Computational Intelligence Techniques for Predicting Earthquakes

Martínez‐Álvarez, Francisco; Troncoso, Alicia; Morales‐Esteban, Antonio; Santos, J.R.

doi:10.1007/978-3-642-21222-2_35

Cited by 19 publications

(7 citation statements)

References 12 publications

(11 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These algorithms have their own characteristics and possible applications, but they are more used in linear prediction, and the accuracy is relatively poor used in non-linear prediction. This paper chooses SMOreg [5] and M5P [6] which can be effectively used for non-linear prediction as experimental methods. Through Comparing the predictable results from two algorithms, then determine the final method which used to predict the indicators.…”

Section: Methods Selectionmentioning

confidence: 99%

Study and application of non-linear time series prediction in ground source heat pump system

Wang¹,

Tan²,

Yu³

et al. 2012

2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet)

View full text Add to dashboard Cite

this paper presents the calculation of economic indicators which can reflect the geothermal energy utilization of ground source heat pump system. We forecast the technical and economic indicators based on the non-liner time-series data from ground source heat pump and building integrated systems. The paper selects SMOreg and M5P two different non-liner algorithms for forecasting. Through comparing the predictable results from two algorithms, the experimental results show that SMOreg algorithm has higher accuracy when predicting the indicators. In addition, we select more data to experiment for further verifying the forecast accuracy of SMOreg algorithm.

show abstract

Section: Methods Selectionmentioning

confidence: 99%

Study and application of non-linear time series prediction in ground source heat pump system

Wang¹,

Tan²,

Yu³

et al. 2012

2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet)

View full text Add to dashboard Cite

show abstract

“…Decision trees, have often fast and accurate performance in machine learning, have been combined with other techniques to predict emergencies. To predict disaster before it has occurrence, there are many studies which combine the decision tree with various machine learning techniques such as regression [145], hidden Markov model [125], association rule learning [79,34] and fuzzy logic and particle swarm optimization [106]. In particular, decision tree techniques have been also applied into prediction of surroundings (e.g., flood susceptible areas [127] and landslide susceptible areas [30]) for emergency situation.…”

Section: Event Predictionmentioning

confidence: 99%

Analytics and Evolving Landscape of Machine Learning for Emergency Response

Hong

Akerkar

2019

Learning and Analytics in Intelligent Systems

View full text Add to dashboard Cite

The advances in information technology have had a profound impact on emergency management by making unprecedented volumes of data available to the decision makers. This has resulted in new challenges related to the effective management of large volumes of data. In this regard, the role of machine learning in mass emergency and humanitarian crises is constantly evolving and gaining traction. As a branch of artificial intelligence, machine learning technologies have the huge advantages of self-learning, self-organization, and self-adaptation, along with simpleness, generality and robustness. Although these technologies do not perfectly solve issues in emergency management. They have greatly improved the capability and effectiveness of emergency management. In this paper, we review the use of machine learning techniques to support the decision-making processes for the emergency management and discuss their challenges. Additionally, we discuss the challenges and opportunities of the machine learning approaches and intelligent data analysis to distinct phases of emergency management. Based on the literature review, we observe a trend to move from narrow in scope, problem-specific applications of machine learning to solutions that address a wider spectrum of problems, such as situational awareness and real-time threat assessment using diverse streams of data. This chapter also focuses on crowd-sourcing approaches with machine learning to achieve better understanding and decision support during an emergency. Keywords emergency management • crisis analytics • data analysis • data mining • machine learning • decision making • situational awareness • real-time assessment • deep learning • data streams

show abstract

“…The earthquake dataset [15] has been retrieved from the Spanish's National Geographical Institute. This dataset consists of 4 quantitative attributes and 873 instances related to the location and the magnitude of Spanish earthquakes collected from 1981 to 2008.…”

Section: Datasets Descriptionmentioning

confidence: 99%

Obtaining optimal quality measures for quantitative association rules

et al. 2016

Self Cite

View full text Add to dashboard Cite

a b s t r a c tThere exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures used to assess the quality of association rules could be obtained according to the values of the weights of the measures included in the fitness function. Therefore, user's decision is very important in order to specify the weights of the measures involved in the optimization process. This paper presents a study of well-known quality measures with regard to the weights of the measures that appear in a fitness function. In particular, the fitness function of an existing evolutionary algorithm called QARGA has been considered with the purpose of suggesting the values that should be assigned to the weights, depending on the set of measures to be optimized. As initial step, several experiments have been carried out from 35 public datasets in order to show how the weights for confidence, support, amplitude and number of attributes measures included in the fitness function have an influence on different quality measures according to several minimum support thresholds. Second, statistical tests have been conducted for evaluating when the differences in measures of the rules obtained by QARGA are significative, and thus, to provide the best weights to be considered depending on the group of measures to be optimized. Finally, the results obtained when using the recommended weights for two real-world applications related to ozone and earthquakes are reported.

show abstract

Computational Intelligence Techniques for Predicting Earthquakes

Cited by 19 publications

References 12 publications

Study and application of non-linear time series prediction in ground source heat pump system

Study and application of non-linear time series prediction in ground source heat pump system

Analytics and Evolving Landscape of Machine Learning for Emergency Response

Obtaining optimal quality measures for quantitative association rules

Contact Info

Product

Resources

About