The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method

Wang, Wenchuan; Chau, Kwok Wing; Xu, Dianxiang; Qiu, Lin; Liu, Can can

doi:10.1007/s11269-016-1538-9

Cited by 45 publications

(22 citation statements)

References 28 publications

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“…The accuracy of 3B42V7 and CMORPH at the monthly scale is higher, and the accuracy of 3B42V7 is higher than that of CMORPH. The monthly rainfall data from satellite-based rainfall products can be parameters that are input to the hydrological model and data assimilation [51][52][53][54]. In the future, we will integrate the two evaluation methods and deeply analyze the calculation of different error statistics to elucidate insights into rainfall detection.…”

Section: Discussionmentioning

confidence: 99%

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Jiang

Wen

et al. 2018

Water

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Satellite-based rainfall products have extensive applications in global change studies, but they are known to contain deviations that require comprehensive verification at different scales. In this paper, we evaluated the accuracies of two high-resolution satellite-based rainfall products: the Tropical Rainfall Measurement Mission (TRMM) rainfall product 3B42V7 and the Climate Prediction Center morphing (CMORPH) technique from January 2010 to December 2011 in Shanghai, by using categorical metrics (Probability of Detection, False Alarm Ratio, and Critical Success Index) and statistical indicators (Mean Absolute Error, Root Mean Square Error, Relative Bias, and Correlation Coefficient). Our findings show that, firstly, CMORPH data has a higher accuracy than 3B42V7 at the daily scale, but it underestimates the occurrence frequency of daily rainfall for some intensity ranges. Most errors of the two products are distributed between −10 and 10 mm/day. Second, at the monthly scale, the total accuracy of 3B42V7 is higher than CMORPH in terms of the value of the Correlation Coefficient (CC) and Relative Bias (RB). Finally, CMORPH brings about daily rainfall detection results from categorical metrics computation better than 3B42V7. Generally, the two satellite-based rainfall products show a high correlation with rain gauge data in Shanghai, particularly in spring and winter. Unfortunately, in summer, both of them do not perform well in detecting the short-duration heavy rainfall events. Overall, the relatively poor data accuracy has limited their further applications in Shanghai and similar urban areas.

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Section: Discussionmentioning

confidence: 99%

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Jiang

Wen

et al. 2018

Water

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“…It has been reported that these hybrid MLMs, which consists of time series decomposition and sub-time series modeling, were able to achieve better performance compared with the single MLMs. Finally, the hybrid MLMs, combined with more than two methods, have been developed for rainfall-runoff and streamflow modeling including DWT, PSO, and SVMs [45]; DWT, GA, and adaptive neuro-fuzzy inference system (ANFIS) [46]; EEMD, PSO, and SVMs [47]; EEMD, SOM, and linear genetic programming [48]; wavelet transform, singular spectrum, chaotic approach, and SVR [49]; and Hermite-projection pursuit regression, social spider optimization, and least square algorithm [50].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

2018

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Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for daily rainfall-runoff modeling. These hybrid MLMs consist of VMD-based extreme learning machine (VMD-ELM) and VMD-based least squares support vector regression (VMD-LSSVR). The VMD is employed to decompose original input and target time series into sub-time series called intrinsic mode functions (IMFs). The ELM and LSSVR models are selected for developing daily rainfall-runoff models utilizing the IMFs as inputs. The performances of VMD-ELM and VMD-LSSVR models are evaluated utilizing efficiency and effectiveness indices. Their performances are also compared with those of VMD-based artificial neural network (VMD-ANN), discrete wavelet transform (DWT)-based MLMs (DWT-ELM, DWT-LSSVR, and DWT-ANN) and single MLMs (ELM, LSSVR, and ANN). As a result, the VMD-based MLMs provide better accuracy compared with the single MLMs and yield slightly better performance than the DWT-based MLMs. Among all models, the VMD-ELM and VMD-LSSVR models achieve the best performance in daily rainfall-runoff modeling with respect to efficiency and effectiveness. Therefore, the VMD-ELM and VMD-LSSVR models can be an alternative tool for reliable and accurate daily rainfall-runoff modeling.

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“…Coastal city case studies should employ longer timeframes to find risk areas. In longer timeframes, properties such as hourly, daily, and monthly precipitation can be employed to predict flood events with accuracy through data-driven and statistical methods [55][56][57][58][59]. A case study with longer timeframes can also demonstrate how risky developments increase, and it can also predict such developments in port hinterlands.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

The Effect of Coastal City Development on Flood Damage in South Korea

Park

Paterson²,

Zigmund³

et al. 2020

Sustainability

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Flood damage continues to be an issue in coastal cities. Impervious areas that contribute to flood damage are increasing due to the continuous development of ports in cities. However, previous research has not explored development in port hinterlands and in the coastal flood risk areas of coastal cities. Therefore, this study analyzed the impact of coastal city development on flood damage in Korea. A panel Tobit analysis was conducted on 58 coastal cities between 2002 and 2018. The results revealed that a 1% increase in impervious surfaces and one coastal development permit would increase damage costs by 1.29% and 2%, respectively. The analysis revealed that the increasing development of coastal cities had a significant impact on flood damage. The findings suggest that land-use plans highlight a conflict between port development and safety. This article provides insight that can be used by policy makers to manage risk areas near ports.

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The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method

Cited by 45 publications

References 28 publications

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Accuracy Evaluation of Two High-Resolution Satellite-Based Rainfall Products: TRMM 3B42V7 and CMORPH in Shanghai

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

The Effect of Coastal City Development on Flood Damage in South Korea

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