Tsunami Impact Prediction System Based on TsunAWI Inundation Data

It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a pre-calculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS

Iliou Machine Learning Data Preprocessing Method for Stress Level Prediction

Part 7: Medical IntelligenceInternational audienceData pre-processing is an important step in the data mining process. Data preparation and filtering steps can take considerable amount of processing time. Data pre-processing includes cleaning, normalization, transformation, feature extraction and selection. In this paper, Iliou and PCA data preprocessing methods evaluated in a data set of 103 students, aged 18–25, who were experiencing anxiety problems. The performance of Iliou and PCA data preprocessing methods was evaluated using the 10-fold cross validation method assessing seven classification algorithms, IB1, J48, Random Forest, MLP, SMO, JRip and FURIA, respectively. The classification results indicate that Iliou data preprocessing algorithm consistently and substantially outperforms PCA data preprocessing method, achieving 98.6% against 92.2% classification performance, respectively