Prior Determination of Flash Floods: Artificial Intelligence Based Predictive Analysis Using Modified Cuckoo Search

Flash floods can demolish infrastructure and property within seconds as they are very sudden. Flash floods are the main cause of the casualties and loss of properties. Existing natural disaster prediction algorithms contains false alarms. Indefinite techniques have been applied to overcome this leading issue in many countries. A competent flood management system must have the potential and tendency to identify the flash floods and atmospheric and climatic changes on early basis with less false alarm rate. Techniques which have been designed for the flash flood investigation may be categorized into following types a. Sensors based direct measurement b. Radar images c. Satellite based X-band images. The proposed research consisted of Artificial intelligence-based decision making for multi-modal sensing (direct measurement from multi-resolution sensors). A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio. Moreover, modified multi-layer feed forward neural network optimized by article swarm optimization algorithm results have been compared with the cuckoo search, modified cuckoo search, particle swarm optimization and Multi-layer perceptron neural network configurations for the validation purpose.

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.

Flash flood is a recurrent natural hazard with substantial impacts in the Southeast US (SEUS) due to the frequent torrential rainfalls that occur in the region, which are triggered by tropical storms, thunderstorms, and hurricanes. Flash floods are costly natural hazards, primarily due to their rapid onset. Therefore, predicting property damage of flash floods is imperative for proactive disaster management. Here, we present a systematic framework that considers a variety of features explaining different components of risk (i.e. hazard, vulnerability, and exposure), and examine multiple machine learning methods to predict flash flood damage. A large database of flash flood events consisting of more than 14 000 events are assessed for training and testing the methodology, while a multitude of data sources are utilized to acquire reliable information related to each event. A variable selection approach was employed to alleviate the complexity of the dataset and facilitate the model development process. The random forest (RF) method was then used to map the identified input covariates to a target variable (i.e. property damage). The RF model was implemented in two modes: first, as a binary classifier to estimate if a region of interest was damaged in any particular flood event, and then as a regression model to predict the amount of property damage associated with each event. The results indicate that the proposed approach is successful not only for classifying damaging events (with an accuracy of 81%), but also for predicting flash flood damage with a good agreement with the observed property damage. This study is among the few efforts for predicting flash flood damage across a large domain using mesoscale input variables, and the findings demonstrate the effectiveness of the proposed methodology.

5 - Assigning Travel-Activity Patterns Based On Socio-Demographics for Flood Risk Assessment

<strong class="journal-contentHeaderColor">Abstract.</strong> In recent years, flash floods repeatedly occurred in temperate regions of central western Europe. Unlike in Mediterranean catchments, this flooding behaviour is unusual. In the past, and especially in the 1990s, floods were characterized by predictable, slowly rising water levels during winter and driven by westerly atmospheric fluxes (Pfister et al., 2004). The intention of this study is to link the recent occurrence of flash floods in central western Europe to extreme precipitation and specific atmospheric conditions to identify the cause for this apparent shift. Therefore, we hypothesise that an increase in extreme precipitation events has subsequently led to an increase in the occurrence of flash flood events in central western Europe and all that being caused by a change in the occurrence of flash flood favouring atmospheric conditions. To test this hypothesis, we compiled data on flash floods in central western Europe and selected precipitation events above 40 mm h^&minus;1 from radar data (RADOLAN, DWD). Moreover, we identified proxy parameters representative for flash flood favouring atmospheric conditions from the ERA5 reanalysis dataset. High specific humidity in the lower troposphere (q &ge; 0.004 kg kg^&minus;1), sufficient latent instability (CAPE &ge; 100 J kg^&minus;1) and weak deep-layer wind shear (DLS &le; 10 m s^&minus;1) proved to be characteristic for long-lasting intense rainfall that can potentially trigger flash floods. These atmospheric parameters, as well as the flash flood and precipitation events were then analysed using linear models. Thereby we found significant increases in atmospheric moisture contents and increases in atmospheric instability. Parameters representing the motion and organisation of convective systems occurred slightly more often or remained unchanged in the time period from 1981&ndash;2020. Moreover, a trend in the occurrence of flash floods was confirmed. The number of precipitation events, their maximum 5-minute intensities as well as their hourly sums were however characterized by large inter-annual variations and no trends could be identified between 2002&ndash;2020. This study therefore shows that the link from atmospheric conditions via precipitation to flash floods cannot be traced down in an isolated way. The complexity of interactions is likely higher and future analyses should include other potentially relevant factors such as intra-annual precipitation patterns or catchment specific parameters.

