Search

Previous studies show that the fuzzy-based approach predicts incoming floods better than machine learning (ML). However, with numerous observation points, difficulties in manually determining fuzzy rules and membership values increase. This research proposes a novel fuzzy logic-based learning (FLBL) that embeds missing data imputations and a fuzzy rule optimization strategy to enhance ML performance while still benefiting from fuzzy theory. The simple moving average handles sensors' missing data. The logical mapping is used for fuzzification automation and fuzzy rule generation. The join function between the Szymkiewicz-Simpson coefficient similarity and max function is applied to optimize a fuzzy rules model. The case study uses observation data from three rivers traversing three districts in Semarang City. As a result, FLBL achieves 97.87% accuracy in predicting flood, outperforming the decision tree (96%) and the neural network (73.07%). This work is significant as a part of preventive flood-related disaster plans. [ABSTRACT FROM AUTHOR]

The occurrence of natural disasters, accelerated by climate change, has become a continuous menace to the environment and consequently impacts the socioeconomic well-being of people. Flood events are natural disasters resulting from excessive rainfall duration, intensity, and snow melt. Flood disaster management systems that are machine learning-based have been increasingly suggested and applied to forestall the impacts of floods on the environment in terms of monitoring and warning. This study aims to critically review various studies conducted on flood management systems to identify applicable data sources and machine learning models. The study applied Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to source data from an academic database using some selected keywords, which were identified for the review process after filtering a total number of forty-two pertinent research papers was used. The review identified different combinations of flood data, flood management techniques, flood models, application of machine learning in flood predictions, optimization techniques, data processing techniques, and evaluation techniques. The study concluded that a standard approach should be applied in building robust and efficient flood disaster management systems. Lastly, informed future research directions on using machine learning for flood prediction and susceptibility mapping are provided. [ABSTRACT FROM AUTHOR]

In order to deal with the risk of flood disaster caused by rainstorm, based on hydrological data monitored by Lijin Hydrological Station on the Yellow River, the LightGBM model was taken as the benchmark model, the CEEMDAN-LightGBM model optimized by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm was used to predict water levels, and compared its prediction performance with the Long Short-Term Memory (LSTM) model and LightGBM model. The hydrological data from two Yellow River hydrological stations (Lijin and Huayuankou) with different climatic conditions were input into the CEEMDAN-LightGBM model as original dataset to verify the adaptability and stability of the model. The results show that the CEEMDAN-LightGBM model exhibits superior performance in water level prediction. Comparing with the LSTM and LightGBM models, the model's EMA decrease by 46.08% and 9.95%, ERMS decrease by 33.01% and 43.01%, EMAP decrease by 94.99% and 3.82%, and R² increase by 30.48% and 7.58%, respectively. The CEEMDAN-LightGBM model can also predict the important hydrological feature of flow, providing stronger judgment basis for the model to predict flood occurrence. Comparing with the CEEMDAN-LightGBM model for predicting the water level and flow of Huayuankou hydrological station and Lijin hydrological station, except for the predicted difference of 23.64% in EMAP values between the two stations' water levels, the difference between EMAP values and ERMS values does not exceed 10%, and the difference in R² does not exceed 2%. [ABSTRACT FROM AUTHOR]

Efficient flood risk management hinges on the precise mapping and assessment of areas vulnerable to flooding. This research endeavors to advance the flood susceptibility mapping in Jeddah, Saudi Arabia by harnessing the long short-term memory (LSTM) algorithm enriched with two sophisticated metaheuristic optimizers: invasive weed optimization (IWO) and harmony search (HS). The process commenced with the utilization of synthetic aperture radar (SAR) imagery to construct a detailed flood inventory map. A comprehensive geodatabase encompassing various flood conditioning factors—encompassing lithology, land use, proximity to water bodies, hydrologic soil group (HSG), topographical features (such as slope, plan curvature, and aspect), and hydrological indices [including profile curvature, Topographic Wetness Index (TWI), flow accumulation, Topographic Position Index (TPI), altitude, Terrain Ruggedness Index (TRI), and Stream Power Index (SPI)] was meticulously curated. To develop the model, 70% of this dataset was employed, while the remaining 30% served to validate the predictive efficacy of the resultant flood susceptibility maps. These maps' accuracy was quantitatively gauged through the receiver operating characteristic curve and the area under the curve (AUC) statistics. Findings reveal that the integration of LSTM with IWO and HS metaheuristic algorithms significantly enhances accuracy (LSTM-IWO: AUC = 0.900; LSTM-HS: AUC = 0.876) in comparison to the standalone LSTM approach (AUC = 0.863). The implementation of these hybrid algorithms manifests as a potent and economically viable approach for detailed geospatial modeling of flood susceptibility, providing invaluable insights to bolster flood mitigation, preparedness, and emergency response strategies. [ABSTRACT FROM AUTHOR]

Assessing the growth of social, environmental, and economic domains are too difficult task. Thus, an accurate prediction could not be made via the conventional prediction method. Hence, a novel Goat-based Artificial Neural System (GbANS), in order to provide improved growth visualization of socioeconomic and environmental behavior prediction. Moreover, the study region selected for this validation is Muzaffarpur City and its environs. Using the trained rainfall and flood data, the initial phase's flood and rainfall rates were estimated. The growth rate of environmental and socioeconomic behaviors was projected based on the condition. Here, the goat's best solution is used to analyze the growth in the socioeconomic and environmental domains. In the present research, the novelty lies in the integration of goat optimization with the artificial neural network for improved forecasting of rainfall and floods. Also, GbANS goes beyond simply predicting rainfall and floods. It incorporates modules to assess the potential socioeconomic and environmental impacts of the predicted flood. The model holds the most promising solution for accurate rainfall and flood prediction. GbANS has the potential to be a significant advancement in rainfall and flood forecasting due to its potential for improved accuracy and enhanced decision-making support. The performance characteristics of the developed GbANS were then assessed and compared in terms of accuracy, Mean Square Error (MSE), and Root Mean Square Error (RMSE) with other models that were already in use. The suggested method has achieved an enhanced best result with an accuracy of 98.1%. [ABSTRACT FROM AUTHOR]

