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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]

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]

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]

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]

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]

Flood has become the main hidden threat to the safety of the karst tunnel. Scientific and reasonable flood classification assessment and prevention measures have also become the main problems to be solved urgently in the construction and operation of tunnels. Aiming at the problems of multiple influencing factors, complex index systems, and linear processing of the connection degree of set pair analysis, a set pair cloud model based on cloud theory eigenvalues is proposed to optimize the connection degree of set pair analysis. Regarding the uncertainty of the evaluation system and the ambiguity of the evaluation grade boundary, firstly, the ridge-type membership function is used to determine the basic probability distribution of each measured index. Secondly, the distance discrimination weighting theory based on the index distance is also established, so that each measured index can assign the dynamic weight of the evaluation system. It can comprehensively determine the weight of the cloud connection degree for the system, and draw the cloud level diagram to determine the tunnel flood risk level. Finally, the proposed model is verified with tunnel flood. The results show that the evaluation results are consistent with other evaluation models and actual tunnel conditions. This model can provide guidance for flood prediction and prevention in karst tunnels. [ABSTRACT FROM AUTHOR]

Global climate change and sea level rise have led to increased losses from flooding. Accurate prediction of floods is essential to mitigating flood losses in coastal cities. Physically based models cannot satisfy the demand for real-time prediction for urban flooding due to their computational complexity. In this study, we proposed a hybrid modeling approach for rapid prediction of urban floods, coupling the physically based model with the light gradient boosting machine (LightGBM) model. A hydrological–hydraulic model was used to provide sufficient data for the LightGBM model based on the personal computer storm water management model (PCSWMM). The variables related to rainfall, tide level, and the location of flood points were used as the input for the LightGBM model. To improve the prediction accuracy, the hyperparameters of the LightGBM model are optimized by grid search algorithm and K-fold cross-validation. Taking Haidian Island, Hainan Province, China as a case study, the optimum values of the learning rate, number of estimators, and number of leaves of the LightGBM model are 0.11, 450, and 12, respectively. The Nash-Sutcliffe efficiency coefficient (NSE) of the LightGBM model on the test set is 0.9896, indicating that the LightGBM model has reliable predictions and outperforms random forest (RF), extreme gradient boosting (XGBoost), and k-nearest neighbor (KNN). From the LightGBM model, the variables related to tide level were analyzed as the dominant variables for predicting the inundation depth based on the Gini index in the study area. The proposed LightGBM model provides a scientific reference for flood control in coastal cities considering its superior performance and computational efficiency. [ABSTRACT FROM AUTHOR]

Forecasting systems for foreseeing water levels and flow rates have become necessary to mitigate climate change negative impacts. Most of these systems are based on powerful tools such as Artificial Intelligence (AI) methods. This paper presents a comprehensive review of AI methods for high-flow extremes prediction. The review starts with an overview of the state-of-the-art AI techniques and examples of their application, followed by a SWOT analysis to benchmark their predictive capability based on set of criteria. Finally, the most suitable AI methods for short-term and/or long-term prediction, based on a rigorous suitability assessment are proposed. As a result, Fourteen AI methods have been identified. Their evaluation revealed that the methods that averagely behave the best for achieving high-flow extremes prediction are ANNs, SVMs, wavelets and Bayesian methods, at all-time scales. The latter, as stochastic methods, have the privilege by their cheap computation cost, their reliability and ability to handle hydrological uncertainty, and their capacity to perform causal relationships between features. This study also urges researchers to further explore the predictive potential of decision trees, ensembles, CNNs, MARS, GP and agent-based methods for high-flow extremes. [ABSTRACT FROM AUTHOR]

