The Experts below are selected from a list of 14046 Experts worldwide ranked by ideXlab platform
Andrea G Fabbri - One of the best experts on this subject based on the ideXlab platform.
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predicting landslides for risk analysis spatial models tested by a cross Validation Technique
Geomorphology, 2008Co-Authors: Changjo Chung, Andrea G FabbriAbstract:Abstract A landslide-hazard map is intended to show the location of future slope instability. Most spatial models of the hazard lack reliability tests of the procedures and predictions for estimating the probabilities of future landslides, thus precluding use of the maps for probabilistic risk analysis. To correct this deficiency we propose a systematic procedure comprising two analytical steps: “relative-hazard mapping” and “empirical probability estimation”. A mathematical model first generates a prediction map by dividing an area into “prediction” classes according to the relative likelihood of occurrence of future landslides, conditional by local geomorphic and topographic characteristics. The second stage estimates empirically the probability of landslide occurrence in each prediction class, by applying a cross-Validation Technique. Cross-Validation, a “blind test” here using non-overlapping spatial or temporal subsets of mapped landslides, evaluates accuracy of the prediction and from the resulting statistics estimates occurrence probabilities of future landslides. This quantitative approach, exemplified by several experiments in an area near Lisbon, Portugal, can accommodate any subsequent analysis of landslide risk.
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Predicting landslides for risk analysis — Spatial models tested by a cross-Validation Technique
Geomorphology, 2008Co-Authors: Changjo Chung, Andrea G FabbriAbstract:Abstract A landslide-hazard map is intended to show the location of future slope instability. Most spatial models of the hazard lack reliability tests of the procedures and predictions for estimating the probabilities of future landslides, thus precluding use of the maps for probabilistic risk analysis. To correct this deficiency we propose a systematic procedure comprising two analytical steps: “relative-hazard mapping” and “empirical probability estimation”. A mathematical model first generates a prediction map by dividing an area into “prediction” classes according to the relative likelihood of occurrence of future landslides, conditional by local geomorphic and topographic characteristics. The second stage estimates empirically the probability of landslide occurrence in each prediction class, by applying a cross-Validation Technique. Cross-Validation, a “blind test” here using non-overlapping spatial or temporal subsets of mapped landslides, evaluates accuracy of the prediction and from the resulting statistics estimates occurrence probabilities of future landslides. This quantitative approach, exemplified by several experiments in an area near Lisbon, Portugal, can accommodate any subsequent analysis of landslide risk.
Changjo Chung - One of the best experts on this subject based on the ideXlab platform.
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predicting landslides for risk analysis spatial models tested by a cross Validation Technique
Geomorphology, 2008Co-Authors: Changjo Chung, Andrea G FabbriAbstract:Abstract A landslide-hazard map is intended to show the location of future slope instability. Most spatial models of the hazard lack reliability tests of the procedures and predictions for estimating the probabilities of future landslides, thus precluding use of the maps for probabilistic risk analysis. To correct this deficiency we propose a systematic procedure comprising two analytical steps: “relative-hazard mapping” and “empirical probability estimation”. A mathematical model first generates a prediction map by dividing an area into “prediction” classes according to the relative likelihood of occurrence of future landslides, conditional by local geomorphic and topographic characteristics. The second stage estimates empirically the probability of landslide occurrence in each prediction class, by applying a cross-Validation Technique. Cross-Validation, a “blind test” here using non-overlapping spatial or temporal subsets of mapped landslides, evaluates accuracy of the prediction and from the resulting statistics estimates occurrence probabilities of future landslides. This quantitative approach, exemplified by several experiments in an area near Lisbon, Portugal, can accommodate any subsequent analysis of landslide risk.
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Predicting landslides for risk analysis — Spatial models tested by a cross-Validation Technique
Geomorphology, 2008Co-Authors: Changjo Chung, Andrea G FabbriAbstract:Abstract A landslide-hazard map is intended to show the location of future slope instability. Most spatial models of the hazard lack reliability tests of the procedures and predictions for estimating the probabilities of future landslides, thus precluding use of the maps for probabilistic risk analysis. To correct this deficiency we propose a systematic procedure comprising two analytical steps: “relative-hazard mapping” and “empirical probability estimation”. A mathematical model first generates a prediction map by dividing an area into “prediction” classes according to the relative likelihood of occurrence of future landslides, conditional by local geomorphic and topographic characteristics. The second stage estimates empirically the probability of landslide occurrence in each prediction class, by applying a cross-Validation Technique. Cross-Validation, a “blind test” here using non-overlapping spatial or temporal subsets of mapped landslides, evaluates accuracy of the prediction and from the resulting statistics estimates occurrence probabilities of future landslides. This quantitative approach, exemplified by several experiments in an area near Lisbon, Portugal, can accommodate any subsequent analysis of landslide risk.
Rata Suwantong - One of the best experts on this subject based on the ideXlab platform.
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Support vector regression for rice age estimation using satellite imagery
2016 13th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2016Co-Authors: Panu Srestasathiern, Siam Lawawirojwong, Rata SuwantongAbstract:Rice age estimation is an process in crop management and monitoring. In this paper, we present an approach for estimating age of rice on satellite image using support vector regression Technique. The advantage of using support vector regression is that it is non-parametric Technique. Therefore, any age model is not required. Moreover, the support vector regression is robust to outlier. The input data is the satellite image features i.e., radiometric information. To tune the parameters of support vector machine, the k-fold cross Validation Technique is utilized. The experiment was conducted using Landsat-8 image. Comparing the estimated age result with ground truth, the proposed method showed expected performance.
Erdal Oruklu - One of the best experts on this subject based on the ideXlab platform.
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Ultrasonic flaw detection using Support Vector Machine classification
2015 IEEE International Ultrasonics Symposium (IUS), 2015Co-Authors: Kushal Virupakshappa, Erdal OrukluAbstract:In this work, a Support Vector Machine (SVM) classifier is introduced for ultrasonic flaw detection based on features extracted from the output of the subband decomposition filters. SVM is a machine learning method used for classification and regression analysis of complex real-world problems that may be difficult to analyze theoretically. A dataset constituting feature vectors of ultrasonic signals containing flaw and no flaw, is created in order to train and test the SVM. A k-fold cross Validation Technique is then performed to choose the best parameters for classification. Experimental results, using A-scan data measurements from a steel block, show that a very high classification accuracy can be achieved. Robust performance of the classifier is due to proper selection of frequency-diverse feature vectors and successful training.
Panu Srestasathiern - One of the best experts on this subject based on the ideXlab platform.
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Support vector regression for rice age estimation using satellite imagery
2016 13th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2016Co-Authors: Panu Srestasathiern, Siam Lawawirojwong, Rata SuwantongAbstract:Rice age estimation is an process in crop management and monitoring. In this paper, we present an approach for estimating age of rice on satellite image using support vector regression Technique. The advantage of using support vector regression is that it is non-parametric Technique. Therefore, any age model is not required. Moreover, the support vector regression is robust to outlier. The input data is the satellite image features i.e., radiometric information. To tune the parameters of support vector machine, the k-fold cross Validation Technique is utilized. The experiment was conducted using Landsat-8 image. Comparing the estimated age result with ground truth, the proposed method showed expected performance.
