Occurrence Of Floods Research Articles

Using Copula functions to predict climatic change impacts on floods in river source regions

Flood frequency in river source regions is significantly affected by rainfall and snowmelt as part of climatic changes. A traditional univariate flood frequency analysis cannot reflect the complexity of floods, and when used in isolation, it can only underestimate flood risk. For effective flood prevention and mitigation, it is essential to consider the combined effects of precipitation and snowmelt. Copula functions can effectively quantify the joint distribution relationship between floods and their associated variables without restrictions on their distribution characteristics. This study uses copula functions to consider a multivariate probability distribution model of flood peak flow (Q) with cumulative snowmelt (CSm) and cumulative precipitation (CPr) for the Hutubi River basin located in northern Xinjiang, China. The joint frequencies of rainfall and snowmelt floods are predicted using copula models based on the Coupled Model Intercomparison Project Phase 6 data. The results show that Q has a significant positive correlation with 24-d CSm (r = 0.559, p = 0.002) and 23-d CPr (r = 0.965, p < 0.05). Flood frequency will increase in the future, and mid- (2050–2074) and long-term (2075–2099) floods will be more severe than those in the near-term (2025–2049). The probability of flood occurrence is higher under the SSP2-4.5 and SSP1-2.6 scenarios than under SSP5-8.5. Precipitation during the historical period (1990–2014) led to extreme floods, and increasing future precipitation trends are found to be insignificant. Snowmelt increases with rising temperatures and occurs earlier than estimated, leading to an earlier flood period in the basin and more frequent snowmelt floods. The Q under the joint return period is larger than that during the same univariate return period. This difference indicates that neglecting the interaction between precipitation and snowmelt for floods leads to an underestimation of the flood risk (with underestimations ranging from 0.3% to 22%). The underestimations decrease with an increase in the return period. The joint risks of rainfall or snowmelt according to various flood periods should be considered for rivers with multi-source runoff recharge in flood control design. This study reveals the joint impact of precipitation and snowmelt on extreme floods under climate change in river source regions. This study also provides a scientific basis for regional flood prevention and mitigation strategies, as well as for the rational allocation of water resources.

Deciphering spatial-temporal dynamics of flood exposure in the United States,

Extracting baseline information on flood exposure and its temporal evolution is essential to formulate flood risk reduction strategies. At present, comprehensive, long-term spatial-temporal flood exposure research in the United States is lacking. The objective of the study is to evaluate county-wide flood exposure in the US and unravel its spatial-temporal dynamics from 2001 to 2019 to answer three research questions via hypothesis testing. First, how have human settlement areas exposed to flood threats in the US changed over the past two decades? Second, has the occurrence of floods and their resulting damage influenced the growth of developed areas in flood zones? Third, do disparities exist in the sensitivity to flood threats among urban-rural communities? Results show that the overall rate of development in flood zones in the contiguous United States has steadily decreased from 2001 to 2019. The Local Moran's I analysis discovers pockets of emerging, expanding, shrinking, and changing clusters of communities that show a rapid increase or decrease of developed areas within flood zones over time. Most counties that experienced more frequent flooding events demonstrate greater responsiveness to flood hazards by avoiding development in flood zones. Finally, urban communities exhibit a higher exposure and sensitivity to flood hazards compared to rural areas.

Assessing urban drainage pressure and impacts of future climate change based on shared socioeconomic pathways

The increasing frequency of urban flood disasters presents a significant obstacle to urban sustainability. Urban flood management aims to reduce the flood occurrences, currently addressed through urban drainage systems. Previous studies have demonstrated future precipitation extremes will pose larger pressure on urban drainage network, but when and where the pressure will reach a dangerous level have never been assessed in any city of China. This study establishes the initial framework for identifying critical decades and hot spots of urban drainage pressure changes due to future climate change, through a case study conducted in southern China (Haining city). Urban drainage pressure was assessed by a combination of the urban drainage model known as the Storm Water Management Model (SWMM) and pipe statistics. Using climate projections from the latest phase of Coupled Model Intercomparison Project (CMIP6) under four typical SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios, we project the changes in urban drainage pressure by 21st century, and identify the key decades and high risk areas with the occurrence of dangerous pressure levels. The results indicate an overall upward trend in urban drainage pressure for Haining city, with over 97% of the flooding nodes projected to firstly reach the dangerous level by the 2030 s. Comparisons of the patterns under different SSP-RCP scenarios, suggest that a higher forcing pathway would expedite the deterioration of urban drainage pressure, particularly in urban areas with lower DEM and high building intensity. This has broad implications for better informing disaster management and policy-making in similar cities, especially those with inadequate drainage capacities.

