Comparison of a fuzzy control and the data-driven model for flood forecasting

Sun, Ying; De-shan, Tang; Sun, Yifei; Cui, Qingfeng

doi:10.1007/s11069-016-2220-5

Cited by 11 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation performance of the two models on great floods is shown in Table 7. 1 The average value in Table 7 is obtained from the qualified flood events.…”

Section: Comparison Of Simulation Results Of Floods Under Different Gradesmentioning

confidence: 99%

“…In recent years, with the improvement of hydrological data acquisition technology and the development of artificial intelligence computing, data-driven flood forecasting models have gained increasing attention [1][2][3][4][5][6][7][8]. The development of intelligent computing has gone through three important stages.…”

Section: Introductionmentioning

confidence: 99%

“…According to the current research status, scholars have achieved some research achievements in the flood forecasting model and flood simulation in medium and small watershed of the loess region, but there are still some issues that need to be clarified: (1) The research on flood forecasting in medium and small watershed in the loess region mostly concentrates on statistical methods and process-driven hydrological models, and most of the existing data-driven flood forecasting models do not consider the impact of underlying surface changes. The research results reflecting the impact of the underlying surface of medium and small watershed in the loess region on flooding and the flood forecasting are relatively scare; (2) There are several articles in the literature on flood forecasting using a single machine learning model, but in the actual flood forecasting process, there is a lack of single machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region

Ren

Zhang

et al. 2019

Water

View full text Add to dashboard Cite

Sudden floods in the medium and small watershed by a sudden rainstorm and locally heavy rainfall often lead to flash floods. Therefore, it is of practical and theoretical significance to explore appropriate flood forecasting model for medium and small watersheds for flood control and disaster reduction in the loess region under the condition of underlying surface changes. This paper took the Gedong basin in the loess region of western Shanxi as the research area, analyzing the underlying surface and floods characteristics. The underlying surface change was divided into three periods (HSP1, HSP2, HSP3), and the floods were divided into three grades (great, moderate, small). The paper applied K Nearest Neighbor method and Fireworks Algorithm to improve the Extreme Learning Machine model (KNN-FWA-ELM) and proposed KNN-FWA-ELM hybrid flood forecasting model, which was further applied to flood forecasting of different underlying surface conditions and flood grades. Results demonstrated that KNN-FWA-ELM model had better simulation performance and higher simulation accuracy than the ELM model for flood forecasting, and the qualified rate was 17.39% higher than the ELM model. KNN-FWA-ELM model was superior to the ELM model in three periods and the simulation performance of three flood grades, and the simulation performance of KNN-FWA-ELM model was better in HSP1 stage floods and great floods.

show abstract

“…The simulation performance of the two models on great floods is shown in Table 7. 1 The average value in Table 7 is obtained from the qualified flood events.…”

Section: Comparison Of Simulation Results Of Floods Under Different Gradesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region

Ren

Zhang

et al. 2019

Water

View full text Add to dashboard Cite

show abstract

“…The NSE of the predictive results is 0.9747. Compared with physical-based models, data-driven models demand fewer data and computational resources, rendering them more accessible to implement (Das et al 2016;Sun et al 2016). Currently, both the physical modeling approach and the data-driven approach are widely applied in water level prediction.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Reservoir Water Level by Remote Sensing and Deep Learning

Jin,

Liu,

Huang

2024

Preprint

View full text Add to dashboard Cite

Accurate reservoir water level prediction is crucial for the safe operation of reservoirs and the utilization of their functions. Traditional physical-based water level forecasting methods rely heavily on auxiliary data, such as precipitation and reservoir outflow. However, obtaining timely and reliable data on reservoir discharge flow can be expensive and impractical due to limitations in infrastructure or data accessibility. To address this issue, the current study utilized multi-source remote sensing data to extract a time series of reservoir storage volume. A technical framework for predicting water levels using deep learning models and remote sensing technology was proposed. To validate the effectiveness of this method, we compared the predictive accuracy of reservoir water levels among 16 different machine learning input scenarios. The results indicate that the model incorporating water level, rainfall, water surface area, and daily changes in reservoir storage volume as input data performed the best. Compared to the input data that did not consider water surface area and daily changes in reservoir storage, it demonstrated higher accuracy, with an increase in R2 value by 1.13%, RMSE increased by 52.17%, and MAE increased by 63.80%. The framework proposed in this study offers a reliable method for predicting reservoir water levels in the absence of operational data for reservoirs.

show abstract