Time series prediction of natural calamities is effectively solved with deep neural networks due to their ability to automatically assimilate the temporal linkages in time series data. This research develops a hybrid stacked deep learning with one-dimensional Convolution–Stacked Bidirectional Gated Recurrent Unit (Conv1D-SBiGRU) algorithm, unifying the predictive advantages of one-dimensional Convolution (Conv1D) and Bidirectional Gated Recurrent Unit (BiGRU) using hydro-meteorological and atmospheric data to build and evaluate a flood prediction model in forecasting the phenomenon of forthcoming flood events. The one-dimensional Convolution model effectively obtains valuable information and learns the time series cognitive representation. The stacked BiGRU model efficiently identifies and models the data sequence with temporal dependencies due to their ability to learn from past and future moments. The developed predictive model uses statistically significant predicted rainfall value to estimate the daily Relative Departure Index (RDI) which is used to predict floods. The proposed work was trained and evaluated for predicting floods on the real-world data of Alappuzha district, Kerala, India. The findings demonstrate the preeminence of the Conv1D-SBiGRU-based flood model with around 33% reduced MAE and RMSE and 9% improved R2 over the benchmark and some hybrid techniques. The outcomes showed the efficiency of Conv1D-SBiGRU in precisely forecasting floods during extreme weather events with an accuracy of 98.6%. [ABSTRACT FROM AUTHOR]

Floods, among the most destructive climate-induced natural disasters, necessitate effective prediction models for early warning systems. The proposed Multi-Attention Encoder-Decoder-based Temporal Convolutional Network (MA-TCN-ED) prediction model combines the strengths of the Temporal Convolutional Network (TCN), Multi-Attention (MA) mechanism, and Encoder-Decoder (ED) architecture along with filter-wrapper feature selection for optimal feature selection. This framework aims to improve flood prediction accuracy by effectively capturing temporal dependencies and intricate patterns in atmospheric and hydro-meteorological data. The proposed framework was pervasively assessed for predicting the real-world flood-related data of the river Meenachil, Kerala, and the results showed that MA-TCN-ED using a filter-wrapper feature selection approach achieved higher accuracy in flood prediction. Further the model was validated on the dataset of river Pamba, Kerala. The proposed model exhibits better performance with about 32% reduced MAE, 39% reduced RMSE, 12% increased NSE, 14% enhanced R2, and 17% enhanced accuracy relative to the average performance of all the compared baseline models. The proposed work holds promise for enhancing early warning systems and mitigating the impact of floods and contributes to the broader understanding of leveraging deep learning models for effective climate-related risk mitigation. [ABSTRACT FROM AUTHOR]

Floods are a major global problem affect communities and businesses. For these effects to be mitigated and emergency measures to be improved, accurate prediction is essential. Conventional flood prediction models frequently fail because the models ignore important hydrological elements like water discharge and instead solely use rainfall data. This limitation was addressed by the combination of rainfall and water discharge data on internet of things (IoT)-based technologies. It focuses on analyzing historical records from flood-prone areas in Semarang using gated recurrent unit (GRU) models. The findings demonstrate how effectively the GRU model performs when rainfall and water discharge factors are taken into account, resulting in very accurate and dependable predictions of flood events. Precision, Recall, and F1-score are evaluation metrics that demonstrate the accuracy on which the model determines flood emergency statuses. This study advances flood prediction methods and highlights the value of integrating internet of things data to improve preparedness and resilience against flood disasters. [ABSTRACT FROM AUTHOR]

This study used Artificial Intelligence (AI) techniques as a modeling tool to estimate the risk of Nile flooding in the cities of southern Sudan. Climatic records, and precipitation, from stations along the area were used between 2010 and 2019. To test how well the models worked, the forecast was done using a variety of stations. To determine the flood rate in southern Sudan with the highest degree of accuracy, various artificial neural network techniques were investigated. Six artificial neural network (ANN) models were created and compared to show flood prediction to reach the maximum level of accuracy and to improve the results (NN, GRNN, RNN, CFNN, PNN, FFNN). The artificial neural network (FFNN) produced the best results in the first test, reaching a 95 % accuracy rate. Three further strategies were evaluated by increasing the neural network's hidden layer count to ten. Tests with 15 and 25 hidden layers also showed that the accuracy changes with the increase of hidden layers. Also, six other algorithms were applied to reach the highest value expected from Using one of the artificial intelligence techniques (AI), in predicting the flood by machine learning methods (ML). The highest expected value of flooding was reached through the (Gradient Boosting) model, where it was Classification Accuracy (CA) 0.937, followed by (AdaBoost), (CA 0.916).

