Exploring seafarers’ emotional responses to emergencies: An empirical study using a shiphandling simulator

A Comprehensive Review of Machine Learning Approaches for Flood Depth Estimation

The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models.

BEW-YOLOv8: A deep learning model for multi-scene and multi-scale flood depth estimation

In order to meet the needs of accurately grasping the situation of people in the mall at all times, the author proposes an analysis method based on computer vision for people flow image detection system. This method combines the HOG feature with the SVM classifier, detects pedestrians through dual cameras, and builds an experimental research platform for dual-camera joint detection of pedestrians. The result shows that the error rate of human flow detected by the author’s method is the lowest of 0% and the highest of 6.25%. Conclusion. This method has a good effect on the statistics of the number of people in the shopping mall and can reduce the workload of the monitoring personnel in the shopping mall.

Analysis of People Flow Image Detection System Based on Computer Vision Sensor

Pluvial floods are destructive natural disasters in cities. With high computational efficiency, machine learning models are increasingly used for flood susceptibility mapping. However, limited flooded or nonflooded samples constrain models’ predictive capability and introduce uncertainty in feature engineering. This study introduces a semi-supervised graph-structured model, Graph Attention Network (GAT), to address data scarcity and enable the use of only basic flood conditioning factors as inputs. GAT uses nodes and edges to represent spatial units and their relative spatial relationships. Based on its graph structure and attention mechanism, GAT automatically extracts high-order features from inputs of labeled and unlabeled units for modeling. In the metropolitan area of Dalian, China, GAT outperformed other models in flooded-nonflooded sample classification and exhibited a rational flood susceptibility distribution pattern, with only four basic flood conditioning factors and less than 1.2% of units for training. GAT can be an effective tool for practical urban flood management. 

Pluvial flood susceptibility mapping for data-scarce urban areas using graph attention network and basic flood conditioning factors

ABSTRACT Citizen science projects engage the public in monitoring the environment and can collect useful data. One example is the CrowdWater project, in which stream levels are observed and compared to reference photos taken at an earlier time to obtain stream level class data. However, crowd-based observations are uncertain and require data quality control. Therefore, we used a deep learning model to estimate the water-level class for photos taken by citizen scientists at different times for the same stream and compared different options for model training. The models had a root mean square error (R) of 0.5 classes or better for all but four of the 385 sites for which the model was trained. Low water levels were in general predicted better than high water levels (R of 0.6 vs 1.0 classes). The study thus highlights the potential of human–computer interaction for data collection and quality control in citizen science projects.

Automatic water-level class estimation from repeated crowd-based photos of streams

How to Use General AI for Task-Specific Applications: A Case Study of Monitoring Water Level Trends with River Cameras

Assessing Large Multimodal Models for Urban Floodwater Depth Estimation

Hybrid early-warning framework for unsafe crew acts detection and prediction

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