Rainstorm-induced flood risk assessment in developed urban area using a data-driven approach with watershed units.

Rainstorm flooding in developed urban areas has become a global focus. This study proposes a data-driven approach to urban rainstorm flood risk assessment. In contrast to the existing research, this study focuses on terrain watersheds as an assessment unit. Using Changsha as the study area, an inventory of 238 historical rainstorm flood locations was produced using automatic web crawling and literature data mining. Subsequently, an assessment model was developed based on a Bayesian algorithm and 16 influencing factors, and its accuracy was verified using a receiver operating characteristic curve. Because underground infrastructure is prone to backflow at its entrances and exits during rainstorms, the developed model was used to assess the backflow risk of two typical underground structures subjected to three rainstorm return periods: 5 (scenario 1), 10 (scenario 2), and 100 years (scenario 3). The conclusions are as follows: (1) The proposed method has a prediction accuracy of 88 % for flood risk. The most influential factors were H11 (proportion of impervious surface), H4 (mean elevation), and H1 (rainfall), contributing 52 %, 14.3 %, and 11.9 %, respectively. (2) Watersheds are classified into "Very Low," "Low," "High," and "Very High" based on the degree of flooding impact, accounting for 83.6 %, 11.9 %, 3.9 %, and 0.7 %, respectively. Watersheds classified as "Very High" are mainly distributed in the central region. (3) A total of 48 subway stations (7.9 % of the total) and 148 underground parking lots (6.5 % of the total) in the study area are located in "Very High" risk areas. (4) Compared to that in scenario 1, the proportion of underground entrances and exits with a "Very high" protection level in scenario 3 increased by approximately 10 %. In conclusion, this framework can assist urban planners in understanding the risks of urban flooding and mitigating potential flooding impacts.

Construction and Assessment of a Prognostic Risk Model for Cervical Cancer Based on Lactate Metabolism-Related lncRNAs.

Purpose: Cervical cancer (CC) has the fourth highest incidence and mortality rate among female cancers. Lactate is a key regulator promoting tumor progression. Long non-coding RNAs (lncRNAs) are closely associated with cervical cancer (CC). The study was aimed to develop a prognostic risk model for cervical cancer based on lactate metabolism-associated lncRNAs and to determine their clinical prognostic value. Patients and Methods: In this study, CESC transcriptome data were obtained from the TCGA database. 262 lactate metabolism-associated genes were extracted from MsigDB (Molecular Characterization Database). Then, correlation analysis was used to identify LRLs. Univariate Cox regression analysis was performed afterwards, followed by least absolute shrinkage and selection operator (LASSO) regression analysis and multiple Cox regression analysis. 10 lncRNAs were finally identified to construct a risk score model. They were divided into two groups of high risk and low risk according to the median of risk scores. The predictive performance of the models was assessed by Kaplan-Meier (K-M) analysis, subject work characteristics (ROC) analysis, and univariate and multivariate Cox analyses. To assess the clinical utility of the prognostic model, we performed functional enrichment analysis, immune microenvironment analysis, mutation analysis, and column line graph generation. Results: We constructed a prognostic model consisting of 10 LRLs at CC. We observed that high-risk populations were strongly associated with poor survival outcomes. Risk score was an independent risk factor for CC prognosis and was strongly associated with immune microenvironment analysis and tumor mutational load. Conclusion: We developed a risk model of lncRNAs associated with lactate metabolism and used it to predict prognosis of CC, which could guide and facilitate the progress of new treatment strategies and disease monitoring in CC patients.