Snow depth time series Generation: Effective simulation at multiple time scales

Snow depth (SD) is a crucial variable of the water, energy, and nutrient cycles, impacting water quantity and quality, the occurrence of floods and droughts, snow-related hazards, and sub-surface ecological functions. As a result, quantifying SD dynamics is crucial for several scientific and practical applications. Ground measurements of SD provide information at sparse locations, and physical global model simulations provide information at relatively coarse spatial resolutions. An approach to complement this information is using stochastic models that generate time series of hydroclimatic variables, preserving their statistical properties in a computationally-effective manner. However, stochastic generation methods to produce SD time series exclusively do not exist in the literature. Here, we apply a stochastic model to produce synthetic daily SD time series trained by 448 stations in Canada. We show that the model captures key statistical properties of the observed records, including the daily distributions of zero and non-zero SD, temporal clustering (i.e., autocorrelation), and seasonal patterns. The model also excelled in capturing the observed higher-order L-moments at multiple temporal scales, with biases between simulated and observed L-skewness and L-kurtosis within (-0.1, +0.1) for 93.0 % and 98.3 % of the stations, respectively. The stochastic modelling approach introduced here advances the generation of SD time series, which are needed to develope Earth-system models and assess the risk of snowmelt flooding that lead to severe damage and fatalities.

Geospatial analysis of flood risk hazard in Zambezi Region, Namibia

The recent decade has seen an increase in frequency and intensity of flood risk globally especially in less developed countries. This has been attributed to many factors such as population growth, urbanization, climate change, increasing precipitation, and poor solid waste management among others. This study used quantitative analysis to identify and characterise flood-prone areas in Kabbe and Kaltima in the Zambezi region. We estimated major factors influencing flood events in the study area. We estimated this by incorporating the analytical hierarchy process (AHP) and GIS-based multi-criteria decision-making to map and identify flood-prone areas in Kabbe and Katima. AHP was employed to ascertain the weight of each criterion taken into consideration for the susceptibility mapping. We analysed ten factors elevation, slope, distance to river, rainfall, topographic wetness index, distance to road, drainage density, land use land cover, modified soil adjusted vegetation index, and soil which are closely associated with flood occurrence in the study area. The flood factors maps were categorized into five susceptibility levels. We used field data by interviewing key informants and community members to validate the GIS analysis. The result from the flood susceptibility map indicates that 46 % of areas in Kaltima and Kabbe have a low susceptibility to flood, 56.04 % are moderate, 43.33 % are high and 17 % are very highly susceptible to flood. The areas susceptible to flooding are mostly low-laying areas in Kabbe which are gentle slopes and are sitting at approximately 921–935 m below sea level. Speaking with stakeholders in the area, they confirmed that communities such as Ihaha, Isize, Mbalasinte, and Kalumnesa are highly susceptible to flooding. The community members confirm that they experience flooding every year with devastating impacts on their lives and livelihoods. We recommend localized flood management strategies that cater to the needs of the communities and develop strategies to improve preparedness, response, and mitigation. Increasing government funding for flood risk management in the area can increase the capacity of the community for flood risk management through training and awareness

Penyuluhan Tentang Banjir Pada Masyarakat Dan Pemuda di Kelurahan Mandalika Sandubaya Kota Mataram

, because there are still many residents who do not understand what floods are and what to do when experiencing floods. To provide awareness about floods, education about floods must be carried out for children and the elderly so that it occurs. similarities in perception and knowledge about this matter, especially for city residents in upstream areas such as in Mandalika Village. Education about floods is carried out in several stages, including the initial survey stage, coordination, discussion, question and answer activities and evaluation. The counseling material provided includes perceptions about floods, factors that cause floods, ways to avoid and reduce the occurrence of floods and the infrastructure needed to anticipate floods. During the outreach process, discussion and question and answer sessions were also provided, to increase participants' understanding of floods. The result of the service that has been carried out is being able to provide counseling to the community, both young people and parents, totaling 25 representatives who attended, including the management of Banjar Sedana Ayu. Participants have gained knowledge about floods and have understood the phenomenon of flooding and ways to avoid it after a question and answer session at the end of the outreach activity. By having the participants at the location of this trial, some of the people in the Mandalika sub-district already understand how floods occur and are expected to be able to deal with them if they happen.