In recent years, flash floods repeatedly occurred in temperate regions of central western Europe. Unlike in Mediterranean catchments, this flooding behaviour is unusual. In the past, and especially in the 1990s, floods were characterized by predictable, slowly rising water levels during winter and driven by westerly atmospheric fluxes (Pfister et al., 2004). The intention of this study is to link the recent occurrence of flash floods in central western Europe to extreme precipitation and specific atmospheric conditions to identify the cause for this apparent shift. Therefore, we hypothesise that an increase in extreme precipitation events has subsequently led to an increase in the occurrence of flash flood events in central western Europe and all that being caused by a change in the occurrence of flash flood favouring atmospheric conditions. To test this hypothesis, we compiled data on flash floods in central western Europe and selected precipitation events above 40 mm h−1 from radar data (RADOLAN, DWD). Moreover, we identified proxy parameters representative for flash flood favouring atmospheric conditions from the ERA5 reanalysis dataset. High specific humidity in the lower troposphere (q ≥ 0.004 kg kg−1), sufficient latent instability (CAPE ≥ 100 J kg−1) and weak deep-layer wind shear (DLS ≤ 10 m s−1) proved to be characteristic for long-lasting intense rainfall that can potentially trigger flash floods. These atmospheric parameters, as well as the flash flood and precipitation events were then analysed using linear models. Thereby we found significant increases in atmospheric moisture contents and increases in atmospheric instability. Parameters representing the motion and organisation of convective systems occurred slightly more often or remained unchanged in the time period from 1981–2020. Moreover, a trend in the occurrence of flash floods was confirmed. The number of precipitation events, their maximum 5-minute intensities as well as their hourly sums were however characterized by large inter-annual variations and no trends could be identified between 2002–2020. This study therefore shows that the link from atmospheric conditions via precipitation to flash floods cannot be traced down in an isolated way. The complexity of interactions is likely higher and future analyses should include other potentially relevant factors such as intra-annual precipitation patterns or catchment specific parameters.

In recent years, flash floods repeatedly occurred in temperate regions of central western Europe. Unlike in Mediterranean catchments, this flooding behaviour is unusual. In the past, and especially in the 1990s, floods were characterized by predictable, slowly rising water levels during winter and driven by westerly atmospheric fluxes (Pfister et al., 2004). The intention of this study is to link the recent occurrence of flash floods in central western Europe to extreme precipitation and specific atmospheric conditions to identify the cause for this apparent shift. Therefore, we hypothesise that an increase in extreme precipitation events has subsequently led to an increase in the occurrence of flash flood events in central western Europe and all that being caused by a change in the occurrence of flash flood favouring atmospheric conditions. To test this hypothesis, we compiled data on flash floods in central western Europe and selected precipitation events above 40 mm h−1 from radar data (RADOLAN, DWD). Moreover, we identified proxy parameters representative for flash flood favouring atmospheric conditions from the ERA5 reanalysis dataset. High specific humidity in the lower troposphere (q ≥ 0.004 kg kg−1), sufficient latent instability (CAPE ≥ 100 J kg−1) and weak deep-layer wind shear (DLS ≤ 10 m s−1) proved to be characteristic for long-lasting intense rainfall that can potentially trigger flash floods. These atmospheric parameters, as well as the flash flood and precipitation events were then analysed using linear models. Thereby we found significant increases in atmospheric moisture contents and increases in atmospheric instability. Parameters representing the motion and organisation of convective systems occurred slightly more often or remained unchanged in the time period from 1981–2020. Moreover, a trend in the occurrence of flash floods was confirmed. The number of precipitation events, their maximum 5-minute intensities as well as their hourly sums were however characterized by large inter-annual variations and no trends could be identified between 2002–2020. This study therefore shows that the link from atmospheric conditions via precipitation to flash floods cannot be traced down in an isolated way. The complexity of interactions is likely higher and future analyses should include other potentially relevant factors such as intra-annual precipitation patterns or catchment specific parameters.

Risk assessment and sensitivity analysis of flash floods in ungauged basins using coupled hydrologic and hydrodynamic models

Recently, the earth’s climate has changed considerably, leading to several hazards, including flash floods (FFs). This study aims to introduce an innovative approach to mapping and identifying FF exposure in the city of Tetouan, Morocco. To address this problem, the study uses different machine learning methods applied to remote sensing imagery within the Google Earth Engine (GEE) platform. To achieve this, the first phase of this study was to map land use and land cover (LULC) using Random Forest (RF), a Support Vector Machine (SVM), and Classification and Regression Trees (CART). By comparing the results of five composite methods (mode, maximum, minimum, mean, and median) based on Sentinel images, LULC was generated for each method. In the second phase, the precise LULC was used as a related factor to others (Stream Power Index (SPI), Topographic Position Index (TPI), Slope, Profile Curvature, Plan Curvature, Aspect, Elevation, and Topographic Wetness Index (TWI)). In addition to 2024 non-flood and flood points to predict and detect FF susceptibility, 70% of the dataset was used to train the model by comparing different algorithms (RF, SVM, Logistic Regression (LR), Multilayer Perceptron (MLP), and Naive Bayes (NB)); the rest of the dataset (30%) was used for evaluation. Model performance was evaluated by five-fold cross-validation to assess the model’s ability on new data using metrics such as precision, score, kappa index, recall, and the receiver operating characteristic (ROC) curve. In the third phase, the high FF susceptibility areas were analyzed for two-way validation with inundated areas generated from Sentinel-1 SAR imagery with coherent change detection (CDD). Finally, the validated inundation map was intersected with the LULC areas and population density for FF exposure and assessment. The initial results of this study in terms of LULC mapping showed that the most appropriate method in this research region is the use of an SVM trained on a mean composite. Similarly, the results of the FF susceptibility assessment showed that the RF algorithm performed best with an accuracy of 96%. In the final analysis, the FF exposure map showed that 2465 hectares were affected and 198,913 inhabitants were at risk. In conclusion, the proposed approach not only allows us to assess the impact of FF in this study area but also provides a versatile approach that can be applied in different regions around the world and can help decision-makers plan FF mitigation strategies.