The global community is continuously working to minimize the impact of disasters through various actions, including earth surveying. For example, flood-prone areas must be identified appropriately, predicted, understood, and socialized. In that case, it will increase the risk of disaster impacts on the affected population in the form of death, property damage, and socio-economic losses.The data mining approach has had a significant influence on research related to flood prediction in recent years, namely its impact on researchers related to forecast, classification, and clustering. Floods can also be predicted using a time series approach used to predict the future, a type of data-driven prediction that has been developed and widely applied and can be applied to predictions related to hydrology.A review to identify, evaluate, and interpret all relevant research results carried out so far for flood prediction and flood prediction with a data mining approach. The review method used in this study is PRISMA as a tool and guide for evaluating systematic reviews and meta-analyses.Some things discussed are types of data, types of floods and their parameters, types of approaches and combinations, and evaluation methods used in related studies. This study found that although the univariate time series approach dominates in related studies, multivariate time series Analysis (53 papers or 48.62%) can also be used to strengthen flood predictions in the long term or short term, t; this is an opportunity for further research. Some research opportunities to be carried out are combining the team series approach and the Estimation or Classification approaches. In contrast, the optimization approach is 11% of the total study. This is the next research opportunity. The type of flood chosen is also an opportunity for research to find a research gap; the less response on a kind of flood, the easier the study will be. This review found four types of floods: River Flood (76.1%), Urban Flood (11.9%), Coastal Flood (6.4%), and Flash Flood (5.5%). The dominant use of the evaluation method is RMSE, although this method is an absolute measure on the same scale as the target (depending on the data). Methods that produce percentages, such as MAPE, which are easier to understand by end users, need to be used more frequently in future studies. The amount of data also determines whether the resulting model is good, especially the choice of the time series approach, whether long-term or short-term. Whether short-term or long-term, forecasting is essential in disaster mitigation, which in this study is related to floods based on time series. Short-term forecasting can be used as an early warning system, while long-term forecasting can be used to support infrastructure planning by the government.

With the impact of global climate change and the urbanization process, the risk of urban flooding has increased rapidly, especially in developing countries. Real-time monitoring and prediction of flooding extent and drainage system are the foundation of effective urban flood emergency management. Therefore, this paper presents a rapid nowcasting prediction method of urban flooding based on data-driven and real-time monitoring. The proposed method firstly adopts a small number of monitoring points to deduce the urban global real-time water level based on a machine learning algorithm. Then, a data-driven method is developed to achieve dynamic urban flooding nowcasting prediction with real-time monitoring data and high-accuracy precipitation prediction. The results show that the average MAE and RMSE of the urban flooding and conduit system in the deduction method for water level are 0.101 and 0.144, 0.124 and 0.162, respectively, while the flooding depth deduction is more stable compared to the conduit system by probabilistic statistical analysis. Moreover, the urban flooding nowcasting method can accurately predict the flooding depth, and the R2 are as high as 0.973 and 0.962 of testing. The urban flooding nowcasting prediction method provides technical support for emergency flood risk management. HIGHLIGHTS A rapid nowcasting prediction method of urban flood based on data-driven and real-time monitoring.; The deduction model accurately estimates the global water depth.; The proposed urban flooding nowcasting model was observed to outperform the traditional machine learning model to predict.;

Large‐scale hydrologic modeling is important for understanding changes in water resources and flood hazard across a broad range of climatic and hydrologic conditions. Parsimonious models, although simple, allow for an efficient way to model river systems across multiple decades to even centuries. Therefore, this study aims to assess the ability of the distributed Hillslope Link Model (HLM) TETIS to simulate streamflow observations across the contiguous United States (CONUS) from 1981 to 2020. To obtain model parameters across this domain, we partition the study area into 234 HydroSHEDS level 5 basins and calibrate the model to a single representative location near the outlet of each basin using dynamical dimension search for 100 realizations. Performance is then assessed at 5046 US Geological Survey streamgages with respect to the Kling Gupta Efficiency (KGE) and bias. Our simulations result in a median KGE of 0.43, with 89% of the sites having a value above the reference of 1 − √2 (~ ‐0.41). Furthermore, there is a dependence of the model performance on climate regions, with the model performing better in basins in cold and temperate regions than in arid ones. While the parameters are estimated based on daily precipitation inputs, it is shown that the model performs well even when forced with hourly precipitation, highlighting the robustness of the selected parameters to different inputs. Finally, the soil related parameters show dependence on soil properties, providing a basis for future model improvement. Overall, this study highlights the model's flexibility in performing across a vast domain with different runoff generation mechanisms. [ABSTRACT FROM AUTHOR]

This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR's role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy. [ABSTRACT FROM AUTHOR]

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts. [ABSTRACT FROM AUTHOR]

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management. [ABSTRACT FROM AUTHOR]

This study used Artificial Intelligence (AI) techniques as a modeling tool to estimate the risk of Nile flooding in the cities of southern Sudan. Climatic records, and precipitation, from stations along the area were used between 2010 and 2019. To test how well the models worked, the forecast was done using a variety of stations. To determine the flood rate in southern Sudan with the highest degree of accuracy, various artificial neural network techniques were investigated. Six artificial neural network (ANN) models were created and compared to show flood prediction to reach the maximum level of accuracy and to improve the results (NN, GRNN, RNN, CFNN, PNN, FFNN). The artificial neural network (FFNN) produced the best results in the first test, reaching a 95 % accuracy rate. Three further strategies were evaluated by increasing the neural network's hidden layer count to ten. Tests with 15 and 25 hidden layers also showed that the accuracy changes with the increase of hidden layers. Also, six other algorithms were applied to reach the highest value expected from Using one of the artificial intelligence techniques (AI), in predicting the flood by machine learning methods (ML). The highest expected value of flooding was reached through the (Gradient Boosting) model, where it was Classification Accuracy (CA) 0.937, followed by (AdaBoost), (CA 0.916). [ABSTRACT FROM AUTHOR]