Rainfall and run-off prediction capabilities for flash flood need improvement in the high porn regions of mountain hazard, those caused massive distraction. At some point, these events result in a massive loss of life and property. This research demonstrates the application of soil conservation service (SCS), a hydrological model used to simulate the relationship between rainfall and run-off. The data cover a 62-year observation period of torrential rains and debris flows, which number up to thirty flow events, all occurring in the mountainous regions close to the Tieli watershed area. Rainfall hydrographs were generated for each of the said torrential rain cases and can be used as markers for flood warnings for critical rainfalls lasting from 1 to 24 h. The results show that the SCS model can successfully define the rainfall–run-off-water level relationship of small-medium-size catchments wherein the calculated run-off values show good agreement with measured discharges. Moreover, from the relationship of the water level and SCS calculated run-off is similar to the relationship obtained using measured run-offs. From this relationship, critical water levels that would correspond to the occurrence of a possibly devastating flood can be determined to provide for an early warning measure for the nearby regions. [ABSTRACT FROM AUTHOR]

A flood monitoring and warning system provides critical information that can protect property and save lives. A basin-scale flood monitoring system requires an effective observation platform that offers extensive ground coverage of flood conditions, low latency, and high spatiotemporal resolution. While satellite imagery offers substantial spatial flood extent in detail due to its high spatial resolution, the coarse temporal resolution and cloud obstruction limit its near real-time application. Daily soil moisture data derived from satellite sensors at a scale of a few km can be used to monitor extreme wet surface conditions arising in flood occurrences. This study analyzes the flood detection capabilities of several sources of soil moisture information, including the Soil Moisture and Ocean Salinity mission (SMOS), the Advanced Microwave Scanning Radiometer on EOS, the Advanced SCATterometer on MetOp, the Global Land Data Assimilation System, and the WaterGAP Global Hydrology Model. In addition to soil moisture, the analysis includes measurements of surface reflectance from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation measurements from the Tropical Rainfall Measuring Mission, and terrestrial water storage estimates from the Gravity Recovery And Climate Experiment as proxies for flood inundations. The analysis was conducted over the Chao Phraya River Basin in Thailand, where the Great Flood of 2011 led to one of the most significant economic losses in the country's history. Satellite-derived soil moisture exhibits a stronger correlation with the flood inundations than the precipitation, model-derived soil moisture, and terrestrial water storage data. SMOS soil moisture observation agrees best with the MODIS-derived flood extent/occurrence, both in terms of spatial distribution and timing, and providing approximated flood lead-time of one week or longer. A neural network constructed from SMOS and MODIS data is used to predict flood intensity/occurrence (given soil moisture input) with a predicted time window from eight days to thirty-two days. The short-term prediction (e.g., eight days) achieves the highest accuracy with an averaged recovery rate of approximately 60% (correlation coefficient). This study's results suggest a potential application of satellite soil moisture data in assisting flood monitoring and warning systems. [ABSTRACT FROM AUTHOR]

The experience of constructing a method for short-range forecasting of water discharge in the Kama River basin is described. The forecast method is based on the HBV-96 conceptual model of runoff formation in a watershed with optimized parameters, as well as on the algorithm for the correction of operational forecasts. It is shown that if the runoff formation model parameters are optimized and the forecast correction algorithm is applied, the model is highly efficient at simulating variations in water discharge at gaging stations and can be used for operational short-range hydrological forecasting and for the evaluation of the hazard of expected hydrological conditions on the rivers. The implementation of the forecasting method allows obtaining water discharge forecasts for gaging stations in the Kama River basin with a lead time up to 3 days using meteorological forecasts with a corresponding lead time. [ABSTRACT FROM AUTHOR]

Algeria has experienced catastrophic floods over the second half of the twentieth century, causing many deaths and extensive material damage. This study was conducted to find a suitable event-based rainfall-runoff (RR) model for semi-arid conditions, where continuous data are not available in all regional basins. The study compared, based on data availability, the SCS-CN model based on the antecedent moisture conditions (AMC) and four modified SCS-CN models incorporating antecedent moisture amounts (AMA) in order to find the best model to reproduce the flood hydrographs in two catchments. The modified models were predominant over the SCS-CN method. Nonetheless, the Singh et al. (Water Resour Manag 29:4111–4127, 2015. https://doi.org/10.1007/s11269-015-1048-1) model (M4) and the Verma et al. (Environ Earth Sci 76:736, 2017a. https://doi.org/10.1007/s12665-017-7062-2) model (M5) were superior and demonstrated more stable structures. Coupled with the Hayami routing model, the models showed promising results and were able to reproduce the observed hydrographs' shape. However, it was impossible to choose the preferred model since they each excelled as to a criterion. Therefore, the corresponding outputs were combined using the simple average (SA) method and the weighted average (WA) method. We found that the WA method showed better results in the two catchments and allowed a more accurate prediction according to the performance criteria. [ABSTRACT FROM AUTHOR]

