Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula

Martínez‐Álvarez, Francisco; Reyes, J.; Morales‐Esteban, Antonio; Rubio-Escudero, Cristina

doi:10.1016/j.knosys.2013.06.011

Cited by 90 publications

(52 citation statements)

References 45 publications

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“…The prediction capability of a machine learning model may be enhanced if input variables with null or negative predictive values are removed [41,45]; therefore, the predictive ability of forest fire related factors should be quantified and assessed first. In this study, the Pearson correlation method was used to assess predictive powers of the forest fire related factors due to its efficiency.…”

Section: Resultsmentioning

confidence: 99%

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

et al. 2016

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Abstract:The Cat Ba National Park area (Vietnam) with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM). The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management.

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Section: Resultsmentioning

confidence: 99%

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

et al. 2016

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“…Actually, many of these efforts can be noticed in everyday events such as energy management [1], telecommunications [2], pollution [3], bioinformatics [4], earthquakes [5], and so forth. Accurate predictions are essential in economical activities as remarkable forecasting errors in certain areas may involve large loss of money.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

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“…Redundant factors where predictive ability values are null or negative should be removed from the original dataset. This will help to improve overall performances of resulting models [3,64]. In this study, the predictive abilities of the fourteen influencing factors are quantified with the use of the Information Gain technique.…”

Section: Discussionmentioning

confidence: 99%

“…The results could be used for the determination of the best subset of influencing factors that not only have high predictive abilities to the output but are also uncorrelated with each other [3]. For this study, the Information Gain technique that has been successfully used recently for feature selection and predictive ability assessment was [64] used.…”

Section: Feature Selectionmentioning

confidence: 99%

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

et al. 2017

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This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques, i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

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Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula

Cited by 90 publications

References 45 publications

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS

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