Flooding is a difficult and common hazard in Indonesia, particularly in Jakarta during the rainy season. Floods have been the subject of several endeavours, ranging from discovering the causes to reducing their impacts. Floods cause significant damage to infrastructure, the social economy, and human lives. The government continues to create reliable flood risk maps and plans for long-term flood risk management. According to data from Jakarta Flood Monitoring, 12 sub-districts and 26 urban villages were hit by floods each year between 2016 and 2020, with an average flood length of nearly 2 days. The flood tendency in Jakarta decreased from 2018 to 2019, but increased in 2020. Floods are produced by a variety of reasons, including weather, geography, and human actions such as deforestation. Strong flood prediction is required for disaster management, however this might be difficult owing to changing weather conditions. This study focuses on flood prediction in Jakarta based on weather parameters utilising machine learning techniques to provide accurate and realtime predictions. K-Nearest Neighbours (KNN) is an algorithm employed to forecast the areas that will encounter the consequences of floods. The outcomes of this research with the value of k=2 to k=9 obtained the best performance values at k=7, where the level of accuracy reaches 92.25%, 88.89% precision, 92.25% recall, and F1-measure of 89.52%. The integration of machine learning algorithms which encompasses multiple weather variables provides significant utility in comprehensive flood predictions and early warning systems in flood disaster mitigation. [ABSTRACT FROM AUTHOR]

Flood time series forecasting stands a critical challenge in precise predictive models and reliable error estimation methods. A novel approach utilizing a hybrid deep learning model for both point and interval flood prediction is presented, enhanced by improved kernel density estimation (KDE) for prediction comparison and error simulation. Firstly, an optimized gated recurrent unit-time convolutional network (GRU-TCN) is constructed by tuning the internal structure of the TCN, the activation function, the L2 regularization, and the optimizer. Then, Pearson Correlation is used for feature selection, and the hyperparameters of the improved GRU-TCN are optimized by the subtraction-average-based optimizer (SABO). To further assess the prediction uncertainty, interval predictions are provided via Non-parametric KDE, with an optimized bandwidth setting for accurate error distribution simulation. Experimental comparisons are made on 5-year hydro-meteorological daily data from two stations along the Yangtze River. The proposed model surpasses long short-term memory network (LSTM), TCN, GRU, TCN-LSTM, and GRU-TCN, with a reduction of more than 13% in root mean square error (RMSE) and approximately 15% in mean absolute error (MAE), resulting in better interval estimation and error control. The improved kernel density estimation curves for the errors are closer to the mean value of the confidence intervals, better reflecting the trend of the error distribution. This research enhances the accuracy and reliability of flood predictions and improves the capacity of humans to cope with climate and environmental changes. [ABSTRACT FROM AUTHOR]

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error. [ABSTRACT FROM AUTHOR]

The global community strives to minimize the impact of disasters through various actions, for example, mapping flood-prone areas. Flood-prone areas need to be identified correctly, predicted, understood, and socialized to minimize risks when a disaster occurs regarding death, property damage, and socio-economic losses. This type of data-based prediction has been developed and implemented widely and can be applied to predictions related to hydrology. Data mining approaches (estimation, classification, clustering, and time-series forecast) have significantly influenced research related to flood prediction in recent years. The time-series flood forecast has been widely used in previous research using various statistical and data-mining methods. Predicting floods that occur in coastal areas is less discussed than river floods. One method that is often used is exponential smoothing. Determining damping factor values (alpha, beta, and gamma) in the triple exponential smoothing method, in general, is to use all values from 0 to 1 to find the most optimal damping factor, this takes quite a long time and results generally appear with less accuracy. So, a combination of the triple exponential smoothing algorithm is proposed to perform tTimeseries forecast, and the gradient descent algorithm is used as an optimization algorithm to obtain optimal weight values for alpha, beta, and gamma in triple exponential smoothing. [ABSTRACT FROM AUTHOR]

The increasing prominence of natural disasters and the variability in climate change have underscored the significance of research in contemporary and prospective studies. This investigation zeroes in on floods, a ubiquitous natural calamity that plagues regions globally. Bihar stands out as a state exceptionally vulnerable to persistent and intense flooding, with a staggering 69.70 lakh hectares—equivalent to nearly 74 percent of its geographical expanse—enduring its detrimental consequences. The repercussions of such inundations are widespread, permeating multiple facets of life and erecting impediments to both the welfare and the developmental trajectory of local communities. Vital sectors, including infrastructure, agriculture, and livestock, bear the brunt of the damage, catalyzing the dislocation of innumerable households. In an agile response to these adversities, the state's administrative machinery has mobilized disaster response brigades, ensuring timely aid and succor to the distressed populace. To adeptly predict impending floods, this study harnesses data from 1991 to 2022, integrating pivotal metrics such as the 'Total Population affected (in lacs)', 'Mean Annual Rainfall', and 'Crop damage (in INR lakhs)'. By adopting an LSTM (Long Short-Term Memory) model, the research elucidates its findings via illustrative line plots, furnishing an accessible rendition of the flood trajectory predictions over the forthcoming five-year span. These predictive insights are instrumental for disaster management agencies, enhancing their strategic planning and readied responses to imminent flood events in Bihar. Capitalizing on these forecasts enables authorities to architect proactive interventions, ensuring reduced flood repercussions, bolstered community resilience, and an elevated state of disaster preparedness in the region. [ABSTRACT FROM AUTHOR]

Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high. HIGHLIGHTS In our study area, our models have demonstrated the capability to dramatically decrease the computation time required to generate high-fidelity results, reducing it from 7–8 h to 1 min.; The GAN model displays a lower sensitivity to changes in input resolution compared with the U-Net model.; The proposed method effectively recovers lost information because of the large grid size in the low-resolution geometry.;

ABSTRACTA strong correlation between the effect of climate change and the increase in flooding frequency and magnitude has been reported in Canada. Consequently, there is a crucial need to examine the effects of future climate change scenarios on flooding conditions. The main objective of this research is to better understand the destructive effects of flood events under historical and future climate change conditions for a small watershed (Eel River watershed) in New Brunswick (NB), Eastern Canada. A practical model had been developed using the modified Artificial Neural Network (ANN) in MATLAB by the authors of this study. The architecture and data structure of ANN is characterized by a back propagation with the Levenberg–Marquardt method. The observed daily total precipitation, daily maximum and minimum air temperatures, daily discharge for the period 1967 to 1983, the simulated monthly maximum and minimum air temperatures, and monthly total precipitation for the period of 1996–2099 from the CanESM2, the second-generation Canadian Earth System Model (CGCM), were used as input of the model. The Representative Concentration Pathways (RCP 4.5 and 8.5), as suitable climate change scenarios, were selected based on the Intergovernmental Panel on Climate Change (IPCC) recommendations for flood studies. Daily values of temperatures, precipitations, and discharges were converted to monthly mean values for better prediction of the output results. In addition, two series of observed discharges were prepared using mean monthly (Qavg) and daily maximum discharges (Qd) as the Target of the model. For more accurate analysis, the time frames of 1996–2012 (for the historical) and 2022–2038, 2039–2055, 2056–2072, 2073–2089, and 2083–2099 (for the future) were considered with a duration of 16 years for each time frame. The output results of ANN were predicted daily maximum (Qd) and mean (Qavg) discharges under the impact of climate change scenarios. As a part of the developed model, Flood Frequency Analysis (FFA) was undertaken using the generalized extreme value (GEV) and the three-parameter lognormal (LN3) distributions based on the predicted and observed discharges. The performance of FFA and ANN were demonstrated using the Anderson–Darling (AD), the Chi-square (CS) tests and coefficient of correlation (R) and mean squared error (MSE), respectively. In conclusion, the three most critical time frames with the highest values of predicted discharges were 2022–2038, 2056–2072, and 2073–2089 for RCP4.5 and 2039–2055, 2073–2089, and 2083–2099 for RCP8.5. Also, based on the FFA, the magnitudes of flood recurrence for the future time period of 100 years will dramatically increase according to the most critical time frames of 2056–2072 and 2039–2055 for RCP 4.5 and 8.5, respectively. Findings indicated that the Eel River watershed will encounter severe floods, and about a 50% increase in mean discharge, especially for the critical time frames. Finally, flood occurrences show increasing trends due to climate change effects in the most critical time frames.

A typhoon passing through or making landfall in a coastal city may result in seawater intrusion and continuous rainfall, which may cause urban flooding. The urban flood disaster caused by a typhoon is a dynamic process that changes over time, and a dynamic Gaussian Bayesian network (DGBN) is used to model the time series events in this paper. The scene data generated by each typhoon are different, which means that each typhoon has different characteristics. This paper establishes multiple DGBNs based on the historical data of Macau flooding caused by multiple typhoons, and similar analysis is made between the scene data related to the current flooding to be predicted and the scene data of historical flooding. The DGBN most similar to the scene characteristics of the current flooding is selected as the predicting network of the current flooding. According to the topography, the influence of the surface confluence is considered, and the Manning formula analysis method is proposed. The Manning formula is combined with the DGBN to obtain the final prediction model, DGBN-m, which takes into account the effects of time series and non-time-series factors. The flooding data provided by the Macau Meteorological Bureau are used to carry out experiments, and it is proved that the proposed model can predict the flooding depth well in a specific area of Macau under the condition of a small amount of data and that the best predicting accuracy can reach 84%. Finally, generalization analysis is performed to further confirm the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Globally, floods are the most damaging natural hazards, because of its severe nature and difficulty in forecasting that restrains emergency responses. Floods triggered by thunderstorms are frequent in the high mountainous area of Hindu Kush Himalaya especially in the north of Pakistan. This research delves into effective water management in the Panjkora River basin (a tributary of Swat River), combining statistical evaluations with socio-economic considerations for flood predictions. Amidst climate change uncertainties, a historical analysis of the 2010 and 2022 catastrophic events emphasises the urgency for robust predictive models. The study identifies the Artificial Neural Networks (ANN) model's proficiency in daily discharge prediction and the Support Vector Machine (SVM) model's commendable performance in monthly flood event forecasting. Focusing on daily runoff in the Panjkora River, the research leverages the R-squared (R²) metric, revealing the ANN model's superior accuracy (R² = 0.75) compared to the SVM model (R² = 0.60) in daily discharge prediction. Extending the analysis to monthly flood event prediction, the SVM model excels with an R² value of 0.60 despite limitations in capturing nuances. The significance of this research lies in the fact that a combination of multiple inputs, ANN and SVM techniques has been used to develop the flood models. This research revealed the potential of algorithm-based computational models in predicting floods and developed some useful techniques that can be used by the disaster management authorities in Pakistan. [ABSTRACT FROM AUTHOR]

