Road Closures Research Articles

Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach

Abstract. This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.

Women living in the region under an emergency command post are experiencing delays in reaching health facilities: a multi-center cross-sectional study

IntroductionThe time between seeking emergency care and receiving the first healthcare service is referred to as delays in reaching health facilities. Delay in reaching health facilities had three main sub-themes in different studies. These include (i). Availability of transportation and infrastructure, (ii). Distance from health facilities and (iii). Lack of finance for transportation. Although there are plenty of findings about maternal delays in reaching health facilities, studies do not address the delay situation of women in conflict zones all over the world. In Ethiopia, specifically, we saw women delayed in healthcare service due to restricted movement and closure of roads, even for ambulances, at night time. MethodsA multi-center cross-sectional study was conducted among mothers attending public health facilities in the south Gondar zone, Ethiopia from September 01/2023 to April 30/2024. The data was collected by four BSc degree-holder midwives, using structured questionnaires after training was given for a day in each hospital. A pretest using 5% of the sample size was conducted. Descriptive statistics, and multivariable logistic regression analyses were used to identify factors associated with delays in reaching health facilities. Variables having p-value ≤ 0.2 in the bi-variable analysis were fitted into multiple logistic regression models.Result The prevalence of delay in reaching health facilities among women who are living in the region of emergency command post was 54.9% CI (42.04-59.87%). Living in rural areas, non-availability of public transport during the armed conflict, labor onset at nighttime, road closures by the armed forces, and non-availability of ambulances were the factors that contributed to the delay during the emergency command post. ConclusionThe magnitude of delay in reaching health facilities among women who are living in the region of emergency command post was high.

Commuter exposure to smoke and particulate matter air pollution in Auckland during the 2019 New Zealand International Convention Centre fire

ABSTRACT Human mobility typically exhibits temporal and spatial predictability. However, during hazardous events, roadways, footpaths and public transport networks can be disrupted by detours, closures and congestion. Urban fires, exemplified by the October 2019 New Zealand International Convention Centre (NZICC) fire in Auckland, New Zealand, are on the rise, posing threats to life, property and mobility. In this study, we employ geospatial analyses to investigate the impact of the NZICC fire on human mobility, encompassing both driving and walking. We generate predicted surfaces of particulate matter (PM2.5 and PM10) from seven local fixed air monitoring stations to estimate network-based exposure and inhalation dosage during travel. High levels of air pollution during the fire exceeded baseline concentrations, surpassing National Environmental Standards for Air Quality (NESAQ) and World Health Organization (WHO) limits for both PM2.5 and PM10. Private car commuters faced delays and congestion due to road closures, prolonging smoke exposure. Pedestrians, including those accessing essential public transportation infrastructure, experienced unavoidable exposure to smoke along the fastest routes from NZICC to places like Britomart train station and the Downtown Ferry. We recommend increasing the availability and dissemination of air pollutant monitoring data to enhance public awareness of the health risks associated with smoke exposure.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Road and Bridge Blocking System in Flood Situation

Abstract: The system uses hydraulic or mechanical barriers that can be quickly opened in response to rising water levels, preventing access to barriers and bridges. Up to date data from weather forecasters and water gauges provide effective protection to ensure closure times before flooding reaches critical levels. In addition, the central control centre provides timely updates to the public via mobile and digital signage applications, encouraging collaboration with emergency services and local authorities. The purpose of this document is to provide guidance to highway authorities and other relevant organisations on road drainage. One problem that arises during floods is the closure of roads due to flooding, especially on roads located near rivers and canals. Most brides give the height above the highest water level, but the approach to the bridges often does not give the required height. As a result, the road was flooded while the bridge was standing

Toward the Development of an Impact-Based Decision Support Tool for Surface-Transportation Hazards. Part II: An Hourly Winter Storm Severity Index

Abstract In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety. Significance Statement A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.

