AN ARTIFICIAL INTELLIGENCE APPROACH TO PREDICTION OF EXTREME EVENTS: THE CASE OF STORMS IN WESTERN FRANCE

Abstract. Storms represent an increased source of risk that affects human life, property, and the environment. Prediction of these events, however, is challenging due to their low frequency of occurrence. This paper proposed an artificial intelligence approach to address this challenge and predict storm characteristics and occurrence using a gated recurrent unit (GRU) neural network and a support vector machine (SVM). Historical weather and marine measurements collected from buoy data, as well as a database of storms containing all the extreme events that occurred in Brittany and Pays de la Loire regions, Western France, since 1996, were used. Firstly, GRU was used to predict the characteristics of storms (wind speed, pressure, humidity, temperature, and wave height). Then, SVM was introduced to identify storm-specific patterns and predict storm occurrence. The approach adopted leads to the prediction of storms and their characteristics, which could be used widely to reduce the awful consequences of these natural disasters by taking preventive measures.

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A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)

Sensors ◽

10.3390/s19112444 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2444 ◽

Cited By ~ 37

Author(s):

Dieu Tien Bui ◽

Ataollah Shirzadi ◽

Himan Shahabi ◽

Kamran Chapi ◽

Ebrahim Omidavr ◽

...

Keyword(s):

Artificial Intelligence ◽

Slope Angle ◽

Area Under The Curve ◽

Kernel Functions ◽

Gully Erosion ◽

Support Vector ◽

Base Function ◽

Conditioning Factors ◽

Intelligence Approach ◽

Artificial Intelligence Approach

In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).

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Exploring Seasonality Effect of Multinational Stock Dynamism with Support Vector Regression and Artificial Intelligence Approach

2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC) ◽

10.1109/icicic.2009.197 ◽

2009 ◽

Cited By ~ 1

Author(s):

Deng-Yiv Chiu ◽

Cheng-Yi Shiu

Keyword(s):

Artificial Intelligence ◽

Support Vector Regression ◽

Support Vector ◽

Intelligence Approach ◽

Artificial Intelligence Approach

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An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Journal of Construction Engineering ◽

10.1155/2014/109184 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Hong-Hai Tran ◽

Nhat-Duc Hoang

Keyword(s):

Artificial Intelligence ◽

Support Vector Machine ◽

Soil Improvement ◽

Function Parameter ◽

Support Vector ◽

Established Method ◽

In Situ Data ◽

Svm Algorithm ◽

Intelligence Approach ◽

Artificial Intelligence Approach

Permeation grouting is a commonly used approach for soil improvement in construction engineering. Thus, predicting the results of grouting activities is a crucial task that needs to be carried out in the planning phase of any grouting project. In this research, a novel artificial intelligence approach—autotuning support vector machine—is proposed to forecast the result of grouting activities that employ microfine cement grouts. In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and failure. Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter. The integration of the SVM and DE algorithms allows the newly established method to operate automatically without human prior knowledge or tedious processes for parameter setting. An experiment using a set of in situ data samples demonstrates that the newly established method can produce an outstanding prediction performance.

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