Flood Situation Research Articles

Severity of Plasmodium vivax Malaria among Patients Presenting at Tertiary Care Hospital after Flood in Sindh – Pakistan

Malaria is one of the widely spread vector bond infectious disease responsible for grave health issues. Pakistan is one of the adversely hit country by malaria. We conducted this study to access whether there was only increase in no of malaria patients after flood in Sindh patients or clinical variance is also there. Objective: To assess the severity of Plasmodium vivax infection at a tertiary care hospital, after flood situation in Sindh. Methods: This cross-sectional study was conducted at LUMHS, Jamshoro from November 2022 to April 2023. After taking inform written consent, adult patients of age 13 to 60 years, irrespective of gender, with positive report of P. vivax malaria were included in the study. Data were recorded on predesigned proforma. Severity of malaria was determined according to the classification criteria. Data were analyzed with the help of SPSS version 23. Results: Out of total 170 patients, 52.4% female, mean age was 26.6 year. 50.6% patients of Plasmodium vivax found to suffer from severe malaria. Severe anemia, ALOC and jaundice were present in 28.2%, 21.2% and 11.2% of patients respectively. Conclusions: Enormous number of severe malaria cases due to of P. vivax was detected after floods in Sindh. Young age people were affected more, irrespective of gender. Severe anemia was the most common complication of malaria. Thrombocytopenia found frequently in patients infected with P. vivax.

Deep Reinforcement Learning-Based Rescue Resource Distribution Scheduling of Storm Surge Inundation Emergency Logistics

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

Development of medium-scale filtration device for supplying drinking water during flood situations

A scaled-up device for rapid water treatment is presented based on technologies described in earlier effort (“Point of use drinking water filtration: A microfluidic solution providing safe drinking water during flood situation,” J. Water Process Eng. 52 (2023) 103545). It comprises inertia-based microfluidic filtration, selective ion-exchange, antimicrobial activated carbon, and ultraviolet disinfection stages. The overall performance of the device is tested using synthetic, field, and flood water samples. The size of the bypass port is optimized to achieve ∼99 % filtration efficacy. The length of IX media encapsulated in mini channels is adjusted by studying its effect in limiting the dissolved solids and the hydraulic resistance. The adsorption performance and capacity of the AC at equilibrium conditions is evaluated by tests using methylene blue. To augment the filtrate throughput, both unit and module level parallelization scheme is developed. Unit filters are arranged both in planar and vertical levels to form a parallelized module (PM). Subsequently, PMs are multiplexed with a carefully designed inlet distributary, outlet, and bypass collection network. The prototype device can treat nearly 400 L of raw water in six days of continuous operation. Integrated UV reactors in the full-scale device, show complete sterilization of the influent water.

The application of integrating comprehensive evaluation and clustering algorithms weighted by maximal information coefficient for urban flood susceptibility

Different sub-regions of Zhengzhou city have various levels of sensitivity to flood due to the impact of urbanization. Thus, an accurate flood sensitivities assessment is a key tool for flood prevention and urban planning and development. To successfully link the urban flood sensitivity assessment with the real flood situation, a method combining clustering algorithm with comprehensive evaluation is presented. The proposed method is not affected by the classification standard of sensitivities levels and has a small and undemanding demand for flood data. First, Maximal Information Coefficient between conditional factors and flood is employed to determine the weight. Then, the different results are obtained by three clustering algorithms. Finally, a four-layer evaluation structure weighted by analytic hierarchy process is established to select the best flood susceptibility map. A case study in the Zhengzhou city, China shows that the positive scale amplification strategy is relatively best and the flood sensitivity of sub-regions in Zhengzhou city should be divided into four levels obtained by K-Means clustering. Hence, it supplies the valuable insights for the urban planning and flood mitigation.

The cut off efficiency of sheet piles with variation of Water level: Al-hindiya barrage as case study.