Quantifying the vulnerability of road networks to flood-induced closures using traffic simulation

Disruptions in road networks can significantly impact traffic flow, necessitating the identification and mitigation of vulnerable components. This study presents a methodology to assess the vulnerability and robustness of road networks, addressing the research gap in comprehensive approaches that effectively evaluate both controlled intersections and uncontrolled/free-flow highway segments. The proposed framework employs traffic simulation tools to model road closures within designated study areas, focusing on networks in Qatar. Performance measures such as average control delay and volume to capacity ratios are utilized to calculate vulnerability indices for network components. The two-pronged approach quantifies the impact of simulated closures at both the component and network levels, revealing critical intersections and road segments that markedly affect network performance during disruptions. The findings contribute to the field by providing transportation engineers with an effective quantitative tool to detect vulnerabilities in their road network, guiding design plans and actions to mitigate the potential harmful impacts of disruptive events on transportation infrastructure. The methodology's scalability ensures its applicability to various operational contexts, offering significant insights for transportation planning and infrastructure resilience. By establishing a comprehensive vulnerability analysis framework, this study enhances the understanding and management of road network vulnerabilities in complex urban environments. Further research on integrating dynamic traffic assignment and real-time data is recommended to further enhance the predictive accuracy and applicability of the proposed framework, ultimately improving transportation infrastructure resilience.

Safety improvement in open-pit mines with challenging mining conditions through upgrading avalanche prevention measures

One of the most common problems mining companies face in developing open-cut deposits in complicated geological and climatic conditions is avalanches during the spring-winter work period. This phenomenon is accompanied by a rapid displacement of snow masses, which can lead to catastrophic consequences. In this regard, the article aims to develop a modernized set of measures for avalanche protection. Prevention of avalanches of catastrophic scale, leading to blockages, road closures, and accidents, and the introduction of measures to reduce accidents and injuries in quarries and dumps, is a time-consuming task that requires serious approaches to its solution. The main research method is analyzing the applied anti-avalanche measures for active impact on snow cover and considering the possibility of using safer methods for active impact on avalanches at a particular mining enterprise. This approach allows for the evaluation of the advantages and disadvantages of the avalanche measures used and for choosing a set of measures to ensure more effective avalanche protection. The authors analyzed the existing shortcomings in the regulatory documents related to the provision of avalanche protection and anti-avalanche measures. It was found that they are not able to effectively ensure the development of internal local documents of enterprises in the field of anti-avalanche measures at the present stage. The resulting study showed that a comprehensive solution to the problem is needed through the introduction of several anti-avalanche devices to reduce the likelihood of avalanches of catastrophic scale. For the first time, the authors attempted to justify the possibility of using a modernized set of measures for the preliminary descent of snow avalanches to solve the problem of avalanche protection in the conditions of a real mountain object. The results obtained can be used to reduce the level of occupational injuries by reducing or eliminating accidents associated with unforeseen avalanches during the operation of an existing quarry.

A novel seismic vulnerability assessment for the urban roadway by using interval valued fermatean fuzzy analytical hierarchy process

AbstractSeismic activity poses significant challenges to urban road infrastructure, often resulting in road closures due to the combined effects of damaged buildings and affected road networks. In contrast, the resilience of roads is crucially important for all kinds of relief activities after an earthquake in this context, this study outlines a methodological framework for assessing the vulnerability of urban road infrastructure to seismic activity. By integrating various criteria within an Interval-valued Fermatean fuzzy Analytic Hierarchy Process framework, the approach offers a comprehensive analysis of vulnerability, considering both quantitative and qualitative factors. This method is a weighting method that has not been used before in MCDM studies in the field of earthquakes. A risk factor is obtained for each road section by using this comprehensive analysis of the vulnerability. This integrated approach considers the interplay between damaged buildings, road networks, and disaster response mechanisms, thereby enhancing the ability to anticipate and respond to seismic events effectively. The study conducts a case study in Istanbul, Turkey, a seismic-prone area, to validate the effectiveness of the proposed methodology. Key findings indicate that the approach can identify and quantify vulnerabilities within the transport network, enabling the identification of high-risk areas for necessary mitigation measures. Moreover, the methodology’s validity is confirmed through a validation study in Gölbaşı district, Adıyaman, Türkiye, which experienced severe damage during earthquakes on 6 February 2023 earthquakes. By providing a structured and comprehensive vulnerability analysis, the research aims to contribute to the resilience of urban infrastructure, particularly in earthquake-prone regions.

