Research on classified real-time flood forecasting framework based on K-means cluster and rough set

2015 ◽  
Vol 71 (10) ◽  
pp. 1507-1515 ◽  
Author(s):  
Wei Xu ◽  
Yong Peng
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Rough Set ◽  
Hydrological Model ◽  
Temporal Distribution ◽  
Flood Forecasting ◽  
Spatial And Temporal Distribution ◽  
Time Variance ◽  
Forecasting Method ◽  
Conceptual Hydrological Model

This research presents a new classified real-time flood forecasting framework. In this framework, historical floods are classified by a K-means cluster according to the spatial and temporal distribution of precipitation, the time variance of precipitation intensity and other hydrological factors. Based on the classified results, a rough set is used to extract the identification rules for real-time flood forecasting. Then, the parameters of different categories within the conceptual hydrological model are calibrated using a genetic algorithm. In real-time forecasting, the corresponding category of parameters is selected for flood forecasting according to the obtained flood information. This research tests the new classified framework on Guanyinge Reservoir and compares the framework with the traditional flood forecasting method. It finds that the performance of the new classified framework is significantly better in terms of accuracy. Furthermore, the framework can be considered in a catchment with fewer historical floods.

Application of K-Means Cluster and Rough Set in Classified Real-Time Flood Forecasting

2015 ◽  
Vol 1092-1093 ◽  
pp. 734-741
Author(s):  
Heng Qing Wu ◽  
Qiang Huang ◽  
Wei Xu ◽  
Shu Feng Xi
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Rough Set ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Model Parameters ◽  
Operation Rules ◽  
Conceptual Hydrological Model

A new classified real-time flood forecasting framework was presented. Firstly, the historical floods were classified by K-means cluster, according to the hydrological factors. Then rough set was used to extract operation rules for flood forecasting. Following, the conceptual hydrological model was constructed and Genetic Algorithm (GA) was used to calibrate the hydrological model parameters. In simulation, River A is taken as study example. The categories of parameters are selected in operation according to flood information and rules. The result is compared with traditional flood forecasting. It demonstrates the performance of classified framework is improved in terms of accuracy and reliability.

scholarly journals Hydrological Evaluation of Satellite Soil Moisture Data in Two Basins of Different Climate and Vegetation Density Conditions

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Lu Zhuo ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Spatial Difference ◽  
Vegetation Density ◽  
Modis Ndvi ◽  
Basin Scale ◽  
Microwave Sensors ◽  
Validation Tool

Accurate soil moisture information is very important for real-time flood forecasting. Although satellite soil moisture observations are useful information, their validations are generally hindered by the large spatial difference with the point-based measurements, and hence they cannot be directly applied in hydrological modelling. This study adopts a widely applied operational hydrological model Xinanjiang (XAJ) as a hydrological validation tool. Two widely used microwave sensors (SMOS and AMSR-E) are evaluated, over two basins (French Broad and Pontiac) with different climate types and vegetation covers. The results demonstrate SMOS outperforms AMSR-E in the Pontiac basin (cropland), while both products perform poorly in the French Broad basin (forest). The MODIS NDVI thresholds of 0.81 and 0.64 (for cropland and forest basins, resp.) are very effective in dividing soil moisture datasets into “denser” and “thinner” vegetation periods. As a result, in the cropland, the statistical performance is further improved for both satellites (i.e., improved to NSE = 0.74, RMSE = 0.0059 m and NSE = 0.58, RMSE = 0.0066 m for SMOS and AMER-E, resp.). The overall assessment suggests that SMOS is of reasonable quality in estimating basin-scale soil moisture at moderate-vegetated areas, and NDVI is a useful indicator for further improving the performance.

scholarly journals Accuracy assessment of real-time flood forecasting of coupled hydrological and mesoscale meteorological models

2018 ◽  
Author(s):  
Aida Jabbari ◽  
Jae-Min So ◽  
Deg-Hyo Bae
Keyword(s):  
Real Time ◽  
Spatial Resolution ◽  
Lead Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Atmospheric Model ◽  
Flood Forecasting ◽  
Coupled Models ◽  
Temporal And Spatial ◽  
Meteorological Models

Abstract. Hydro-meteorological predictions are important for water management plans, which include providing early flood warnings and preventing flood damages. This study evaluates the real-time precipitation of an atmospheric model at the point and catchment scales to select the proper hydrological model to couple with the atmospheric model. Furthermore, a variety of tests were conducted to quantify the accuracy assessments of coupled models to provide details on the maximum spatial and temporal resolutions and lead times in a real-time forecasting system. As a major limitation of previous studies, the temporal and spatial resolutions of the hydrological model are smaller than those of the meteorological model. Here, through ultra-fine scale of temporal (10 min) and spatial resolution (1 km × 1 km), we determined the optimal resolution. A numerical weather prediction model and a rainfall runoff model were employed to evaluate real-time flood forecasting for the Imjin River (South and North Korea). The comparison of the forecasted precipitation and the observed precipitation indicated that the Weather Research and Forecasting (WRF) model underestimated precipitation. The skill of the model was relatively higher for the catchment than for the point scale, as illustrated by the lower RMSE value, which is important for a semi-distributed hydrological model. The variations in temporal and spatial resolutions illustrated a decrease in accuracy; additionally, the optimal spatial resolution obtained at 8 km and the temporal resolution did not affect the inherent inaccuracy of the results. Lead time variation demonstrated that lead time dependency was almost negligible below 36 h. With reference to our case study, comparisons of model performance provided quantitative knowledge for understanding the credibility and restrictions of hydro-meteorological models.