Analysis of the Post-Cyclonic Physical Flood Susceptibility and Changes of Mangrove Forest Area Using Multi-Criteria Decision-Making Process and Geospatial Analysis in Indian Sundarbans

Tropical cyclones, one of the most extreme and destructive meteorological incidents, cause extensive damage to lives and livelihoods worldwide. This study utilized remotely sensed data along with multi-criteria decision-making, geospatial techniques, and major cyclonic events Aila, Amphan, and Yaas to identify the changes in the vulnerability of cyclone-induced floods in the 19 community development blocks of Indian Sundarbans in the years 2009–2010, 2020–2021, and 2021–2022 (the post-cyclonic timespan). The Sundarbans are a distinctive bioclimatic region located in a characteristic geographical setting along the West Bengal and Bangladesh coasts. In this area, several cyclonic storms had an impact between 2009 and 2022. Using the variables NDVI, MNDWI, NDMI, NDBI, BSI, and NDTI, Landsat 8 Operational Land Imager, Thermal Infrared Sensor, Resourcesat LISS-III, and AWiFS data were primarily utilized to map the cyclonic flood-effective zones in the research area. The findings indicated that the coastline, which was most impacted by tropical storms, has significant physical susceptibility to floods, as determined by the AHP-weighted overlay analysis. Significant positive relationships (p &lt; 0.05, n = 19 administrative units) were observed between mangrove damage, NDFI, and physical flood susceptibility indicators. Mangrove damage increased with an increase in the flood index, and vice versa. To mitigate the consequences and impacts of the vulnerability of cyclonic events, subsequent flood occurrences, and mangrove damage in the Sundarbans, a ground-level implementation of disaster management plans proposed by the associated state government, integrated measures of cyclone forecasting, mangrove plantation, coastal conservation, flood preparedness, mitigation, and management by the Sundarban Development Board are appreciably recommended.

Unveiling Torrential Flood Dynamics: A Comprehensive Study of Spatio-Temporal Patterns in the Šumadija Region, Serbia

This paper presents a comprehensive analysis of flood frequency and a spatio-temporal characterization of historical torrential floods in the Šumadija region using water discharge datasets and documented events. A chronology of 344 recorded torrential flood events, spanning from 1929 to 2020, illustrates the region’s vulnerability, with a death toll exceeding 43. The study defines the intra-annual primary and secondary peaks of torrential flood occurrences and explains their spatial distribution. Furthermore, the identification of suitable probability distribution functions underscores the necessity of tailored approaches for effective flood risk management in this diverse geographical environment. The study employed Flood Frequency Analysis (FFA) and goodness-of-fit tests, including the Kolmogorov–Smirnov (K-S) and Cramér–von Mises (CvM) tests, to assess the frequency and magnitude of flood events and evaluate diverse distribution functions. The main results include the identification of suitable probability distribution functions for each river within the region, emphasizing the need for tailored approaches in flood risk management. Additionally, discharge values for various return periods offer crucial insights for informed decision-making in flood risk management and infrastructure planning.

Examining the Effects of a Flood Event in the Lower Ceyhan Basin in 1980 Using Historical Satellite Data

Analysis of past flood events contributes forecasting of effects of future flood events. Flood maps have been created in order to assess flood hazards in planning projects and to identify flood-inundated regions with flood damage following flood occurrences. Flood mapping in the context of flood monitoring enables development of flood management strategies to protect life and property. Although conventional terrestrial observations and measurements in flood control have been constrained by topographical and meteorological circumstances, remote sensing provides decision support with quick analysis capability. The flood event that occurred in the Lower Ceyhan Basin of Turkey in 1980 was examined in this work utilizing satellite-based remote sensing techniques, and flood inundation areas were calculated using NDWI (Normalized Difference Water Index). As a result, it was determined that 3493.45 ha in the north of Karataş in the Lower Ceyhan Plain, 7799.42 ha between Bahçe, Akdeniz, and Yumurtalık, 7404.9 ha around Çatalpınar and Yakapınar in the Lower Ceyhan Plain, and approximately 24890 ha in the Upper Ceyhan were affected by the flood event in 1980.

Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method

The frequent occurrence of floods and waterlogging has significantly impacted coastal cities. Effective mapping of flood risk can enhance the precision of disaster risk reduction strategies. However, there is room for improvement in fields such as flood inundation data, evaluation objectivity, and classification refinement. This study, using Xiamen as a case study, advanced flood inundation data accuracy by generating a frequency-based flood inundation map from multi-year remote sensing imagery. This study integrated the EWM-TOPSIS model with a neural network model to evaluate the flood risk. The EWM-TOPSIS model was employed to assess the flood vulnerability and flood exposure, while the multi-class neural network model to simulate the flood hazard. This combined approach reduced subjectivity in the flood risk assessment of Xiamen and achieved a finer level of risk classification compared to traditional binary neural networks. The results indicated that the flood vulnerable areas of Xiamen are concentrated along waterways distant from roads, with high flood exposure in Tong'an and Xiang'an districts. The flood-prone areas in Xiamen are primarily located along the coastal areas characterized by extensive impermeable surfaces. High flood risk areas are mainly distributed in Tong'an and Xiang'an subdistricts, particularly in Xiangping and Xindian. The method developed offers support for accurate flood risk identification and decision-making in cities with limited data resources.

Projected seasonal flooding in Canada under climate change with statistical and machine learning

Study regionCanada Study focusFloods are among the costliest and deadliest natural hazards in the world. To date, little is known about future seasonal flooding across all Canada. In this paper, data-driven models for flood occurrence (i.e., happening of a flood) and impact (i.e., displaced population) were calibrated for spring and summer seasons in 14,000 watersheds across Canada. Generalized Additive Models (GAM), Random Forests (RF) and Gradient Boosting Machines (GBM) were considered to model seasonal floods. The best-performing flood models were then used with regional climate models to assess the effect of climate change on flooding for three time horizons: historical, medium-term (∼2050) and long-term (∼2080). New hydrological insights for the regionGAM offered the best out-of-sample performance trade-off in both seasons for predicting flooding in Canada. Projections with GAM showed a general increase in summer flooding occurrence and impact in 2050 and 2080, mainly in the Yukon, western British Columbia, southern Prairies, Ontario and Quebec and some Atlantic provinces. Results for spring flooding were more mixed, but there seemed to be a slight decrease in the impact of spring flooding in the southern Prairies, particularly in 2080. The combination of statistical/machine learning and climate models have provided a more detailed and contrasted picture of the projected seasonal flooding situation over Canada that will help authorities better mitigate future flood risks.

Efficiency Evaluation of Surface Water Collection Infrastructure during Floods: Information Analysis and Zoning Management

Objective: Flood events, occurring as natural and unexpected phenomena, have become increasingly prevalent in recent decades. Assessing the probability of flood risk and creating flood zoning maps in vulnerable areas, particularly urban regions, are crucial for mitigating flood damages and managing such events. This research aims to determine the flood-prone zones along the Darakeh River, located in Tehran, the capital city of Iran, by integrating the hydraulic model HEC-RAS with Geographic Information System (GIS). Methods: After establishing the physiographic characteristics of the watershed, including area, perimeter, elevation, slope, and time of concentration, influential factors on flood occurrences such as permeability, hydrological soil groups, runoff potential, curve number, land use, and vegetation cover were identified. This information, along with other hydraulic data and flow specifications such as boundary conditions, roughness coefficients, and design discharge, was incorporated into the HEC-RAS model. The model was executed for different return periods of 2, 5, 10, 25, 50, and 100 years. Subsequently, the hydraulic model outputs were transferred to the GIS model to obtain the flood-prone zones along the Darakeh River for the aforementioned return periods. Results: As observed, in the middle section of the Khoshke Channel, which has a lower gradient, the floodplain exhibits a wider extent. Furthermore, along the course of the river and after passing through a stretch of the Khoshke Channel with a reduced gradient, the floodplain's area increases up to the location of the western flood channel. Conclusion: It is concluded that areas with lower river slopes exhibited larger flood-prone zones, highlighting the necessity of increased attention to prevent significant damages in urban areas during flood events.

