times from 3 to 24 hr. Differences in performance between models were small for short (3 hr) lead times, and were similar for the 35 mm storm. Reforecasts of hurricane Irene at 24-hr lead times show strong performance for SAC-SMA, but a decline in performance for ANFIS. Model performance did not vary systematically with either catchment size or imperviousness. Our results suggest that model selection is especially important when reforecasting large rain events with longer lead times in small urban catchments.

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