English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The accurate prediction of storm surge disasters' direct economic losses plays a positive role in providing critical support for disaster prevention decision-making and management. Previous researches on storm surge disaster loss assessment did not pay much attention to the overfitting phenomenon caused by the data scarcity and the excessive model complexity. To solve these problems, this paper puts forward a new evaluation system for forecasting the regional direct economic loss of storm surge disasters, consisting of three parts. First of all, a comprehensive assessment index system was established by considering the storm surge disasters' formation mechanism and the corresponding risk management theory. Secondly, a novel data augmentation technique, k-nearest neighbor-Gaussian noise (KNN-GN), was presented to overcome data scarcity. Thirdly, an ensemble learning algorithm XGBoost as a regression model was utilized to optimize the results and produce the final forecasting results. To verify the best-combined model, KNN-GN-based XGBoost, we conducted cross-contrast experiments with several data augmentation techniques and some widely-used ensemble learning models. Meanwhile, the traditional prediction models are used as baselines to the optimized forecasting system. The experimental results show that the KNN-GN-based XGBoost model provides more precise predictions than the traditional models, with a 64.1% average improvement in the mean absolute percentage error (MAPE) measurement. It could be noted that the proposed evaluation system can be extended and applied to the geography-related field as well.

A Data Augmentation-Based Evaluation System for Regional Direct Economic Losses of Storm Surge Disasters