Understanding flash flooding in the Himalayan Region: a case study

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

Predicting affective and cognitive learning outcomes: A quantitative analysis using climate change vectors

Recent changes in the atmospheric conditions have exerted a lot of pressure on the surface of the earth causing attitudinal change, fear of survival as well as pattern of living. Schools are not out of these influences from the changes in climatic conditions. These study, therefore. examines the nexus between climate change vectors such as classroom temperature variation, persistent drought, severe flood occurrence on class attendance, class participation and academic performance among secondary school students. The study adopted an ex post facto research design, and a total of 1,881 were used for data collection. Two research instruments, the climate change variation scale and class attendance, class participation, and academic performance scale were used for data collection. The instrument was validated using factor analysis to assess the dimensionality of the items as well as obtain factors using component analysis and varimax rotation. To assess principal the mode fitness and acceptability, the confirmatory factor analysis (CFA) was carried out using the maximum likelihood estimation method, and the factor loadings from exploratory factor analysis and CFA were not too different. The collected data were analyzed using the simple and multilinear regression techniques. The results showed that relatively, persistent, severe flooding and classroom temperature variation contributes significantly to class participation, class attendance, and academic performance among students. Similarly, the variables; severe flood, drought and classroom temperature when compositely examined contributes to the variation in class attendance, class participation and academic performance among students. Implications and recommendations of the study were stated.

A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China

The combined effect of global warming and urbanization have an impact on the occurrences of floods in the Yangtze River Basin (YRB) and Huaihe River Basin (HRB), causing potential risks to the safe operation of watersheds. Therefore, this study proposed a dynamic von Mises-based (DvM) framework for detecting the impact of urbanization and climate change on flood timing (FT) across HRB and YRB from the nonstationary frequency included: (1) visualization of empirical probability of FT series to ascertain the number of mixed von Mises distributions; (2) selection of the multi-covariate-based model in consideration of the combined effect of multiple physical covariates, following the phase-wise strategy which involves the likelihood ratio test, Kolmogorov-Smirnov goodness-of-fit test, and the Akaike information criterion; (3) development of the linear regression-based comparative approach to isolate and distinguish the delaying and advancing effects of various factors (mainly climate change and urbanization-induced impact) on FT. In terms of the urbanization-induced impact on peak flow timing, HRB suffered an advanced effect by 4% and YRB suffered an advanced effect by 2%, which can be attributed to the divergence of the urbanization development level quantified by the impervious surfaces of sub-basins controlled by the stations in these two basins. In contrast to the modest advancement caused by urbanization (4% for HRB and 2% for YRB) in influencing the timing of flood peaks in the two basins, climate change factors, specifically the timing of soil moisture (TSM) and maximum rainfall (TPRE), play a more significant role in contributing to a delayed trend observed at a total of seven stations in the Huaihe River Basin and Yangtze River Basin. The main driver behind the trend towards delayed flood timing in both the Huaihe River Basin (HRB) and the Yangtze River Basin (YRB) is predominantly influenced by the timing of soil moisture. According to TSM, this factor contributes 16% in HRB and 10% in YRB. In contrast, the timing of rainfall has a lesser impact, accounting for 13% in HRB and 8% in YRB according to TPRE.

Mapping the Lava Flood Hazard Using the Flood Discharge Approach and 2D Hydrodynamic Modeling at the Rejali River, Mount Semeru

In December 2021, Mount Semeru experienced an eruption accompanied by extreme rainfall, which resulted in lava floods, known as lahars or debris flows. The lava flood destroyed infrastructure, resulting in loss of life. Various rivers surrounding Mount Semeru, including the Rejali River, experienced the effects of this phenomenon. To address this, a study is needed to analyze the occurrence and frequency of lava floods over specific time intervals through the creation of a hazard map. This study aims to map the hazard of lava floods for various return periods using a coupled HEC-HMS and HEC-RAS software alongside a lava flood discharge approach. The HEC-HMS software is used to simulate hydrological processes, to obtain the lava flood discharge, while the HEC-RAS is used to model a two-dimensional (2D) lava flood hazard map. The input parameters of the modeling in this study are rainfall intensity, soil type, land cover, river distance, slope, and elevation. The results show that the flood area covers 9.55% of the total study area by 2 year return period (Q2), 11.80% by Q10, 14.10% by Q50, and 15.72% by Q200 with an overall validation Root Mean Square Error (RMSE) of 0.16. These changes are determined by the discharge volume from each return phase and the river's shallow depth, which causes overflow beyond the river's ability to accommodate the flow. Thus, this study suggests that the models successfully generated a reliable model for mapping the risk of lava floods on the Rejali River. These findings can help the government reduce disaster losses through adequate adaptation and mitigation initiatives.

Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada

ABSTRACT Mitigating the harmful effects of flooding is essential in the current climate change and urban growth scenario, where these natural phenomena are expected to occur more often. Addressing this problem, we present a novel graph-based forecasting model for predicting urban flooding and showcase its application in the Bow River in the City of Calgary, Alberta, Canada. The proposed model is based on graph theory and deep learning paradigms and was used to forecast flooding occurrences up to 24 h ahead. Our new approach developed the SAGE algorithm, a hybrid learning and planning approach that accurately predicted floodings for various analyzed forecasting horizons, increasing the forecasting performance by up to 44% compared to the persistence model. The SAGE model also returned superior or competitive results compared to other models in the literature. The superiority of the SAGE model was highlighted for longer forecast horizons of 24 h ahead, where it reached a maximum improvement of 79%, suggesting its superiority in capturing spatiotemporal information in the dataset. These results indicate that the SAGE approach is a cutting-edge tool for flooding forecasting, and it could be used to develop early flood warning systems to reduce potential flooding impacts.

Investigation of Unsaturated Soil Hydraulic Properties for Subgrade Improvement using Marble Dust Waste

Introduction The distribution of suction, which has implications for seepage and shear strength, plays a crucial role in determining the stability of unsaturated soil. The Air Entry Value (AEV) and Residual Water Contents (RWC) change over time to create the Soil-Water Characteristic Curve (SWCC), which is very important for figuring out how the suction is distributed. During a flood occurrence, the subgrade becomes inundated with water, leading to adverse effects on the AEV and RWC of the subgrade. Consequently, the performance of the subgrade is diminished. This study focuses on the use of Marble Dust Waste (MDW) as an addition to the subgrade to improve the strength of the subgrade. Aims This study aims to investigate the variations in air AEV in relation to the optimal MDW in unsaturated soil found in Malaysia. Methods The study uses control samples and soil that has been mixed with different amounts of MDW: 5%, 10%, 15%, 20%, and 25%. The SWCC is generated using the pressure plate extractor device. Results The results indicate a significant increase in the AEV for the mixed soil sample when compared to the control sample. While the soil-MDW mixture containing 5% of this material has an AEV value of 23 kPa, the control sample's value is 10 kPa. Conclusion The findings of the study suggest that the utilization of MDW yields a beneficial influence on the AEV, hence potentially improving the performance of the subgrade. The researchers aim to evaluate the potentially dangerous waste and convert it into a substance that is appropriate for engineering applications. This study provides empirical evidence that aligns with the objectives outlined in Chapter 8 of the 12th Malaysia Plan for the period 2021–2025. The topic under discussion pertains to environmental sustainability, specifically focusing on enhancing the ability to withstand the adverse effects of climate change and disasters, as well as the Green Technology Master Plan Malaysia 2017–2030, with a specific emphasis on Chapter 6, which addresses the issue of waste management.

The Most Likely Flooded Soil in Terengganu State During Historically Greatest Rainfall Intensity

Water infiltration in the soil is an essential topic for investigation because soil fully saturated with water forms standing water that leads to flooding on top of the soil. Soil investigation is vital for agricultural study due to the necessity to know soil carrying capacity in holding water, field capacity, and also the soil temperature for biological activities and chemical reactions. However, human urbanization and expansion into previously unexplored soil areas could lead to unnecessary environmental stress, such as floods. Floodwater forms when the rainfall intensity exceeds soil water infiltration. The flood event is crucial since it affects the human livelihood and facility damages. Therefore, a good understanding of soil hydraulic water infiltration helps better understanding the cause of flood occurrence. The current study examines the soil series in Terengganu state with its soil properties concerning the soil water characteristics curve, which indicates the soil's ability to absorb and allow water movement in the ground. The equation used was the improved Richards’ equation that can be applied to examine the standing water or floodwater impacts on water infiltration. Also, the unsaturated soil moisture movement conditions can be investigated. The simulation equation verifies with benchmark datasets that was solved on Richards’ equation using a different numerical method. The twelve-soil series of Terengganu state subjected to water infiltration study subjected to flooded conditions, by exposing the soil to the highest rainfall identified in the history of Kuala Terengganu from 1985 to early 2023. The results reveal Jambu, Rhu Tapai and Rudua soil series' water infiltration rate was greater than the rainfall intensity, whereas other soil series could not accommodate the rainfall intensity. Thus, other soil series areas will likely encounter flooded conditions when exposed to heavy rainfall, as has occurred in history.

Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction.