scholarly journals A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

2020 ◽  
Vol 12 (9) ◽  
pp. 1373 ◽  
Author(s):  
Viet-Nghia Nguyen ◽  
Peyman Yariyan ◽  
Mahdis Amiri ◽  
An Dang Tran ◽  
Tien Dat Pham ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Flash Flood ◽  
Spatial Prediction ◽  
Flash Floods ◽  
Mountainous Area ◽  
Random Subspace ◽  
Topographic Wetness Index ◽  
Modeling Approach ◽  
Inventory Map ◽  
Sar Imagery

Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

scholarly journals A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

2020 ◽  
Vol 12 (17) ◽  
pp. 2688 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Phuong-Thao Thi Ngo ◽  
Tien Dat Pham ◽  
Jie Dou ◽  
Xuan Song ◽  
...  
Keyword(s):  
Soil Type ◽  
Flash Flood ◽  
Predictive Performance ◽  
Slope Aspect ◽  
Support Vector ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Random Subspace ◽  
Topographic Wetness Index ◽  
Inventory Map

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

scholarly journals A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3704 ◽  
Author(s):  
Phuong-Thao Ngo ◽  
Nhat-Duc Hoang ◽  
Biswajeet Pradhan ◽  
Quang Nguyen ◽  
Xuan Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Flash Flood ◽  
Spatial Prediction ◽  
Flash Floods ◽  
Flood Inundation ◽  
Machine Learning Technique ◽  
Flood Model ◽  
Learning Technique ◽  
Sar Imagery

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.

scholarly journals A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

2021 ◽  
Vol 13 (9) ◽  
pp. 1818
Author(s):  
Lisha Ding ◽  
Lei Ma ◽  
Longguo Li ◽  
Chao Liu ◽  
Naiwen Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flash Flood ◽  
Flash Floods ◽  
Geographic Information ◽  
Flood Disaster ◽  
Future Research ◽  
Time Zone ◽  
Gis Technologies

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

scholarly journals Hydrological problems of flash floods and the encroachment of wastewater affecting the urban areas in Greater Cairo, Egypt, using remote sensing and GIS techniques

2020 ◽  
Vol 44 (1) ◽  
Author(s):  
Hanaa A. Megahed ◽  
Mohammed A. El Bastawesy
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Satellite Images ◽  
Flash Flood ◽  
Flow Direction ◽  
Flash Floods ◽  
Mitigation Measures ◽  
Water Volume ◽  
Gis Techniques ◽  
Greater Cairo

Abstract Background This paper discusses the hydrological problems assessment of flash floods and the encroachment of wastewater in selected urban areas of Greater Cairo using remote sensing and geographic information system (GIS) techniques. The integration of hydrogeological and geomorphological analyses with the fieldwork of drainage basins (Wadi Degla) hosting these urban areas endeavors to provide the optimum mitigation measures that can be feasibly taken to achieve sustainability of the urban areas and water resources available. Results Landsat 5 and Sentinel-2 satellite images were obtained shortly before and after flash flood events and were downloaded and analyzed to define the active channels, urban interference, storage areas, and the natural depressions response. The quantitative flash flood estimates include total GSMap meteorological data sets, parameters of rainfall depths from remote sensing data, active channel area from satellite images, and storage areas that flooded. In GIS, digital elevation model was used to estimate the hydrographic parameters: flow direction within the catchment, flow accumulation, time zone of the catchment, and estimating of the water volume in the largely inundated depressions. Conclusions Based on the results obtained from the study of available satellite images, it has been shown that there are two significant hydrological problems, including the lack of flash flood mitigation measures for urban areas, as the wastewater depressions and sanitary facilities are dotting in the downstream areas.

scholarly journals Flash Flood Susceptibility Mapping at Andungbiru Watershed, East Java Using AHP-Information Weighted Method

2021 ◽  
Vol 6 (2) ◽  
pp. 127
Author(s):  
Devi Ratna Handini ◽  
Entin Hidayah ◽  
Gusfan Halik
Keyword(s):  
Land Use ◽  
Vegetation Index ◽  
Flash Flood ◽  
Characteristic Curve ◽  
Flash Floods ◽  
Assessment Method ◽  
Economic Losses ◽  
Landsat 8 ◽  
Gis Technology ◽  
Topographic Wetness Index