Using statistical functions and hydro-hydraulic models to develop human vulnerability curves for flash floods: The flash flood of the Taitou catchment (China) in 2016

Floods are among the most costly natural hazards that affect Europe and Germany, demanding a continuous adaptation of flood risk management. While social and economic development in recent years altered the flood risk patterns mainly with regard to an increase in flood exposure, different flood events are further expected to increase in frequency and severity in certain European regions due to climate change. As a result of recent major flood events in Germany, the German flood risk management shifted to more integrated approaches that include private precaution and preparation to reduce the damage on exposed assets. Yet, detailed insights into the preparedness decisions of flood-prone households remain scarce, especially in connection to mental impacts and individual coping strategies after being affected by different flood types. This thesis aims to gain insights into flash floods as a costly hazard in certain German regions and compares the damage driving factors to the damage driving factors of river floods. Furthermore, psychological impacts as well as the effects on coping and mitigation behaviour of flood-affected households are assessed. In this context, psychological models such as the Protection Motivation Theory (PMT) and methods such as regressions and Bayesian statistics are used to evaluate influencing factors on the mental coping after an event and to identify psychological variables that are connected to intended private flood mitigation. The database consists of surveys that were conducted among affected households after major river floods in 2013 and flash floods in 2016. The main conclusions that can be drawn from this thesis reveal that the damage patterns and damage driving factors of strong flash floods differ significantly from those of river floods due to a rapid flow origination process, higher flow velocities and flow forces. However, the effects on mental coping of people that have been affected by flood events appear to be weakly influenced by different flood types, but yet show a coherence to the event severity, where often thinking of the respective event is pronounced and also connected to a higher mitigation motivation. The mental coping and preparation after floods is further influenced by a good information provision and a social environment, which encourages a positive attitude towards private mitigation. As an overall recommendation, approaches for an integrated flood risk management in Germany should be followed that also take flash floods into account and consider psychological characteristics of affected households to support and promote private flood mitigation. Targeted information campaigns that concern coping options and discuss current flood risks are important to better prepare for future flood hazards in Germany.

Simulating flash flood hydrographs and behavior metrics across China: Implications for flash flood management

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs’ changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

Abstract. This work focuses on the analysis and characterization of the flash flood events occurring during summer in Catalonia. To this aim, a database with information about the social impact produced by all flood events recorded in Catalonia between 1982 and 2007 has been built. The social impact was obtained systematically on the basis of news press data and, occasionally, on the basis of insurance data. Flood events have been classified into ordinary, extraordinary and catastrophic floods, following the proposal of Llasat et al.~(2005). However, bearing in mind flash flood effects, some new categories concerning casualties and car damage have also been introduced. The spatial and temporal distribution of these flood events has been analyzed and, in an effort to better estimate the social impact and vulnerability, some indicators have been defined and analyzed for a specific region. These indicators allow an analysis of spacial and temporal trends as well as characterization of the events. Results show a flash-flood increase in summer and early autumn, mainly due to inter-annual and intra-annual changes in population density.

&amp;lt;p&amp;gt;In the second half of the 20&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; century, hydrological regimes in central Western Europe were largely characterised by large-scale winter floods. This type of event was predominantly triggered by westerly atmospheric fluxes, bringing moist and mild air masses from the Atlantic Ocean to the European continent. Since the late 1990&amp;amp;#8217;s, major flooding events seem to have shifted in time and magnitude. Flash flood events, while being a well-known phenomenon in Mediterranean catchments, are increasingly also reported at higher latitudes. Unlike the large-scale winter flood events, flash floods are of very narrow spatial extension and triggered by rather short, but highly intense rainfall events.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we focus on the specific case of rivers in Luxembourg that have experienced several flash flood events in recent years, while only small to moderate winter flood events have been reported since the late 1990&amp;amp;#8217;s. National hydro-meteorological monitoring and flood forecasting systems have been designed for large-scale floods and are not suited for simulating local flash flood events. Therefore, there is a need to increase our understanding of the hydro-meteorological processes underlying flash flood occurrences in our area of interest.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;While increasing air temperature is known to allow a higher air moisture content that can lead to more intense rainfall events and possible flooding, we moreover hypothesize that the recent increase in flash flood occurrences in Luxembourg is reinforced by a change in atmospheric circulation patterns. To test this hypothesis, we analyse the prevailing atmospheric patterns on rainy days during summer and winter months over the period 1954 - 2019, with a particular focus on rainfall events that lead to moderate and extreme floods. In a next step, we intend to extend our findings for Luxembourg in a larger European context. This analysis should allow to better assess the current situation of hydrological extreme events in central Western Europe in order to take precaution measures and prepare for a diversifying hazard.&amp;lt;/p&amp;gt;