The Potential Uses of Operational Earthquake Forecasting

This article reports on a workshop held to explore the potential uses of operational earthquake forecasting (OEF). We discuss the current status of OEF in the United States and elsewhere, the types of products that could be generated, the various potential users and uses of OEF, and the need for carefully crafted communication protocols. Although operationalization challenges remain, there was clear consensus among the stakeholders at the workshop that OEF could be useful

Application of artificial neural networks and colored petri nets on earthquake resilient water distribution systems

Water distribution systems are important lifelines and a critical and complex infrastructure of a country. The performance of this system during unexpected rare events is important as it is one of the lifelines that people directly depend on and other factors indirectly impact the economy of a nation. In this thesis a couple of methods that can be used to predict damage and simulate the restoration process of a water distribution system are presented. Contributing to the effort of applying computational tools to infrastructure systems, Artificial Neural Network (ANN) is used to predict the rate of damage in the pipe network during seismic events. Prediction done in this thesis is based on earthquake intensity, peak ground velocity, and pipe size and material type. Further, restoration process of water distribution network in a seismic event is modeled and restoration curves are simulated using colored Petri nets. This dynamic simulation will aid decision makers to adopt the best strategies during disaster management. Prediction of damages, modeling and simulation in conjunction with other disaster reduction methodologies and strategies is expected to be helpful to be more resilient and better prepared for disasters --Abstract, page iv

Application of Artificial Intelligence in predicting earthquakes: state-of-the-art and future challenges

Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field

Earthquake Hazard Mitigation in the New Madrid Seismic Zone: Science and Public Policy

In the central United States, earthquake sources that are not well defined, long earthquake recurrence intervals, and uncertain ground-motion attenuation models have contributed to an overstatement of seismic hazard for the New Madrid Seismic Zone on the national seismic hazard maps published by the U.S. Geological Survey. A series of informal interviews in western Kentucky with local businesspersons, public officials, and other professionals in occupations associated with seismic-hazard mitigation discussed seismic-mitigation policies in relation to depressed local economy. Scientific and relative economic analysis was then performed using scenario earthquake models developed with the Federal Emergency management Agency\u27s Hazus-MH software. The ground-motion hazard generated by the 2008 Wenchuan, China, earthquake and seismic mitigation policies in that area were compared with those of the New Madrid Seismic Zone. Continued scientific research, additional educational opportunities for laymen and engineering professionals, and changes in the application of current earthquake science to public policy in the central United States should help improve public safety and economic development

StuA: An Intelligent Student Assistant

With advanced innovation in digital technology, demand for virtual assistants is arising which can assist a person and at the same time, minimize the need for interaction with the human. Acknowledging the requirement, we propose an interactive and intelligent student assistant, StuA, which can help new-comer in a college who are hesitant in interacting with the seniors as they fear of being ragged. StuA is capable of answering all types of queries of a new-comer related to academics, examinations, library, hostel and extra curriculum activities. The model is designed using CLIPS which allows inferring using forward chaining. Nevertheless, a generalized algorithm for backward chaining for CLIPS is also implemented. Validation of the proposed model is presented in five steps which show that the model is complete and consistent with 99.16% accuracy of the knowledge model. Moreover, the backward chaining algorithm is found to be 100% accurate

When less is more: How increasing the complexity of machine learning strategies for geothermal energy assessments may not lead toward better estimates

Previous moderate- and high-temperature geothermal resource assessments of the western United States utilized data-driven methods and expert decisions to estimate resource favorability. Although expert decisions can add confidence to the modeling process by ensuring reasonable models are employed, expert decisions also introduce human and, thereby, model bias. This bias can present a source of error that reduces the predictive performance of the models and confidence in the resulting resource estimates. Our study aims to develop robust data-driven methods with the goals of reducing bias and improving predictive ability. We present and compare nine favorability maps for geothermal resources in the western United States using data from the U.S. Geological Survey\u27s 2008 geothermal resource assessment. Two favorability maps are created using the expert decision-dependent methods from the 2008 assessment (i.e., weight-of-evidence and logistic regression). With the same data, we then create six different favorability maps using logistic regression (without underlying expert decisions), XGBoost, and support-vector machines paired with two training strategies. The training strategies are customized to address the inherent challenges of applying machine learning to the geothermal training data, which have no negative examples and severe class imbalance. We also create another favorability map using an artificial neural network. We demonstrate that modern machine learning approaches can improve upon systems built with expert decisions. We also find that XGBoost, a non-linear algorithm, produces greater agreement with the 2008 results than linear logistic regression without expert decisions, because the expert decisions in the 2008 assessment rendered the otherwise linear approaches non-linear despite the fact that the 2008 assessment used only linear methods. The F1 scores for all approaches appear low (F1 score \u3c 0.10), do not improve with increasing model complexity, and, therefore, indicate the fundamental limitations of the input features (i.e., training data). Until improved feature data are incorporated into the assessment process, simple non-linear algorithms (e.g., XGBoost) perform equally well or better than more complex methods (e.g., artificial neural networks) and remain easier to interpret

SCIENCE AND PUBLIC POLICY OF EARTHQUAKE HAZARD MITIGATION IN THE NEW MADRID SEISMIC ZONE

In the central United States, undefined earthquake sources, long earthquake recurrence intervals and uncertain ground motion attenuation models have contributed to an overstatement of regional seismic hazard for the New Madrid Seismic Zone on the National Seismic Hazard Maps. This study examined concerns regarding scientific uncertainties, overly stringent seismic mitigation policies and depressed local economy in western Kentucky through a series of informal interviews with local businessmen, public officials, and other professionals in occupations associated with seismic mitigation. Scientific and relative economic analyses were then performed using scenario earthquake models developed with FEMA’s Hazus-MH software. Effects of the 2008 Wenchuan earthquake in central China and seismic mitigation policies in use there were considered for potential parallels and learning opportunities. Finally, suggestions for continued scientific research, additional educational opportunities for laymen and engineering professionals, and changes in the application of current earthquake science to public policy in the central United States were outlined with the goal of easing western Kentucky economic issues while maintaining acceptable public safety conditions