This research devised a cutting-edge artificial intelligence methodology to enhance flood forecasting in Quebec, Canada, an area frequently affected by floods. The core of this project was creating a novel artificial intelligence (AI) model (i.e., Generalized Structure of Group Method of Data Handling) dedicated to the early detection of potential flood events. Utilizing data from two key hydrometric stations, Saint-Charles and Huron, located within the region, the study aggregated data from 15-minute intervals into comprehensive hourly averages. An initial analysis sought to understand the relationship between river flow rates and the environmental factors of temperature and precipitation upstream and downstream. The investigation uncovered intricate relationships among these factors, presenting challenges in accurately predicting floods. To address this, a specialized AI model was developed to translate the flow data from the Huron station to predict potential flooding at the Saint-Charles station. This model, leveraging 48-hour lag data from upstream, was designed to forecast flood events at the Saint-Charles station with lead times ranging from one to eighteen hours. The model demonstrated significant predictive accuracy, with a correlation coefficient surpassing 0.9. Consequently, this innovative AI model emerges as a promising tool for improving Quebec's flood prediction and early-warning systems. [ABSTRACT FROM AUTHOR]

Deep-learning neural network (DNN) models are currently gaining popularity in the field of hydrology as well as flow forecasting. This is because these models have proven that they are capable of producing short-period forecasts with high accuracy. However, studies on flow forecasting for hydrological stations located upstream of river basins are still uncommon as the limited number of input parameters can be gathered in such places. This study looked into the capacity of DNN models to predict discharge upstream of the Da River in Vietnam, where the topography is mostly mountainous. Streamflow data from the LaiChau hydrological station — the largest and most distant hydrological station upstream of the Da River — has been gathered and employed as input for three DNN models. These models are the LSTM (long short-term memory neural network), ANN (artificial neural network), and CNN (convolutional neural network). According to research findings, the performance of the LSTM and CNN models outperforms that of the ANN, which has an NSE coefficient of just approximately 0.91. LSTM has a slight advantage over CNN, although the difference is modest because their NSE coefficients are 0.97 and 0.96, respectively. This finding suggests that DNN models, particularly LSTM, can be a feasible alternative for upstream hydrological station discharge predictions. [ABSTRACT FROM AUTHOR]

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715. [ABSTRACT FROM AUTHOR]

Abstract Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.

Flood is India's most prevalent natural calamity, devastatingly affecting human lives, infrastructure, and agriculture. Predicting floods can help to mitigate the potential damage and conduct timely evacuation drives. This research proposes a deep-learning regression model to forecast flood runoff. Various climatological, hydrological, land, and vegetation-related data have been collected from multiple sources for 18 years (2002–2019) to create a comprehensive dataset for the Godavari River at the Perur water station in India. The relevant attributes identified through feature selection are river water level, precipitation, temperature, surface pressure, evaporation, soil water content, daily runoff, and average river flow. The selected features were fed into various time series prediction models like AutoRegressive Integrated Moving Average (ARIMA), Prophet, Neural Prophet, and Long Short-Term Memory (LSTM). The LSTM model obtained the best results achieving a Root Mean Squared Error (RMSE) value of 0.05, Mean Absolute Error (MAE) value of 0.007, Willmott's Index (WI) of 0.83, Legates-McCabe's Index (LMI) of 0.58, and R2 of 0.67 for a 1-day prediction with a look-back window of 183 days. The model is also trained to predict the flood runoff value for a week ahead. The proposed model can serve as an essential component in flood warning systems. HIGHLIGHTS Created a comprehensive dataset of various climatological, hydrological, land, and vegetation-related data.; Feature selection is used to identify top features related to the flood runoff.; Proposed a multivariate multi-step LSTM model for predicting the flood runoff for a day and a week ahead.; Compared the performance of the proposed LSTM model with other state-of-the-art models like ARIMA, Prophet, NeuralProphet.;

This research devised a cutting-edge artificial intelligence methodology to enhance flood forecasting in Quebec, Canada, an area frequently affected by floods. The core of this project was creating a novel artificial intelligence (AI) model (i.e., Generalized Structure of Group Method of Data Handling) dedicated to the early detection of potential flood events. Utilizing data from two key hydrometric stations, Saint-Charles and Huron, located within the region, the study aggregated data from 15-minute intervals into comprehensive hourly averages. An initial analysis sought to understand the relationship between river flow rates and the environmental factors of temperature and precipitation upstream and downstream. The investigation uncovered intricate relationships among these factors, presenting challenges in accurately predicting floods. To address this, a specialized AI model was developed to translate the flow data from the Huron station to predict potential flooding at the Saint-Charles station. This model, leveraging 48-hour lag data from upstream, was designed to forecast flood events at the Saint-Charles station with lead times ranging from one to eighteen hours. The model demonstrated significant predictive accuracy, with a correlation coefficient surpassing 0.9. Consequently, this innovative AI model emerges as a promising tool for improving Quebec's flood prediction and early-warning systems.

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts.

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management.