Al-Hindiya barrage is an important project to ensure safety of structures is considered as one of the important projects in Iraq, so it’s very important to checking it’s the extent of the efficiency of the cut-off wall can be determined by calculating the amount of seepage flow that occurred under the structure in different conditions, such as the difference in water levels in the upstream and downstream, using the Geo-studio SEEP/W program by taking a natural analysis and a differential analysis. Cut off and a significant drop, and also when the most dangerous flood situation occurs at the greatest level in the Upstream In this case, all the gates must be opened, i.e., a quick descent into the origin. This case was studied, and the efficiency of the cut-off wall was proven in the most dangerous case, and it is considered one of the indicators of peace for the Al-Hindiya barrage The finite element method was used to investigate cutoff walls and downstream filters to control seepage, exit hydraulic gradient, and uplift forces for dams, The Al-Hindiyah Bridge was studied and the error rate was less than 1%, studying the unloading situation at the highest level at a time approved by the executing company, and delaying the unloading process at this time at the start of the project operation This case was analyzed by studying the uplift pressure, seepage rate, seepage velocity, gradient and discharge at the upstream, i.e. from a point close to the face of the sheet pile and a point far from the face of the sheet pile at the downstream we conclude through this case the efficiency of the sheet pile at the point located in the backside, which shows the efficiency of the sheet pile significantly The study proved that when facing the Al-Hindiya Bridge, the state of flooding, all gates must be opened at an average speed for a period of 12 hours to get rid of the water in excess of the permitted storage The study proved the efficiency of the sheet bale when confronting the flood situation and at different times, by calculating the safety factor 2.24 within the permissible limits. Recommendations for suitable combinations of upstream cutoff and downstream filter are provide.Recommendations for suitable combinations of upstream cutoff and downstream filter are provide

Driver Decisions Based on Flood Warning Information

Flood warnings can be communicated through mobile devices and should convey enough information to keep the user safe during a flood situation. However, the amount of detail included in the warning, such as the depth of the flood, may vary. The purpose of this study was to investigate how to best inform drivers of floods to keep them protected. Participants were tasked to drive to a restaurant in a driving simulator after receiving instructions and a type of flood information warning during each scenario (flood, no flood, flood of 6 inches, flood of 6 inches maximum). We found that participants accepted the alternate route more when in a scenario with a flood present compared to the no-flood scenario. These results deepened the understanding of human decisionmaking and can guide future flood warning designs to keep drivers protected from flooded roadways

Mechanism and Simulation Evaluation of Urban Rainflood Induced Disasters

The mechanism and simulation evaluation of urban rain and flood disasters are necessary means for the healthy development of cities, and are of great significance for comprehensive urban governance and flood prevention. Based on the mechanism of urban disasters, this paper analyzes and studies the simulation of urban rainwater and flood and disaster risk assessment, and summarizes the methods of urban runoff generation and concentration, domestic and foreign rainwater and flood models, and disaster assessment methods. Combined with the development of computer technology, starting from the current situation of urban flood, and based on multi-source data to improve the simulation accuracy, it is proposed that rainstorm flood simulation evaluation should focus on the research of full physical mechanism model and the application of information technology.

A priority-aware sequential pattern mining method for detection of compact patterns from alarm floods

Alarm floods are commonly present in modern complex industrial process, and usually degrade the performance of the alarm systems severely. In an alarm flood, the massive alarm messages may overwhelm plant operators and submerge critical alarms, which would further worsen the situation and lead to severe consequences. One effective way to deal with alarm flood situations is to mine meaningful alarm patterns that can be used to configure dynamic alarm suppression modules that suppress irrelevant alarms in alarm floods based on certain conditions or to help with online alarm prediction. Using the existing methods in literature, alarms of higher priorities could be easily overlooked in the pattern mining as they usually appear less frequent; thus, it makes the results become unreliable. Motivated by this issue, this paper proposes a new pattern mining method to extract priority-aware compact alarm patterns from historical alarm flood sequences. The main contributions of this study are twofold: (1) a priority-aware sequential pattern mining algorithm is designed to mine alarm sequential patterns using a closed sequential pattern mining algorithm with alarm priority information incorporated; (2) a pattern compression strategy is proposed to merge similar sequences into compact patterns, making it easier to search for alarm patterns. The effectiveness of the proposed pattern mining method is demonstrated by alarm data generated using a vinyl acetate monomer chemical plant model.