Use of Radar Quantitative Precipitation Estimates (QPEs) for Improved Hydrological Model Calibration and Flood Forecasting

2021 ◽  
Author(s):  
Dayal Wijayarathne ◽  
Paulin Coulibaly ◽  
Sudesh Boodoo ◽  
David Sills
Keyword(s):  
Real Time ◽  
Model Calibration ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates ◽  
Streamflow Simulation ◽  
Rainfall Estimates

AbstractFlood forecasting is essential to minimize the impacts and costs of floods, especially in urbanized watersheds. Radar rainfall estimates are becoming increasingly popular in flood forecasting because they provide the much-needed real-time spatially distributed precipitation information. The current study evaluates the use of radar Quantitative Precipitation Estimates (QPEs) in hydrological model calibration for streamflow simulation and flood mapping in an urban setting. Firstly, S-band and C-band radar QPEs were integrated into event-based hydrological models to improve the calibration of model parameters. Then, rain gauge and radar precipitation estimates’ performances were compared for hydrological modeling in an urban watershed to assess radar QPE's effects on streamflow simulation accuracy. Finally, flood extent maps were produced using coupled hydrological-hydraulic models integrated within the Hydrologic Engineering Center- Real-Time Simulation (HEC-RTS) framework. It is shown that the bias correction of radar QPEs can enhance the hydrological model calibration. The radar-gauge merging obtained a KGE, MPFC, NSE, and VE improvement of about + 0.42, + 0.12, + 0.78, and − 0.23, respectively for S-band and + 0.64, + 0.36, + 1.12, and − 0.34, respectively for C-band radar QPEs. Merged radar QPEs are also helpful in running hydrological models calibrated using gauge data. The HEC-RTS framework can be used to produce flood forecast maps using the bias-corrected radar QPEs. Therefore, radar rainfall estimates could be efficiently used to forecast floods in urbanized areas for effective flood management and mitigation. Canadian flood forecasting systems could be efficiently updated by integrating bias-corrected radar QPEs to simulate streamflow and produce flood inundation maps.

scholarly journals Flood Forecasting with Uncertainty Using a Fully Automated Flood Model Chain: a Case Study for the City of Kulmbach

10.29007/jb27 ◽  
2018 ◽  
Author(s):  
Md Nazmul Azim Beg ◽  
Jorge Leandro ◽  
Punit Bhola ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Flood Forecast ◽  
The Public ◽  
Uncertainty Sources ◽  
Flood Model ◽  
The City ◽  
Model Chain

Real time flood forecasting can help authorities in providing reliable warnings to the public. This process is, however, non-deterministic such that uncertainty sources need to be accounted before issuing forecasts. In the FloodEvac project, we have developed a tool which takes as inputs rainfall forecasts and links a hydrological with a hydraulic model for producing flood forecasts. The tool is able to handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecast with associated uncertainty of flood discharges and flood extents. In this case study, we focus on the linkage with the hydrological model and on the real-time discharge forecasts generated.

scholarly journals Verification of operational Quantitative Discharge Forecast (QDF) for a regional warning system – the AMPHORE case studies in the upper Po River

2008 ◽  
Vol 8 (1) ◽  
pp. 161-173 ◽  
Author(s):  
D. Rabuffetti ◽  
G. Ravazzani ◽  
C. Corbari ◽  
M. Mancini
Keyword(s):  
Real Time ◽  
Case Studies ◽  
Hydrological Model ◽  
Lag Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Distributed Hydrological Model ◽  
Po River ◽  
Prediction And Prevention ◽  
Small Catchments

Abstract. In recent years, the interest in the prediction and prevention of natural hazards related to hydrometeorological events has grown due to the increased frequency of extreme rainstorms. Several research projects have been developed to test hydrometeorological models for real-time flood forecasting. However, flood forecasting systems are still not widespread in operational context. Real-world examples are mainly dedicated to the use of flood routing model, best suited for large river basins. For small basins, it is necessary to take advantage of the lag time between the onset of a rainstorm and the beginning of the hydrograph rise, with the use of rainfall-runoff transformation models. Nevertheless, when the lag time is very short, a rainfall predictor is required, as a result, meteorological models are often coupled with hydrological simulation. While this chaining allows floods to be forecasted on small catchments with response times ranging from 6 to 12 h it, however, causes new problems for the reliability of Quantitative Precipitation Forecasts (QPF) and also creates additional accuracy problems for space and time scales. The aim of this work is to evaluate the degree to which uncertain QPF affects the reliability of the whole hydro-meteorological alert system for small catchments. For this purpose, a distributed hydrological model (FEST-WB) was developed and analysed in operational setting experiments, i.e. the hydrological model was forced with rain observation until the time of forecast and with the QPF for the successive period, as is usual in real-time procedures. Analysis focuses on the AMPHORE case studies in Piemonte in November 2002.

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