Local and Global Bayesian Network based Model for Flood Prediction

Wu, Yirui; Xu, Weigang; Fengt, Jun; Palaiahnakote, Shivakumara; Lü, Tong

doi:10.1109/icpr.2018.8546257

Cited by 14 publications

(9 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that we apply linear kernel function for [40] during experiments. Wu et al [41] construct entities and connections of Bayesian network to represent variables and physical processes of a famous physical model, which appropriately embeds hydrology expert knowledge for high rationality and robustness. Dawson et al [42] develop Artificial Neural Networks (ANNs) for 6 h lead times flow forecasting using real hydrometric data.…”

Section: Resultsmentioning

confidence: 99%

“…Chang et al [43] develop a two-stage rainfall runoff model for 3-h-ahead flood forecasting based on radial basis function (RBF) neural network, which firstly utilize fuzzy min-max clustering to determine the characteristics of the nonlinear RBFs and then adopt multivariate linear regression to determine the weights between the hidden and output layers. Above all, the cores of Han et al [40], Dawson et al [42], Chang et al [43] , Lima et al [44], and Wu et al [41] are SVM, Neural Network, Radical Basis Function Network, Extreme Learning Machine, and Bayesian Network, respectively. All these machine learning structures are popular to predict floods in pattern recognition community.…”

Section: Resultsmentioning

confidence: 99%

“…The small value of RMSE by the proposed method implies our method is more accurate and robust to predict runoff values; meanwhile, large value of DC achieved implies our method quantify uncertainty to a certain extent. Wu et al [41] is more accurate in predicting the appearance time and runoff values of flood peaks than the proposed model, since it contains the embedded hydrology processes and variables to increase prior knowledge for accurate prediction of flood peaks. To sum up, both generating virtual samples and integrating classifiers help accurately predict floods even with imbalanced data.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

SMOTE-Boost-based sparse Bayesian model for flood prediction

Ding

Feng

2020

J Wireless Com Network

Self Cite

View full text Add to dashboard Cite

show abstract

“…Following such idea, Biondi et al [18] firstly simulate the hydrologic response by a rainfall-run-off model named as Infiltration and Saturation Excess (RISE) and then utilize the extracted 2 Complexity hydrological information for later deterministic Bayesian Forecasting. Recently, Wu et al [5] successfully transformed hydrological process described by Xinanjiang model into entities and connections of Bayesian network, which offers a solution to integrate expert knowledge in a data-driven model. In order to offer a task-specified computing service, data-driven models nowadays have been developed accompany with Internet of things [19,20], cloud-edge computing [21][22][23], big data [24,25], and other technologies [26][27][28].…”

Section: Data-driven Model For Floodmentioning

confidence: 99%

“…To minimize impacts brought by floods, researchers have proposed quantity of methods to accurate forecasting in the past decade [1,2]. Based on core ideas to forecast, we divide their proposed methods into two categories: physical model [3,4] and data-driven model [5,6]. Physical model explains hydrological procedures with conceptual math equations, such as rain, evaporation, and flow concentration.…”

Section: Introductionmentioning

confidence: 99%

Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution

Ding

Zhu

et al. 2020

Complexity

Self Cite

View full text Add to dashboard Cite

With significant development of sensors and Internet of things, researchers nowadays can easily know what happens in physical space by acquiring time-varying values of various factors. Essentially, growing data category and size greatly contribute to solve problems happened in physical space. In this paper, we aim to solve a complex problem that affects both cities and villages, i.e., flood. To reduce impacts induced by floods, hydrological factors acquired from physical space and data-driven models in cyber space have been adopted to accurately forecast floods. Considering the significance of modeling attention capability among hydrology factors, we believe extraction of discriminative hydrology factors not only reflect natural rules in physical space, but also optimally model iterations of factors to forecast run-off values in cyber space. Therefore, we propose a novel data-driven model named as STA-LSTM by integrating Long Short-Term Memory (LSTM) structure and spatiotemporal attention module, which is capable of forecasting floods for small- and medium-sized rivers. The proposed spatiotemporal attention module firstly explores spatial relationship between input hydrological factors from different locations and run-off outputs, which assigns time-varying weights to various factors. Afterwards, the proposed attention module allocates temporal-dependent weights to hidden output of each LSTM cell, which describes significance of state output for final forecasting results. Taking Lech and Changhua river basins as cases of physical space, several groups of comparative experiments show that STA-LSTM is capable to optimize complexity of mathematically modeling floods in cyber space.

show abstract

Context and Temporal Aware Attention Model for Flood Prediction

Ding

Feng

et al. 2018

Advances in Multimedia Information Processing – PCM 2018

View full text Add to dashboard Cite

Local and Global Bayesian Network based Model for Flood Prediction

Cited by 14 publications

References 20 publications

SMOTE-Boost-based sparse Bayesian model for flood prediction

SMOTE-Boost-based sparse Bayesian model for flood prediction

Complexity to Forecast Flood: Problem Definition and Spatiotemporal Attention LSTM Solution

Context and Temporal Aware Attention Model for Flood Prediction

Contact Info

Product

Resources

About