An association rule mining approach to predict alarm events in industrial alarm floods

Industrial operators often experience overwhelming situations during ongoing alarm floods due to high alarm rates. In such situations, real-time assistance in the form of prediction of upcoming alarm events can ease off the decision-making for industrial operators. Accordingly, this work studies alarm prediction in alarm flood situations, and the main contribution lies in a novel association rule mining approach for real-time prediction of alarm events and their corresponding times of annunciation during an ongoing alarm flood. The proposed method is capable of performing predictions at the triggering instant and modifying the predictions with the increasing of the ongoing alarm flood. The proposed method is implemented mainly in the following steps: (1) A Compact Prediction Tree (CPT) model is modified with new features, namely, the time table and co-occurrence matrix, and constructed based on historical alarm sequences; (2) an alarm relevancy detection strategy is designed to detect and eliminate irrelevant alarms in alarm floods; (3) an online alarm prediction algorithm is designed to predict upcoming alarms at the early stage of the ongoing alarm flood; (4) the confidence intervals of the time differences between the annunciations of subsequent predicted alarm events are calculated for time prediction. To demonstrate the effectiveness of the proposed method, an industrial case study based on real alarm & event logs from an oil refinery is provided.

The problem of flooding and analysis of the ways of its solution

An analysis of literary sources and informational materials on flood situations that occurred in different historical periods on different continents and in different countries was carried out. It has been established that floods accompany mankind from the earliest times to the present day. Information about catastrophic floods has been preserved since the third millennium BC, in connection with which they should be considered as a historical category. According to the World Meteorological Organization (WMO), floods account for more than 45% of natural disasters related to water (hydrological disasters), which account for about 70% of all natural disasters on the planet. Taking into account the fact that floods are among the most dangerous natural phenomena in terms of the number of victims and the amount of damage, the problem of floods should be considered global. The global nature of the flood problem is confirmed by its following features: the problem concerns all humanity, affects the interests and fate of all countries and peoples; leads to significant social, ecological and economic losses, which, if they increase, will threaten the existence of human civilization; contributes to the aggravation of other internationally recognized global problems, such as environmental, food, social (in terms of population protection), land degradation. Taking into account the fact that in recent years the incidence of catastrophic floods has increased, their social, ecological and economic consequences have increased, as well as the number of human victims from floods, the article focuses on the need to develop a highly effective scientific and methodological toolkit for solving the problem of floods and its wide use not only in Ukraine, but also in many countries of the world. It has been established that among the many areas of systemic research of the problem of floods, the main and most important area is the fundamental improvement of the conceptual foundations of flood protection. The author's version of the universal concept of solving the flood problem is proposed, which is based on a universal algorithm for achieving the target indicator of flood protection. The maximum value of the ratio of the flood protection effect to the costs of achieving this effect was chosen as the target indicator of flood protection. The protective anti-flood effect is represented by the total damage averted by anti-flood measures, which consists of averted social, ecological and economic damages.

Real-time social media sentiment analysis for rapid impact assessment of floods

Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions.Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time.In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time.

An alternative flood measurement approach: Flood Meter

Flash floods commonly occur in urban areas due to a short period of heavy rainfall associated with the large impervious area and inadequate drainage system. An increase in the uncertainty of predicting flash floods, especially due to climate change, reduces the accuracy of the existing flood forecasting system. In such cases, the affected zone’s actual flood level data is required for more effective flood management and incident response. This study aims to develop a mobile application called Flood Meter to identify flood levels in real-time. It measured the flood level based on object scale and images taken by mobile phones. Although the validation using an actual flood event with three different types of mobile phones shows lack of accuracy but by considered the maximum flood level only 0.17 m and the result keep improving when flood level increase, it shows a potentially good agreement compared with manual measurement. The application can become an alternative method to disseminate accurate real-time flood situations as well as flood data collection for future flood risk management.

Elderly people evacuation planning in response to extreme flood events using optimisation-based decision-making systems: A case study in western Sydney, Australia

Climate change is causing an increase in the frequency and severity of floods in various regions globally, raising concerns about the efficacy of evacuation planning strategies that were created under different climate scenarios, particularly for vulnerable individuals. Urban areas are at high risk of flooding, making the evacuation of elderly people to safer locations a potential necessity in many future flooding situations. However, despite the significance of transportation planning in the evacuation of the elderly, there has been little research considering the impact of disasters on the areas undergoing evacuation. This paper aims to address this research gap by presenting an integrated evacuation approach for elderly people under flooding conditions. The proposed model combines a flood simulator with multi-objective metaheuristic algorithms to minimise the expected evacuation time for all evacuees and reduce the number of rescue vehicles used. To improve the performance of the optimisation algorithms, dominant properties are established, which are used to create a local search. The proposed approach is tested and validated using a case study in Sydney, Australia, and the findings suggest that the proposed method can generate effective evacuation plans for the elderly in a reasonable amount of time under realistic emergency scenarios brought on by extreme flood events. The study adds to the body of knowledge on disaster risk reduction strategies both theoretically and practically, and has crucial policy implications for emergency managers, planners, and decision-makers in countries where urban areas are becoming increasingly vulnerable to climate change.