Flood is a natural calamity and causes damage of life and property devastation. The main objective of this study was to analyse flood hazard and inundation area mapping of Paravanar sub-Basin. Flood generating factors, like elevation, slope, drainage density, rainfall, soil type and land use were analyzed and delineated flood zones using Geospatial techniques and multi criteria evaluation. The flood generating factor was computed by data integration, floodplain depiction, mapping, and evaluation. Various parameters such as Normalized difference vegetation index (NDVI), Normalized difference build-up index (NDBI), Normalized difference water index (NDWI) were acquired for performing Regression Analysis. This information can be further utilized for hydro-meteorological figures and Surge forecast. These data can be used in building hazard and risk maps for pre-flood prediction. The flood hazard threats in the southeast and in between southeast and west part of the Paravanar River basin are high flood hazard threat zone. [ABSTRACT FROM AUTHOR]

Flood is one of the most destructive natural disaster that can affect hundreds of thousands people and infrastructure that can cost in the range billion of rupees. After so much research in this field, we still suffer from the disastrous effect of it due to lack of consideration of small scale flood affecting parameters affecting regional floods. In this research paper, an IoS based sensing network powered by mobile edge computing (MEC), fog computing and cloud computing is proposed for flood prediction and forecasting after analysis through modified multi-ANFIS architecture known as OFFM-ANFIS. The sensing layer includes various static and mobile sensors IoS nodes that pass the data to fog server via MEC. Both regional fog server and cloud is facilitated with the training, testing, analysis and decision making power that rates the chances of flood on an ordinal scale. The OFFM-ANFIS includes seven modified ANFIS models that make decision on flood forecasting on the basis of trained data and analysis of received senses data. The flow of raw and analyze data from OFFM-ANFIS is staged in a way that more influential parameters have strong impact in the final forecasting output. Evaluation of the proposed mechanism is done on the basis of data provided by Indian Meteorological Department and effectiveness is shown in the application of proposed mechanism in forecasting flood well before its occurrence. [ABSTRACT FROM AUTHOR]

A flood hazard rating prediction model was developed that is based on a long short-term memory (LSTM) neural network and random forest. The target area was Samseong District in Seoul, which has a history of severe flooding. The Storm Water Management Model was used to generate training data for the LSTM model to predict the total overflow as the rainfall input data. Two-dimensional numerical analysis was performed to calculate inundation and flow velocity maps for training the random forest, which was used to generate a map of the predicted flood hazard rating of grid units given the total accumulative overflow of the target area. To confirm the goodness of fit, the proposed model was used to predict a flood hazard rating map for a rainfall event observed on July 27, 2011. The prediction accuracy for the flood hazard rating of each grid was 99.86% when the debris factor was considered and 99.99% when the debris factor was not considered. [ABSTRACT FROM AUTHOR]

With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (SMOTE)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, SMOTE-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In SMOTE-based data enhancement, we adopt a SMOTE algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally, we construct a sparse Bayesian model based on AdaBoost training strategy, which could offer flood prediction results with high rationality and robustness. We demonstrate the accuracy and effectiveness of the proposed model for flood prediction by conducting experiments on a collected dataset with several comparative methods. [ABSTRACT FROM AUTHOR]

It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned. The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications. [ABSTRACT FROM AUTHOR]