Event-based hydrological models are extensively adopted for the estimation of design floods and in operational flood forecasting frameworks. However, an accurate estimation of the initial hydrologic condition (IHC) is essential in enhancing the predictive capability of an event-based hydrological model. Hence, in this study, IHCs of an event-based conceptual model are estimated using two different methods: (1) assimilation of observed variables such as streamflow and soil moisture using an ensemble Kalman filter and (2) states obtained from the continuous model calibrated using four different calibration metrics. The observed flood events at the Jagdalpur catchment are simulated using a conceptual hydrologic model setup at two spatial resolutions (lumped and semi-distributed). The results of the study demonstrate that IHCs estimated by the continuous models perform better than those obtained through data assimilation. The performance of semi-distributed event-based models was found to be outperforming their lumped counterparts demonstrating the advantage of increased model resolution. The states obtained from the continuous models calibrated using Nash–Sutcliffe Efficiency (NSE) are performing well in initialising the event-based models. The median efficiency of the semi-distributed event-based model (based on states from the NSE calibrated continuous model) is 0.91 and 0.77 during calibration and validation periods, respectively. HIGHLIGHTS Methods to estimate the initial hydrologic condition (IHC) to initialise event-based models were evaluated.; Streamflow assimilation in both lumped and semi-distributed models led to improved simulations.; Soil moisture assimilation yielded slightly better predictions in the semi-distributed model.; Semi-distributed event-based model, initialised by IHCs extracted from the corresponding continuous model, is outperforming other models.;

Classical approaches for flood prediction apply numerical methods for the solution of partial differential equations that capture the physics of inundation processes (e.g., the 2D Shallow Water equations). However, traditional inundation models are still unable to satisfy the requirements of many relevant applications, including early-warning systems, high-resolution (or large spatial domain) simulations, and robust inference over distributions of inputs (e.g., rainfall events). Machine learning (ML) approaches are a promising alternative to physics-based models due to their ability to efficiently capture correlations between relevant inputs and outputs in a data-driven fashion. In particular, once trained, ML models can be tested/deployed much more efficiently than classical approaches. Yet, few ML-based solutions for spatio-temporal flood prediction have been developed, and their reliability/accuracy is poorly understood. In this paper, we propose FloodGNN-GRU, a spatio-temporal flood prediction model that combines a graph neural network (GNN) and a gated recurrent unit (GRU) architecture. Compared to existing approaches, FloodGNN-GRU (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. We evaluate FloodGNN-GRU using a LISFLOOD-FP simulation of Hurricane Harvey (2017) in Houston, Texas. Our results, based on several metrics, show that FloodGNN-GRU outperforms several data-driven alternatives in terms of accuracy. Moreover, our approach can be trained 100x faster and tested 1000x faster than the time required to run a comparable simulation. These findings illustrate the potential of ML-based methods to efficiently emulate physics-based inundation models, especially for short-term predictions.

In the past few decades, floods have become a severe problem, causing significant damage across the world, ranging from economic losses to loss of property and human lives. It is not possible to eliminate and avoid the floods, however, disastrous damages instigated by flood can be reduced. Floods are predictable by using advanced technologies like Machine Learning (ML) and Internet of Things (IoT) to notify the authorities and locals in advance. In this study, we built a working prototype using ML and IoT to collect hydro-meteorological data. The data is classified and analyzed using extreme gradient boosting (XGBOOST) and gated recurrent units (GRU) models to predict the flood situation. Both models classify the flood situation into Red (HFL), Orange (Danger), Yellow (Warning), or No alert. The prediction efficiency of the developed models is evaluated by using coefficient of determination, mean error, absolute mean error, relative error, and root mean square error. The task of estimating the water discharge gets difficult since rivers have changing geographic properties. To resolve this issue, a novel technique is proposed to estimate river discharge based on sectional area and flow of the river. [ABSTRACT FROM AUTHOR]

Floods are highly perilous and recurring natural disasters that cause extensive property damage and threaten human life. However, the paucity of hydrological observational data hampers the precision of physical flood models, particularly in ungauged basins. Recent advances in disaster monitoring have explored the potential of social media as a valuable source of information. This study investigates the spatiotemporal consistency of social media data during flooding events and evaluates its viability as a substitute for hydrological data in ungauged catchments. To assess the utility of social media as an input factor for flood prediction models, the study conducted time-series and spatial correlation analyses by employing spatial scan statistics and confusion matrices. Subsequently, a long short-term memory model was used to forecast the outflow volume in the Ui Stream basin in South Korea. A comparative analysis of various input factor combinations revealed that datasets incorporating rainfall, outflow models, and social media data exhibited the highest accuracy, with a Nash–Sutcliffe efficiency of 94%, correlation coefficient of 97%, and a minimal normalized root mean square error of 0.92%. This study demonstrated the potential of social media data as a viable alternative for data-scarce basins, highlighting its effectiveness in enhancing flood prediction accuracy. [ABSTRACT FROM AUTHOR]

Flooding is a hazardous natural calamity that causes significant damage to lives and infrastructure in the real world. Therefore, timely and accurate decision-making is essential for mitigating flood-related damages. The traditional flood prediction techniques often encounter challenges in accuracy, timeliness, complexity in handling dynamic flood patterns and leading to substandard flood management strategies. To address these challenges, there is a need for advanced machine learning models that can effectively analyze Internet of Things (IoT)-generated flood data and provide timely and accurate flood predictions. This paper proposes a novel approach-the Adaptive Momentum and Backpropagation (AM-BP) algorithm-for flood prediction and management in IoT networks. The AM-BP model combines the advantages of an adaptive momentum technique with the backpropagation algorithm to enhance flood prediction accuracy and efficiency. Real-world flood data is used for validation, demonstrating the superior performance of the AM-BP algorithm compared to traditional methods. In addition, multilayer high-end computing architecture (MLCA) is used to handle weather data such as rainfall, river water level, soil moisture, etc. The AM-BP’s real-time abilities enable proactive flood management, facilitating timely responses and effective disaster mitigation. Furthermore, the AM-BP algorithm can analyze large and complex datasets, integrating environmental and climatic factors for more accurate flood prediction. The evaluation result shows that the AM-BP algorithm outperforms traditional approaches with an accuracy rate of 96%, 96.4% F1-Measure, 97% Precision, and 95.9% Recall. The proposed AM-BP model presents a promising solution for flood prediction and management in IoT networks, contributing to more resilient and efficient flood control strategies, and ensuring the safety and well-being of communities at risk of flooding. [ABSTRACT FROM AUTHOR]