A Systematic Literature Review of the Hybrid Methodologies in Assessing Flood Indirect Impacts on Transportation

As there is a staggering increase in flooding worldwide, many countries have prioritized sustainability of their transportation sector through flood impact prediction to support the transition during flooding. As such, research regarding the flood impacts on transportation has dramatically increased in recent years. Hybrid methods play an important role in simulating the flood situation and its impacts on traffic networks. This article offers a systematic literature review of existing research which employ hybrid methods to assess the indirect impacts of flooding on transportation. In this study, 45 articles are reviewed systematically to answer 8 research questions regarding modeling the indirect impacts of flooding on transportation. The hybrid techniques observed in the existing literature are discussed and along with the main barriers to precise prediction of flooding’s indirect impacts on transportation, future research directions are also suggested.

Mitigation of Flash Floods in Kokrajhar of the State of Assam in North East India

Assam, one of the northeastern state of India witnesses heavy rainfall almost every year because of its geographical location. Kokrajhar, a district of Assam witnesses flash floods at certain parts of it during rainy season. Detailed study of the reasons for occurrence of flash floods have made and presented in this paper. Incorrect estimation of maximum flood or providing undersized storm water drains are the major reasons for occurrence of flash floods. Improper construction and maintenance of storm water drains are also the reasons for flash flood occurrence. Kokrajhar, being the headquarter of Bodoland Territorial Region is coming up with large number of infrastructural development projects. However, due to flood situations, these construction projects are halted for a considerable time resulting in slower rate of completion of these projects. The present case study aims at understanding the reasons behind occurrence of flash floods at central part of Kokrajhar town and finding the appropriate preventive and remedial measures to prevent such floods. These measures will thus helps in avoiding difficulties caused to the general public during rainy season and also helps in the faster rate of development of the region.

Two-dimensional (2D) flood analysis and calibration of stormwater drainage systems using geographic information systems.

In recent years, there has been severe flooding in urban areas as well as coastal and river flooding. Urban flooding is exacerbated by climate change, urbanization, growing population, and the increase of impervious surfaces in urban areas. Stormwater drainage systems that discharge stormwater to a safe location in urban areas are becoming increasingly important. The objective of this study is to analyze and calibrate the flood performance of stormwater drainage systems currently used in the central region of Malatya in a potential flood situation using geographic information systems and the InfoWorks ICM. The model was created using the land use type, buildings, and digital elevation model (DEM), and the analysis was performed by exposing stormwater drainage systems to rainfall events of 5, 10, and 15 min of duration for return periods of 2, 5, and 10 years. The model was then validated using field-observed rainfall and flood data and its performance was evaluated using R2, NSE, RMSE, and MAE metrics. The results showed that the eight stormwater drainage systems currently in operation cannot fully convey stormwater and may pose a risk of loss of life and property in residential areas. In addition, the severity of the flooding was found to increase with an increasing return period.

An innovative approach to predict atmospheric rivers: Exploring convolutional autoencoder

Atmospheric rivers (ARs) are filamentary regions of high moisture content in mid-latitude regions through which most of the poleward moisture is being transported. These ARs carry a huge amount of water in the form of vapor and thus landfalling of these ARs may bring either a beneficial supply of water or may create hazardous flood situations and thus cause damage to life and property. These regions have been statistically characterized as intense integrated water vapor transport (IVT) regions in the troposphere based on various thresholds of magnitude, direction, and geometry. To enhance the knowledge of data-driven methods for modelling nonlinear atmospheric dynamics associated with ARs, a first ever study with data-driven methodology incorporating a Deep Learning architecture, Autoencoder has been proposed. While training the proposed model, the Adam optimizer was used to reduce the mean squared error loss and was optimized using the Rectified Linear Unit (ReLU) and Sigmoid activation functions. The prediction results of the availability of ARs at next frames by the Autoencoder were assessed by popularly used performance evaluation metrics structural similarity index metrics (SSMI), mean squared error (MSE), root mean squared error (RMSE), and peak signal to noise ratio (PSNR). We have got comparatively higher scores (average) of SSIM (0.739) and PSNR (64.422) and lower scores (average) of RMSE (0.155) and MSE (0.0247) for AR prediction from our model which signifies the accuracy of the proposed Autoencoder in capturing AR dynamics. The findings of the study could be useful in giving important insights to incorporate Deep Learning models for forecasting ARs at significant lead time and consequently reducing the risk and increasing the resilience of AR flood prone regions.