Many tasks of operational watershed management at the local level require stream flow predictions delivered to decision makers in a timely manner. In highly urbanized watersheds with an impermeable surface, stormwater runoff can cause rapid increases in water levels in streams leading to flood and even flash flood events. Usually, such rapid increases in water flow characteristics are predicted by process-based models with high levels of uncertainty. In this study, the prediction of magnitudes of the stream hydrological characteristics is replaced by the forecasting of an event (i.e., flood or no-flood) using data collected by stream and rain gauges at the watershed. The proposed approach is based on a black box model developed as an ensemble of classifiers generated by independent inducers to predict the class of a future hydrological event in a small highly urbanized watershed. Eight inducers were investigated in the phase space reconstructed from observation data using time-delay embedding extended to multiple observation sites. Five inducers were selected for the ensemble, where the final decision is made by majority vote. The developed model generates 45-minute and hourly predictions of high-flow events with more than 80 % precision - a threshold used in operational flood management. Model site-specific parameterization is replaced by the training step using observation data on water levels and precipitation which are collected at 15-minute intervals and are readily available. The proposed approach to developing a prediction tool can be used by local authorities as one of the methods for flood management. [ABSTRACT FROM AUTHOR]

As part of ASME 4th joint US-European Fluid Engineering Division Summer Meeting, which took place in Chicago in August 2014, a symposium was held on Urban Fluid Mechanics, spanning from urban issues such as climate and heat island effect, to small city-scale effects, such as the flow around isolated buildings. The aim of the present note is to make a synthesis of the presentations, in order to highlight current trends of research and issues. [ABSTRACT FROM AUTHOR]

Many researchers seek to take advantage of the recently available and virtually uninterrupted supply of satellite-based rainfall information as an alternative and supplement to the ground-based observations in order to implement a cost-effective flood prediction in many under-gauged regions around the world. Recently, NASA Applied Science Program has partnered with USAID and African-RCMRD to implement an operational water-hazard warning system, SERVIR-Africa. The ultimate goal of the project is to build up disaster management capacity in East Africa by providing local governmental officials and international aid organizations a practical decision-support tool in order to better assess emerging flood impacts and to quantify spatial extent of flood risk, as well as to respond to such flood emergencies more expediently. The objective of this article is to evaluate the applicability of integrating NASA’s standard satellite precipitation product with a flood prediction model for disaster management in Nzoia, sub-basin of Lake Victoria, Africa. This research first evaluated the TMPA real-time rainfall data against gauged rainfall data from the year 2002 through 2006. Then, the gridded Xinanjiang Model was calibrated to Nzoia basin for period of 1985–2006. Benchmark streamflow simulations were produced with the calibrated hydrological model using the rain gauge and observed streamflow data. Afterward, continuous discharge predictions forced by TMPA 3B42RT real-time data from 2002 through 2006 were simulated, and acceptable results were obtained in comparison with the benchmark performance according to the designated statistic indices such as bias ratio (20%) and NSCE (0.67). Moreover, it is identified that the flood prediction results were improved with systematically bias-corrected TMPA rainfall data with less bias (3.6%) and higher NSCE (0.71). Although the results justify to suggest to us that TMPA real-time data can be acceptably used to drive hydrological models for flood prediction purpose in Nzoia basin, continuous progress in space-borne rainfall estimation technology toward higher accuracy and higher spatial resolution is highly appreciated. Finally, it is also highly recommended that to increase flood forecasting lead time, more reliable and more accurate short- or medium-range quantitative precipitation forecasts is a must. [ABSTRACT FROM AUTHOR]

The spatially distributed hydrologic model WetSpa working on a daily or hourly time scale combines elevation, soil and landuse data within GIS, to predict flood hydrographs and spatial distribution of hydrologic characteristics in a watershed. The model is applied to the Margecany-Hornad river basin (1,131 km2) located in Slovakia. Daily hydrometeorological data from 1991 to 2000, including precipitation data from nine stations, temperature data from four stations and evaporation data measured at one station are used as input to the model. Three base maps, i.e., DEM, landuse and soil types are prepared in GIS form, using 100×100 m cell size. Results of the simulations show good agreement between calculated and measured hydrographs at the outlet of the basin. The model predicts the daily/hourly hydrographs with good accuracy, between 75-80% according to the Nash-Sutcliff criteria. For assessing the impact of forests on floods, the calibrated model is applied for a reforestation scenario using the hourly data of the summer of 2001. The scenario considers a 50% increase of forest areas. The model results show that the reforestation scenario decreases the peak discharge by 12%. Investigation of peak discharges from the whole simulation period, shows that the scenario results are reduced by 18% on average. Also, the time to peak of the simulated hydrograph of the reforestation scenario is 14 h longer than for the present landuse. The results show that the effect of land cover on flood is strongly related to storm characteristics and antecedent soil moisture. [ABSTRACT FROM AUTHOR]

The major obstacles for modelling flood processes in karst areas are a lack of understanding and model representations of the distinctive features and processes associated with runoff generation and often a paucity of field data. In this study, a distributed flood-modelling approach, WetSpa, is modified and applied to simulate the hydrological features and processes in the karst Suoimuoi catchment in northwest Vietnam. With input of topography, land use and soil types in a GIS format, the model is calibrated based on 15 months of hourly meteorological and hydrological data, and is used to simulate both fast surface and conduit flows, and groundwater discharges from karst and non-karst aquifers. Considerable variability in the simulation accuracy is found among storm events and within the catchment. The simulation results show that the model is able to represent reasonably well the stormflows generated by rainfall events in the study catchment. [ABSTRACT FROM AUTHOR]

A new geomatics-based approach for flood prediction was developed and used to model the magnitude and spatial extent of a future Red River flood in southern Manitoba. This approach combines the statistical modelling capabilities of Markov (non-spatial) analysis and logistic regression (spatial) within a geographic information system (GIS) environment, utilizing modelling inputs derived from remotely sensed RADARSAT imagery and other digital geographic data. The 1997 Red River flood was the second largest in recorded history, and the largest for which accurate data are available. The results indicate: (i) a flood ‘one time interval-in terms of 3 days time unit measurement- larger in area’ than the 1997 flood is expected to affect 17.6% more land (an additional 47.6 km²) within the study area compared to 1997 levels based on Markovian probability derived from observations from the 1997 event; and (ii) the majority of this excess flooding will take place on agricultural land; no additional communities are expected to be at risk. Quantitative assessment verified the capability of this modelling approach for producing statistically significant results. The methodology used in this research would be easily transferable to other areas, and may provide the basis for a viable alternative to conventional hydrologic-based flood prediction approaches. [ABSTRACT FROM AUTHOR]

The spatial mapping of flood menace extents is crucial for the effective and competent enactment of risk-lessening strategy. We focused on geographical pattern and variation in flood-affected villages in Bongaon sadar sub-division, West Bengal, India, during the period between 1996 and 2016. To appraise the indigenous smoothing and dissimilarity of flood-affected/non-affected villages, GIS-based Voronoi statistics were used. Inverse distance weighting (IDW) is used to interpolate and predict the pattern of flood-affected/non-affected zones across the sub-division. Moran's I index statistics was considered to appraise spatial auto-correlation among the flood affected and non-affected villages. Getis-OrdGi*(d) statistics was employed to recognize the flood hotspot and cold spot areas within the study site. The higher magnitude of Moran's I was calculated as 1999–2001, 2004, 2011, 2013, 2015, and 2016. The high Z score was recorded in 1996–1999, 2001–2003, 2011, 2013, and 2014 indicated a spatial clustering of flood-affected villages. The predictive map derived through IDW showed that 7.76% (64.59 km2) area comes under very high threat zones of flood, followed by 16.27% as high risk, 24.49% as medium risk, 23.97% as low risk, and 27.51% as very low risk. This study determines the solicitation of GIS-based prophecy for the impost of revelation mapping, so as to define the latitudinal extent and frequency of areas where most affected villages are located and potential risk areas. [ABSTRACT FROM AUTHOR]

Two distributed rainfall-runoff models were developed separately by using linear and nonlinear storage approaches for simulating the heterogeneity of hydrological processes over the catchment. A 1 x 1 km grid-based mesh was used to abstract the geometry of the catchment. Thehydrological processes and geographic information were modeled on this scale. Both linear and nonlinear models were calibrated by global optimization technique. The models to simulate spatial hydrological processes over a catchment were verified using the observed hydrographs at two stream gauged stations within the catchment. The nonlinear model has better performance than the linear model for two verification storm events. [ABSTRACT FROM AUTHOR]