A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems

Ming-Jui Chang; Hsiang-Kuan Chang; Yun-Chun Chen; Gwo-Fong Lin; Peng-An Chen; Jihn-Sung Lai; Yih-Chi Tan

doi:10.3390/w10121734

A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems

Water ◽

10.3390/w10121734 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1734 ◽

Cited By ~ 12

Author(s):

Ming-Jui Chang ◽

Hsiang-Kuan Chang ◽

Yun-Chun Chen ◽

Gwo-Fong Lin ◽

Peng-An Chen ◽

...

Keyword(s):

Emergency Response ◽

Lead Time ◽

Clustering Algorithm ◽

Support Vector ◽

Forecasting Model ◽

Flood Inundation ◽

Inundation Maps ◽

Flood Warning ◽

Inundation Depth ◽

Proposed Model

Accurate real-time forecasts of inundation depth and extent during typhoon flooding are crucial to disaster emergency response. To manage disaster risk, the development of a flood inundation forecasting model has been recognized as essential. In this paper, a forecasting model by integrating a hydrodynamic model, k-means clustering algorithm and support vector machines (SVM) is proposed. The task of this study is divided into four parts. First, the SOBEK model is used in simulating inundation hydrodynamics. Second, the k-means clustering algorithm classifies flood inundation data and identifies the dominant clusters of flood gauging stations. Third, SVM yields water level forecasts with 1–3 h lead time. Finally, a spatial expansion module produces flood inundation maps, based on forecasted information from flood gauging stations and consideration of flood causative factors. To demonstrate the effectiveness of the proposed forecasting model, we present an application to the Yilan River basin, Taiwan. The forecasting results indicate that the simulated water level forecasts from the point forecasting module are in good agreement with the observed data, and the proposed model yields the accurate flood inundation maps for 1–3 h lead time. These results indicate that the proposed model accurately forecasts not only flood inundation depth but also inundation extent. This flood inundation forecasting model is expected to be useful in providing early flood warning information for disaster emergency response.

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Effective real-time forecasting of inundation maps for early warning systems during typhoons

MATEC Web of Conferences ◽

10.1051/matecconf/201814703014 ◽

2018 ◽

Vol 147 ◽

pp. 03014

Author(s):

Jhih-Huang Wang ◽

Gwo-Fong Lin ◽

Bing-Chen Jhong

Keyword(s):

Early Warning ◽

Lead Time ◽

Early Warning Systems ◽

Reference Points ◽

Support Vector ◽

Spatial Expansion ◽

Warning Systems ◽

Inundation Maps ◽

Inundation Depth ◽

Proposed Model

Accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation during typhoons. In this paper, an effective forecasting model is proposed to yield 1- to 6-h lead-time inundation maps for early warning systems during typhoons. The proposed model based on Support Vector Machine (SVM) is composed of two modules, point forecasting and spatial expansion. In the first module, the rainfall intensity, inundation depth, cumulative rainfall and forecasted inundation depths are considered as model input for point forecasting. In the second module, the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps for spatial expansion. The results show that the proposed model is able to provide accurate point forecasts at each inundation point. Moreover, the spatial expansion module is capable of producing accurate spatial inundation forecasts. Obviously, the proposed model provides reasonable spatial inundation forecasts, and is able to deal with the nonlinear relationships between inputs and desired output. In conclusion, the proposed model is suitable and useful for inundation forecasting.

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Development of a real-time regional-inundation forecasting model for the inundation warning system

Journal of Hydroinformatics ◽

10.2166/hydro.2013.202 ◽

2013 ◽

Vol 15 (4) ◽

pp. 1391-1407 ◽

Cited By ~ 13

Author(s):

Gwo-Fong Lin ◽

Hsuan-Yu Lin ◽

Yang-Ching Chou

Keyword(s):