Estimation of the Evacuation Time According to Different Flood Depths

This study focused on pre-flood measures to estimate evacuation times impacted by flood depths and identify alternate routes to reduce loss of life and manage evacuation measures during flood disasters. Evacuation measures, including traffic characteristics, were reviewed according to different flood depths. Several scenarios were constructed for different flooding situations and traffic volumes. Evacuation times in the study area were evaluated and compared for all scenarios with reference to dry conditions. Results of network performance indicators compared to the dry situation showed that average speed dropped to 2 km/h, VHT rose above 200%, and VKT rose above 30%. Cumulative evacuee arrival percentage increased when flood levels were higher than 5 cm. Flood levels of 10–15, 15–20, 20–25, and 25–30 cm represented percentages of remaining evacuees at 9%, 19%, 49%, and 83%, respectively. Time taken to evacuate increased according to flood level. For flood depths of 5–30 cm, travel time increased by 40, 90, 260, and 670 min, respectively, suggesting the need for early evacuation before the flood situation becomes serious.

Assessing Urban Flood Hazard Vulnerability Using Multi-Criteria Decision Making and Geospatial Techniques in Nabadwip Municipality, West Bengal in India

The flood hazard risks and vulnerability in the urban areas alongside major rivers of India have been gradually increasing due to extreme climatic events. The present study is intended to assess flood hazard vulnerability and potential risk areas and aims to ascertain the management strategies in Nabadwip Municipality, a statutory urban area of West Bengal. The multi-criteria decision making (MCDM) of selected criteria and geospatial techniques have been employed to determine the urban flood vulnerability in the study area. The study has been conducted using secondary datasets including relevant remotely sensed data and participant observation. The potential flood-affected zones have been determined using the normalized difference flood index (NDFI) and flood vulnerability index (FVI). The analysis of the standardized precipitation index (SPI) of 20 years of monthly precipitation shows the variability of seasonal rainfall distribution in the study area. Furthermore, the spatial distribution of the composite Ibrahim index of socio-economic development accents that the urban development of the study area was uneven. The municipal wards situated in the central and northeastern portions of Nabadwip Municipality were extremely vulnerable, whereas the western and southwestern wards were less vulnerable. It is also revealed from the strengths–weaknesses–opportunities–challenges (SWOC) of the principal management strategies of the flood situation analysis that the unplanned sewerage system is one of the most effective weaknesses in the area. All-embracing and integrative flood management strategies need to be implemented in the study area considering the intra-regional vulnerability and development for the resilient and sustainable development of the study area.

The Optical Trapezoid Model (OPTRAM)-based soil moisture estimation using Landsat 8 data

ABSTRACT Soil moisture is an important factor that can be used for referring to flood situations, monitoring drought estimation, and changes in the environment such as storms and soil erosion. The Optical Trapezoid Model (OPTRAM) estimates surface soil moisture using shortwave infrared transformed reflectance (STR) and the Normalised Difference Vegetation Index (NDVI) in trapezoidal space. In this study, Landsat 8 satellite data were acquired for May (2019) and October (2019) and used for analysis in the Narmadapuram district of Madhya Pradesh, India. Validation of the retrieved soil moisture was done using microwave satellite data available on the VEDAS portal. The output maps show that soil moisture values vary from 0.0235 (m3/m3) to 0.162 (m3/m3) for May 2019 and from 0.417 to 0.781 (m3/m3) for October 2019. The data obtained from the VEDAS portal has the average change in soil moisture values in May and October 2019 as 0.04 m3/m3 and 0.55 m3/m3, respectively. The outcome of the study shows that the model has nearly the same accuracy as the VEDAS (Visualisation of Earth Observation Data and Archival System) portal data.