Prediction of Traffic Flow in Vehicular Ad-hoc Networks using Optimized Based-Neural Network

Traffic flow prediction is crucial for managing traffic, reducing pollution, ensuring public safety, and is a vital element of Intelligent Transportation Systems. Furthermore, the variables that influence traffic patterns (such as public events, road closures, and accidents) are predominantly unforeseen, rendering the prediction of traffic flow an intricate undertaking. The objective of this research is to enhance the accuracy of traffic flow forecast by employing a novel strategy that utilizes feedforward Neural-Networks and the Quasi-Newton method for optimization. The proposed strategy decreases the error factor based on the Lagrange multiplier and Jacobian vector. This enhancement has resulted in expedited convergence throughout the process of learning. The sample was chosen using datasets provided by the Traffic Monitoring System, specifically from Highway England, Performance Measurement System, and Maryland 511, for the year 2023. In order to assess the proposed model, the research outcomes are juxtaposed with other established prediction methodologies. The study revealed that the research model that was established performed better than previous prediction strategies based on measures of mean absolute error and root mean squared error.

High-Profile Convoy Disruptions: Exploring Socioeconomic and Environmental Ramifications of Road Closures

Congestion persists despite various demand management techniques, particularly for handling recurrent congestion. However, non-recurrent congestion from events like VIP movements poses unique challenges, especially during peak hours. This study investigates the environmental and economic impacts of road blockages due to VIP movements in developing countries, focusing on Pakistan. Considering practiced standard operating procedures associated with VIP movements, this study finds significant delays and economic burdens in debt-ridden economies. It uses discrete choice modeling and microsimulation techniques to evaluate the value of travel time and quantifies road blockage effects on fuel consumption, travel time, and carbon emissions. Data from central blockage locations in Rawalpindi and Islamabad reveal a value of travel time estimated at 1.77 USD/h, with income and gender significantly influencing mode choices during VIP movements. Moreover, road blockages exceeding two minutes substantially negatively impact the environment and economy, particularly in developing nations. Urgent action is needed for effective mitigation strategies and sustainable transportation policies to address the detrimental effects and promote alternative transportation modes. Recommendations include limiting VIP blockages to a maximum of two minutes and implementing policies to discourage private car usage. Despite limitations, the study emphasizes the critical role of sustainable transportation policies in enhancing the well-being of road users in developing nations.

Categorizing resident evacuation behaviors within a flood-protected area

ABSTRACT Flood management due to global warming is one of the most important issues to be considered worldwide. Communicating about flood risk between government and residents is important for enhancing awareness of residents’ evacuation. However, not enough risk information is provided to residents for evacuation from floods (e.g. where, when, and how to evacuate). Therefore, an evacuation analysis linked with a time series of inundation depths was conducted in Kawajima Town, which is surrounded by a ring levee in the watershed of the Arakawa River, Japan. According to the analysis of evacuation by walking, the evacuation should be started by the time an evacuation instruction is issued to avoid getting caught in flooding before reaching shelters. Although the analysis of evacuation by car did not consider road and bridge closures, it indicated that approximately 40% of the town’s residents would likely have difficulty evacuating to even the nearest bridge or road. The central and southern part of the town was also identified as an area where a wide-area evacuation, i.e. outside the town, would be difficult if road and bridge closures were considered. The risk map identifies high-risk areas and districts in the central, southern, and southwestern parts of the town. It also indicates the need for structures for emergent use, such as small mounds or towers for evacuation starting at the breaching time (BT) in these areas. Since there is no area where all residents can evacuate at the start of breaching time, it is necessary to start evacuation to shelters immediately after the evacuation instruction is issued. Because the number of municipalities with similar problems will increase in the future in Japan, the study of evacuation plans from flood inundation for residents in Kawajima Town is expected to serve as a model case for other municipalities to consider.

Reconnaissance report on geotechnical damage caused by Tropical Cyclone Jasper in December 2023

This paper is focussed on the Department of Transport and Main Roads (TMR) initial geotechnical response to damages caused by Tropical Cyclone Jasper (TC Jasper) to road infrastructure and batter slopes along the state-controlled road network in TMR’s Far North Queensland (FNQ) District of Queensland, Australia. TC Jasper delivered extreme rainfalls over six days between 13 and 18 December 2023 in FNQ District. The highest accumulated total being 2,096mm in the area of Port Douglas and Mossman north of Cairns. Within areas of the TC Jasper path, rare to extremely rare rainfall occurrences resulted in severe flooding and geotechnical damage and closure of key state-controlled roads in FNQ. The roads closed included all four range crossings providing access to the Northern Tablelands. Communities were isolated, and tourism and regional transport severely impacted. This paper provides a report on the geotechnical damage caused by TC Jasper to seven key state-controlled roads in TMR’s FNQ District, and outcomes from the event reconnaissance approach used by TMR’s Engineering and Technology Geotechnical Section in collaboration with TMR FNQ District office and RoadTek teams to re-open most of the closed roads and secure significantly damaged road sections within four days of the cyclone passing. Ongoing recovery efforts are progressively re-opening remaining roads.

