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Every year typhoons severely disrupt the normal rhythms of human activities and pose serious threats to China's coast. Previous studies have shown that the impact extent and degree of a typhoon can be inferred from various geolocation datasets. However, it remains a challenge to unravel how dwellers respond to a typhoon disaster and what they concern most in the places with significant human activity changes. In this study, we integrated the geotagged microblogs with the Tencent's location request data to advance our understanding of dweller's collective response to typhoon Hato and the changes in their concerns over the typhoon process. Our results show that Hato induces both negative and positive anomalies in humans' location request activities and such anomalies could be utilized to characterize the impacts of wind and rainfall brought by Hato to our study area, respectively. Topic analysis of Hato-related geotagged microblogs reveals that the negative location request anomalies are closely related to damage-related topics, whereas the positive anomalies to traffic-related topics. The negative anomalies are significantly correlated with economic loss and population affected at city level as suggested by an over 0.7 adjusted R2. The changes in the anomalies can be used to portray the response and recovery processes of the cities impacted. [ABSTRACT FROM AUTHOR]

Soft soils are widely distributed in the Guangdong-Hong Kong-Macao Greater Bay Area of China. The soft soils are featured with large water content, high compressibility and low permeability, posing great challenges in dealing with bearing capacity and foundation settlements. Extensive laboratory tests have to be conducted to determine parameters for shear strength and compressibility properties of soft soils. This is really time consuming and costly. In addition, sample disturbance and lab testing error are also unavoidable. The aim of this study is to develop a simple, efficient while satisfactorily accurate tool for prompt assessments of parameters for shear strength and compressibility of soft soils. The artificial neural network (ANN) technique is employed to reach this goal. A large database is first presented for measured physical and mechanical parameters of soft soils sampled from a core city in the Greater Bay Area. The data are obtained from six types of geotechnical laboratory tests, including direct shear test, consolidation test, unconsolidated undrained test, total stress consolidated undrained test, effective stress consolidated undrained test and compression test. Then, the ANN is applied to map the shear strength and compressibility properties of the soft soil from its physical parameters. The analytical forms of the ANN models are derived and presented to enhance their practical value. Next, the accuracies of the six ANN models are evaluated using model bias statistics where model bias is the ratio of measured to predicted value. The evaluation results showed that the ANN models are practically unbiased on average and the dispersions in prediction accuracy are low. Furthermore, the probability distributions of the model biases are characterized. This study helps saving time and cost of geotechnical investigation for soft soils in the area. • A database containing over 1000 physical and mechanical properties for soft soils • Analytical ANNs for mechanical features of soft soils from physical properties • Evaluation of the model accuracy of the developed ANN models using bias statistics • Characterization of the bias distributions for the ANNs [ABSTRACT FROM AUTHOR]