Real Time ◽

Lead Time ◽

Control Point ◽

Warning System ◽

Computational Method ◽

Support Vector ◽

Forecasting Model ◽

Time Data ◽

Inundation Maps ◽

Proposed Model

Accurate forecasts of the inundation depth are necessary for inundation warning and mitigation. In this paper, a real-time regional forecasting model is proposed to yield 1- to 3-h lead time inundation maps. First, the K-means based cluster analysis is developed to group the inundation depths and to indentify the control points. Second, the support vector machine is used as the computational method to develop the point forecasting module to yield inundation forecasts for each control point. Third, based on the forecasted depths and the geographic information, the spatial expansion module is developed to expand the point forecasts to the spatial forecasts. An actual application to Siluo Township, Taiwan, is conducted to demonstrate the advantage of the proposed model. The results indicate that the proposed model can provide accurate inundation maps for 1- to 3-h lead times. The accurate long lead time forecasts can extend the lead time to allow sufficient time to take emergency measures. Furthermore, the proposed model is an efficient process that can be trained rapidly with real-time data and is more suitable to be integrated with the decision support system. In conclusion, the proposed modeling technique is expected to be useful to support the inundation warning systems.

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A Supervised Learning Model for AGV Perception in Unstructured Environment

Improving Knowledge Discovery through the Integration of Data Mining Techniques - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-8513-0.ch016 ◽

2015 ◽

pp. 334-353

Author(s):

Rizwan Aqeel ◽

Saif Ur Rehman ◽

Saira Gillani ◽

Sohail Asghar

Keyword(s):

Clustering Algorithm ◽

Research Problem ◽

Classification Model ◽

Support Vector ◽

Ground Vehicle ◽

Second Stage ◽

Initial Stage ◽

Cluster Area ◽

Proposed Model ◽

Unstructured Environment

This chapter focuses on an Autonomous Ground Vehicle (AGV), also known as intelligent vehicle, which is a vehicle that can navigate without human supervision. AGV navigation over an unstructured road is a challenging task and is known research problem. This chapter is to detect road area from an unstructured environment by applying a proposed classification model. The Proposed model is sub divided into three stages: (1) - preprocessing has been performed in the initial stage; (2) - road area clustering has been done in the second stage; (3) - Finally, road pixel classification has been achieved. Furthermore, combination of classification as well as clustering is used in achieving our goals. K-means clustering algorithm is used to discover biggest cluster from road scene, second big cluster area has been classified as road or non road by using the well-known technique support vector machine. The Proposed approach is validated from extensive experiments carried out on RGB dataset, which shows that the successful detection of road area and is robust against diverse road conditions such as unstructured nature, different weather and lightening variations.

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Flood-inundation maps and updated components for a flood-warning system or the City of Marietta, Ohio and selected communities along the Lower Muskingum River and Ohio River

Scientific Investigations Report ◽

10.3133/sir20145195 ◽

2014 ◽

Author(s):

Matthew T. Whitehead ◽

Chad J. Ostheimer

Keyword(s):

Warning System ◽

Ohio River ◽

Flood Inundation ◽

Inundation Maps ◽

Flood Warning ◽

Flood Warning System ◽

The City

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Mixed Kernel Function Support Vector Regression with Genetic Algorithm for Forecasting Dissolved Gas Content in Power Transformers

Energies ◽

10.3390/en11092437 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2437 ◽

Cited By ~ 4

Author(s):

Tusongjiang Kari ◽

Wensheng Gao ◽

Ayiguzhali Tuluhong ◽

Yilihamu Yaermaimaiti ◽

Ziwei Zhang

Keyword(s):

