Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning

Flood vulnerability assessment of Thailand's flood-prone Pathum Thani province and vulnerability mitigation strategies

This research assesses the flood vulnerability of Thailand's flood-prone province of Pathum Thani using a comprehensive vulnerability assessment framework. The assessment framework incorporates three key components: exposure, sensitivity, and adaptive capacity, consisting of 10, 12 and 11 flood vulnerability indicators, respectively. The flood vulnerability components and the flood vulnerability indicators are statistically validated by confirmatory factor analysis to determine the factor loadings and reliability of the components and indicators. The flood vulnerability questionnaire corresponding to the flood vulnerability indicators is subsequently developed and applied to the flood-prone districts of the province. The results show that proximity to rivers (with an indicator score of 0.685), household debt levels (0.612), land use patterns (0.617), and the proportion of low-income households (0.621) significantly contribute to the flood exposure of the province (with an exposure index score of 0.531). Larger household size (with an indicator score of 0.901), disruptions in public utility services (0.747), and workplace absenteeism due to flooding (0.741) contribute to the province's higher flood sensitivity (with a sensitivity index score of 0.633). Drainage capacity of natural and man-made waterways (0.571) contributes to low to moderate levels of flood adaptive capacity. The flood vulnerability of seven administrative districts of Pathum Thani, as measured by the flood vulnerability index scores (0.454-0.608), range from moderate to high. Local authorities need to invest in flood warning and response systems, prioritize infrastructure development and encourage community engagement to reduce the flood vulnerability.

Mapping flood vulnerability using an analytical hierarchy process (AHP) in the Metropolis of Mumbai

The burgeoning significance of urban floods in the context of evolving climate dynamics and shifting rainfall patterns underscores the exigency for comprehensive investigation and mitigation strategies. The study employs a multi-criteria assessment (MCE) approach and the analytical hierarchy process (AHP) to evaluate flood-vulnerable zones, wards, and sub-category-wise flood locations in Greater Mumbai. The AHP technique is used to evaluate flood-vulnerable impacting parameters such as rainfall (29.42%), slope (20.96%), land use/land cover (17.52%), vicinity to sewers and storm-water drainage (13.99%), vicinity to natural drainage (8.97%), vegetation (5.58%), and soil (3.56%). The study area is classified under different vulnerable categories as severe vulnerable (46.72%), high to very high (18.74%), and slight to moderate (34.54%). Researchers analysed 234 waterlogged locations, revealing that 85.46% (200 locations) were in the severe to very high vulnerability category, and only 14.52% (34 locations) were in the other three categories. Flood locations are more affected by slope (under the categories of < 5 m and 5.01-10 m), built-up land, sewers and storm water drainage (< 125 m), natural drainage (< 250 m), rainfall (< 2000 to 2200 mm), lowest dense vegetation, and coastal alluvium in soils. These model-based flood vulnerability maps are crucial for planning flood conservation and mitigation measures.

Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis

This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.