A probabilistic bi‐objective model for a humanitarian location‐routing problem under uncertain demand and road closure

AbstractEffective planning and execution of humanitarian aid logistics activities ensure that disaster‐related losses are minimized. This study addresses a tactical‐level pre‐disaster humanitarian logistics problem where a decision‐maker decides on cross‐dock locations by taking potential vehicle routes into account. A decision support model is proposed for the location selection and distribution operations in humanitarian logistics with explicit fuel consumption estimation. In the addressed problem, the demand amount of each node depends on probabilistic disaster scenarios. Probabilities of whether each arc/road is open or closed and heterogeneous vehicle fleet in terms of vehicle sizes are also respected. The model is formulated as probabilistic bi‐objective mixed integer linear programming, whose objectives are minimization of the total cost (i.e., fuel cost, vehicle fixed cost, and fixed opening cost) and total travel time. To the best of our knowledge, the proposed decision support model is unique in terms of the features considered simultaneously. The applicability of the model is demonstrated by the case study and subsequent numerical analyses of a possible earthquake in the Kartal district of Istanbul. The proposed model is shown to have the potential to support decision‐makers in preparation for a disaster. A solution approach based on a clustering algorithm has been also proposed to solve larger instances of the problem. The effectiveness of this heuristic has been demonstrated through its application to larger‐scale problems.

Use of linear features by mammal predators and prey in managed boreal forests

In managed boreal forests, logging operations maintain high levels of anthropogenic disturbance in the ecosystem. The establishment of permanent anthropogenic linear features such as logging roads in the landscape may be a major factor in the predator-prey system. Logging roads may potentially improve the numerical and functional response of predators. Using camera traps, our objective was to explain according to local and landscape factors how the number of uses by wolves, black bears, lynx and moose, varies along different natural and anthropogenic linear features during the snow-free season. In western Quebec (Canada), the managed forest south of Val-d’Or encloses an isolated caribou population facing extinction that requires active restoration of their habitat. In this site, we used stratified random selection of gravel forest roads (n = 33), winter forest roads (n = 28) and riparian areas (n = 19) to compare their characteristics and number of uses by the four species. For wolves, black bears, and lynx, positive differences in lateral cover between the surroundings and the linear feature mainly explained their number of uses. Number of uses by wolves and lynx were positively related to use by their respective prey species (moose and snowshoe hare). Gray wolf use was also positively affected by distance to a higher forest road class and negatively affected by distance to the nearest urban area. Gravel forest roads had the highest number of uses by all species, as they showed greater positive differences in lateral cover as compared to the surroundings area due to their limited vegetation growth and by frequent maintenance activities. We recommend that restoration efforts aimed at forest road closures should target roads with a high value of difference in lateral cover, which is particularly the case in most gravel roads. Lower lateral cover on these linear feature as compared to their surroundings area favors the movement of predators and alternative prey. Our results thus suggest that investing in gravel roads restoration can benefit conservation efforts in caribou habitat.

Accounting for traffic disturbance in road infrastructure management: Optimal maintenance and rehabilitation planning for the society

Maintaining a high level of network performance at minimal cost is a central objective for road infrastructure management. Going beyond agency costs, decisions must consider the economic and environmental impacts of maintenance and repairs strategies due to traffic delays on commuters and providers of goods and services. A dynamic programming model is presented to determine optimal management policies for current and proposed paradigms by selecting the best maintenance and rehabilitation activities at each time step over the time horizon given the concomitant and predicted states of the bridges within the network as well as predicted future origin-destination flows. The empirical analysis is demonstrated for a small network of three viaducts in an urban environment in Montréal, Canada. The incremental user costs due to interventions on the structures of the network are obtained through traffic simulations based on trip-survey data to evaluate travel delays for all possible combinations of concomitant partial or complete road closures. The results show that optimal infrastructure management strategies in space and time differ substantially depending on whether only agency costs are used, or if policies also reflect socio-economic goals (including sustainability). The main findings indicate that by including user costs in decision processes, significant reductions in user costs are achieved with minimal impact on operator costs, thus easily satisfying a cost/benefit criterion. The findings provide new evidence for policymakers that the optimal sequencing of interventions in time and space on a network can significantly reduce societal costs, sustainability objectives and environmental impacts.