The increasing amount of rain produces a number of issues in Kerala, particularly in urban regions where the drainage system is frequently unable to handle a significant amount of water in such a short duration. Meanwhile, standard flood detection results are inaccurate for complex phenomena and cannot handle enormous quantities of data. In order to overcome those drawbacks and enhance the outcomes of conventional flood detection models, deep learning techniques are extensively used in flood control. Therefore, a novel deep hybrid model for flood prediction (DHMFP) with a combined Harris hawks shuffled shepherd optimization (CHHSSO)-based training algorithm is introduced for flood prediction. Initially, the input satellite image is preprocessed by the median filtering method. Then the preprocessed image is segmented using the cubic chaotic map weighted based k-means clustering algorithm. After that, based on the segmented image, features like difference vegetation index (DVI), normalized difference vegetation index (NDVI), modified transformed vegetation index (MTVI), green vegetation index (GVI), and soil adjusted vegetation index (SAVI) are extracted. The features are subjected to a hybrid model for predicting floods based on the extracted feature set. The hybrid model includes models like CNN (convolutional neural network) and deep ResNet classifiers. Also, to enhance the prediction performance, the CNN and deep ResNet models are fine-tuned by selecting the optimal weights by the combined Harris hawks shuffled shepherd optimization (CHHSSO) algorithm during the training process. This hybrid approach decreases the number of errors while improving the efficacy of deep neural networks with additional neural layers. From the result study, it clearly shows that the proposed work has obtained sensitivity (93.48%), specificity (98.29%), accuracy (94.98%), false negative rate (0.02%), and false positive rate (0.02%) on analysis. Furthermore, the proposed DHMFP–CHHSSO displays better performances in terms of sensitivity (0.932), specificity (0.977), accuracy (0.952), false negative rate (0.0858), and false positive rate (0.036), respectively. [ABSTRACT FROM AUTHOR]

Accurate models with low computational times are required to predict the consequences of fluvial floods in real‐time. Even though detailed hydraulic models can predict flood water levels and corresponding inundation extents with high accuracy, their computational times limit their applicability to being used as a flood early‐warning system. Therefore, conceptual models were developed in the literature for many years. These types of models do not attempt to represent the complex dynamic flood generation processes but are based on simplified hydraulic concepts, generally only using a digital elevation model as input. However, a shift in this research field is currently present from conceptual models to data‐driven models, and more specifically to neural networks. This paper discusses the benefits and drawbacks of both modelling approaches and speculates which method is most promising to be used as a flood warning system to predict the consequences of fluvial floods in real‐time. [ABSTRACT FROM AUTHOR]

In light of recent improvements in flood susceptibility mapping using machine learning models, there remains a lack of research focusing on employing ensemble algorithms like Light Gradient Boosting on Elastic-net Predictions (Light GBM) and Elastic-net Classifier (L2/Binomial Deviance) for mapping flood susceptibility in Qaa'Jahran, Yemen. This study aims to bridge this knowledge gap through the development and comparative performance of these models. This approach created the flood inventory map using satellite images and field observations. A geodatabase was used to create flood predictors such as aspect, altitude, distance to rivers, topographic wetness index (TWI), flow accumulation, lithology, distance to road, land use, profile curvature, plan curvature, slope, rainfall, soil type, Topographic Position Index (TPI), and Terrain Ruggedness Index (TRI). The developed models were trained using 80% of the data and evaluated using the remaining 20% to create a flood susceptibility map. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the map's accuracy. The results of this study indicated that the traditional (Elastic-net Classifier) model possessed high accuracy (AUC = 0.9457, F1 = 0.8916, Sensitivity = 0.9024, and Precision = 0.881) than the ensemble algorithm (Light Gradient Boosting on Elastic-net Predictions) (AUC = 0.9629, F1 = 0.9538, Sensitivity = 0.9688, and Precision = 0.9394). Based on the results of this study it can be concluded that these algorithms has a strong potential to offer a practical and affordable method for geospatial modeling of flood vulnerability. This information can be used to assess the flood emergency, early warning system and provide insights for planning and response purposes. [ABSTRACT FROM AUTHOR]

In recent years, Global Navigation Satellite System reflectometry (GNSS-R) has been explored as a methodology for inland water body characterization. However, thorough characterization of the sensitivity and behavior of the GNSS-R signal to inland water bodies is still needed to progress this area of research. In this paper, we characterize the uncertainty associated with Cyclone Global Navigation Satellite System (CYGNSS) measurements on the determination of river width. The characterization study uses simulated data from a forward model that accurately simulates CYGNSS observations of mixed water/land scenes. The accuracy of the forward model is demonstrated by comparisons to actual observations of known water body shapes made at particular measurement geometries. Simulated CYGNSS data are generated over a range of synthetic scenes modeling a straight river subreach, and the results are analyzed to determine a predictive relationship between the peak SNR measured over the river subreaches and the river widths. An uncertainty analysis conducted using this predictive relationship indicates that, for simplistic river scenes, the SNR over the river is predictive of the river width to within +/−5 m. The presence of clutter (surrounding water bodies) within ~500 m of a river causes perturbations in the SNR measured over the river, which can render the river width retrievals unreliable. The results of this study indicate that, for isolated, straight rivers, GNSS-R data are able to measure river widths as narrow as 160 m with ~3% error.