Genetic Algorithm ◽

Gas Content ◽

Power Transformers ◽

Gaussian Kernel ◽

Polynomial Kernel ◽

Support Vector ◽

Forecasting Model ◽

Dissolved Gas ◽

Proposed Model ◽

Forecasting Performance

Forecasting dissolved gas content in power transformers plays a significant role in detecting incipient faults and maintaining the safety of the power system. Though various forecasting models have been developed, there is still room to further improve prediction performance. In this paper, a new forecasting model is proposed by combining mixed kernel function-based support vector regression (MKF-SVR) and genetic algorithm (GA). First, forecasting performance of SVR models constructed with a single kernel are compared, and then Gaussian kernel and polynomial kernel are retained due to better learning and prediction ability. Next, a mixed kernel, which integrates a Gaussian kernel with a polynomial kernel, is used to establish a SVR-based forecasting model. Genetic algorithm (GA) and leave-one-out cross validation are employed to determine the free parameters of MKF-SVR, while mean absolute percentage error (MAPE) and squared correlation coefficient (r2) are applied to assess the quality of the parameters. The proposed model is implemented on a practical dissolved gas dataset and promising results are obtained. Finally, the forecasting performance of the proposed model is compared with three other approaches, including RBFNN, GRNN and GM. The experimental and comparison results demonstrate that the proposed model outperforms other popular models in terms of forecasting accuracy and fitting capability.

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Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

Energies ◽

10.3390/en13195190 ◽

2020 ◽

Vol 13 (19) ◽

pp. 5190 ◽

Cited By ~ 4

Author(s):

Matheus Ribeiro ◽

Stéfano Stefenon ◽

José de Lima ◽

Ademir Nied ◽

Viviana Mariani ◽

...

Keyword(s):

Ensemble Learning ◽

Electricity Price ◽

Support Vector ◽

Forecasting Model ◽

Price Forecasting ◽

Decomposition Approach ◽

Forecasting Models ◽

Electricity Price Forecasting ◽

Proposed Model ◽

Self Adaptive

Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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A Novel Forecasting Model Based on Support Vector Regression and Bat Meta-Heuristic (Bat–SVR): Case Study in Printed Circuit Board Industry

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622014500849 ◽

2015 ◽

Vol 14 (01) ◽

pp. 195-215 ◽

Cited By ~ 10

Author(s):

Amirmohammad Tavakkoli ◽

Jalal Rezaeenour ◽

Esmaeil Hadavandi

Keyword(s):

Support Vector Regression ◽

Printed Circuit Board ◽

Bat Algorithm ◽

Circuit Board ◽

Support Vector ◽

Forecasting Model ◽

Printed Circuit ◽

Proposed Model ◽

Board Industry

Sales forecasting is very beneficial to most businesses. A successful business needs accurate sales forecasting to understand the market and sales trends. This paper presents a novel sales forecasting model by integrating support vector regression (SVR) and bat algorithm (BA). Since the accuracy of SVR forecasting mainly depends on SVR parameters, we use BA for tuning these parameters because Bat is a newly introduced algorithm and has many parameters. In order to find the best set of BA parameters Taguchi method was utilized. We validated our model on four known UCI datasets. Then we applied our model in printed circuit board (PCB) sales forecasting case study. We compared the accuracy of the proposed model with Genetic algorithm (GA)–SVR, particle swarm optimization (PSO)–SVR, and classic-SVR. The experimental results show that the proposed model outperforms the others. To ensure the robustness of our proposed model, sensitivity analysis was also done using our model to find out the effects of dependent variables values on sales time series.

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Midterm Electricity Market Clearing Price Forecasting Using Two-Stage Multiple Support Vector Machine

Journal of Energy ◽

10.1155/2015/384528 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 6

Author(s):

Xing Yan ◽

Nurul A. Chowdhury

Keyword(s):

Support Vector Machine ◽

Electricity Market ◽

Support Vector ◽

Forecasting Model ◽

Two Stage ◽

Market Clearing ◽

Proposed Model ◽

Market Clearing Price ◽

Multiple Support ◽

Short Term Forecasting

Currently, there are many techniques available for short-term forecasting of the electricity market clearing price (MCP), but very little work has been done in the area of midterm forecasting of the electricity MCP. The midterm forecasting of the electricity MCP is essential for maintenance scheduling, planning, bilateral contracting, resources reallocation, and budgeting. A two-stage multiple support vector machine (SVM) based midterm forecasting model of the electricity MCP is proposed in this paper. The first stage is utilized to separate the input data into corresponding price zones by using a single SVM. Then, the second stage is applied utilizing four parallel designed SVMs to forecast the electricity price in four different price zones. Compared to the forecasting model using a single SVM, the proposed model showed improved forecasting accuracy in both peak prices and overall system. PJM interconnection data are used to test the proposed model.

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