تأثير طبوغرافية الأرض على تخطيط المدن بإستخدام تقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية

This research aimed to show case how remote sensing technologies and geographic information systems can be utilized to identify issues arising from the Earths terrain. The study focused on the water accumulation during post rainfall, in the entrance of Tripoli specifically in the Tajoura (bivy) area. This water buildup often leads to traffic congestion resulting in road closures and rising water levels that sometimes reach building entrances. Such circumstances affect the planning of the area and its neighboring regions. Satellite images sourced from the US geological survey website were employed to analyze features of the study area and present them through digital maps illustrating surface topographical characteristics, like area shape, land slopes, water flow patterns, etc. Various analyses were conducted including assessments, hydrological studies, understanding structures and soil types within the study region with an objective to pinpoint core issues related to this phenomenon and explore potential solutions. Keywords: Remote sensing, geographic information systems, urban planning, topography, water buildup.

A novel technique for multi-objective sustainable decisions for pavement maintenance and rehabilitation

To maintain pavement in good condition while considering financial costs and sustainability, it is necessary to develop a comprehensive pavement management plan. Pavement Maintenance and Rehabilitation (M&R) consists of two essential components: firstly, predicting the pavement condition within a specified timeframe, and secondly, employing an appropriate optimization algorithm. This study utilized three ensemble learning techniques including extreme gradient boosting, categorical boosting, and light gradient boosting machine to develop accurate predictions about the pavement condition. Subsequently, the most accurate prediction technique, which was extreme gradient boosting, was combined with non-dominated sorting genetic algorithm III which is a multi-objective metaheuristic optimization algorithm, resulting in a hybrid technique that offers highly accurate multi-objective maintenance and rehabilitation planning. Although previous studies neglected important criteria such as road closure in the optimization process, this study takes into account four objective functions including greenhouse gas emission, M&R cost, pavement condition, and road closure to be minimized over a 5-year program. This process generated 52 non-dominated optimal solutions known as the Pareto front. To compare and rank various optimal maintenance and rehabilitation plans, grey relational analysis was employed. The results suggested that there is a direct correlation between M&R costs and GHG emissions. Minimizing only pavement conditions in the planning can significantly increase GHG emissions, M&R costs, and road closure. Implementing preventive M&R actions can reduce M&R costs and overall road closure while light and medium rehabilitation actions are recommended to optimize the condition of pavements.

Underground infrastructure failures and their impact on road traffic restrictions and consequences for society – best practices based on the example of the city of Płock

Infrastructure failures occurring in road lanes often lead to partial or complete road closures. Works conducted on the roadway often hinder, and sometimes completely prevent, vehicle traffic. In the case of complete road closures, detours must be designated. The occurrence of failures leads to unplanned restrictions, as the specific time of the incident cannot be determined, and traffic participants are unaware of the disruptions. All of this can ultimately lead to a paralysis of the transportation system. Failures in underground infrastructure can have significant consequences for road traffic, leading to disruptions and imposing various challenges on society. This study focuses on exploring the impact of underground infrastructure failures on road traffic limitations and their repercussions for the community. The city of Płock serves as a case study, providing insights into effective practices implemented to address such challenges

A study of road closure due to rainfall and flood zone based on logistic regression

This study investigates the possibility of road closure due to flooding, based on varying amounts of rainfall and in different flood zones. Logistic regression analysis was employed, utilizing the road closure data from TxDOT in 2017 and the daily rainfall data provided by Harris County Flood Warning System (HCFWS). The definition of flood zones were established in three different contexts: the overall road network area, areas recognized as prone by FEMA, and areas where road closure occurred during Hurricane Harvey. Rainfall data were segmented into periods spanning 1–4 days. The findings indicate a positive correlation between rainfall intensity and the likelihood of road closures. In addition, Within a four-day window, the rainfall in the current day and 3-day prior played a more influential role in predicting road closure. Rainfall on the first day tended to wash debris into drainage pipeline, leading to blockage and substantially diminishing street drainage capabilities. This, in turn, heightened the susceptibility of roads to flooding in subsequent days. Finally, an analysis of the 2017 road closure and rainfall data revealed a discrepancy between the flood zone delineated by FEMA and those identified in this study. Nonetheless, critical infrastructures were present in both categories of areas. This study demonstrates how road closure predictions can be instrumental in identifying optimal routes to critical infrastructures, such as hospitals, in flood prone areas.