Flash floods are among the most frequent natural disasters caused by heavy rain associated with a severe thunderstorm, which leads to social and economic losses in infrastructure and agriculture. Therefore, this research aims to map flash flood potential susceptibility (FFPS) in the Pekalen watershed, using Geographic Information System (GIS) technology and statistical analysis to reduce the risk of flooding. The opinion and experience of an expert on the weight assessment method were carried out using the Analytical Hierarchy Process (AHP). Furthermore, the probability statistical methods and GIS were used in flash flood areas in the Pekalen watershed in Andungbiru, Probolinggo village. This study was carried out using geomorphological factors, namely elevation, slope, stream power index, and topographic wetness index, with a resolution of 30 m. Thematic map scale of the land use, river density, distance to the river, rainfall, and geology is in the ratio of is in a ratio of 1:25.000. Imagery processing was carried out using Landsat 8 30 m x 30 m resolution imagery, such as the Normalized Difference Vegetation Index. The result showed that the model map of FFPS obtained low 8%, low 23%, moderate 27%, moderate to high 26%, high 13%, and very high 2% index values. The next stage of modeling analysis led to validation using statistic receiver operating Characteristic Curve (ROC) of area Under Curve (AUC) with a value of 90.15. In conclusion, the factors that significantly trigger flash floods are distance to the river, land use, and slope.   Keywords: AHP-weighted; information content; FFSP; GIS; Geomorphology Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember   This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals Flash Flood Hazard Mapping Using Satellite Images and GIS Tools: A case study of the central High Atlas (Morocco)

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Fatima El Bchari ◽  
Barbara Theilen-Willige ◽  
Abdellatif Souhel
Keyword(s):  
Remote Sensing ◽  
Flash Flood ◽  
Flash Floods ◽  
Hazard Mapping ◽  
High Atlas ◽  
Flooding Hazard ◽  
Gis Tools ◽  
Flooding Events ◽  
Central High ◽  
Central High Atlas

<p><strong>Abstract.</strong> Flash flood is generally defined as a rapid onset of flood with a short duration and a relatively high peak discharge. It occurs rapidly, generally within one hour of rainfall, and sometimes accompanied by landslides, mud flows, bridge collapse, damage to buildings, and fatalities (Hapuarachchi et al., 2011).</p><p> They cause extensive disruptions to a diverse range of living, working, societal, and spatial environments, the raison why they are reported to be one of the deadliest and most expensive natural hazards worldwide.</p><p> Flood damages do not only depend on precipitation amounts but are also a consequence of geomorphological factors and human influences (Maruša et al., 2014) and in this study, the main attention is particularly concentrated on geomorphological factors to flooding. Flash flood events can be characterized by the amount of rain responsible to their occurrences and their duration.</p><p> Leaving aside droughts, floods are one of the most dangerous meteorological hazards in Morocco, followed by wind-/sandstorms, with the largest frequency of occurrence and the largest number of victims (EM-Dat. 2014). Heavy rains often induce floods in Morocco, including flash floods, riverine floods and mud floods during the rainy season (Theilen-Willige, B. 2015). For example, in 2014, The violent storms of 22–30 November 2014, resulted in flash floods and rivers floods in large parts of Southern Morocco. The Guelmim area was the most affected part with at least 32 fatalities and important infrastructure damages. In August 2015 Heavy rain (299m) causes flooding in some area parts of the Geoparc of Mgoun (central High Atlas, Moroco): such as Tllouguit, Zawiat Ahancal,and Ait Bou Guemmez Indeed, this episode affected regional roads, bridges, schools and agricutural infrastractures, water supply and electrical networks and caused several people homeless, 3 deaths and important infrastructure damages.</p><p> The torrential rains caused flooding of Ahancal and Bou Gemmez river and submerging roads and bridges.</p><p> This flooding event rose the question of whether and how remote sensing and GIS tools could be used in an effective manner in order to contribute to a better understanding of the factors leading to this flooding hazard and how to mitigate damage in future by providing maps of areas that have been flooded in the past and areas that are susceptible to flooding due to their morphologic settings during extreme precipitation events.</p><p> The flooding hazard in the High atlas of Azilal region initiated this study in order to investigate the use of remote sensing and geographic information system (GIS) for the detection and identification of areas most likely to be flooded in the future again due to their morphologic properties during similar weather conditions. By combining morphometric analysis of the investigation area involving the quantitative analysis of the landforms based on Digital Elevation Model (DEM) data (Aster GDEM, SRTM-DEM, ALOS PALSAR-DEM) and visual interpretation based on satellite image. The resulting maps of weighted overlay procedures, aggregating causal, morphometric factors influencing the susceptibility to flooding (lowest height levels, flattest areas), allowed for the distinguishing of areas with higher, medium and lower susceptibility to flooding. Thus, GIS and remote sensing tools contribute to the recognition and mapping of areas and infrastructure prone to flooding in this area.</p><p> Satellite radar data as Sentinel 1, A and B, with acquisition times at or near the flooding events help to identify flooded areas due to the typical mirror-like radar signal reflection of water bodies. Optical satellite data (Sentinel 2, Landsat 8) (often useless during the flooding events because of the cloud cover) taken after flooding events can be used to monitor the traces of flash floods such as sediment accumulation areas or erosional features. Merging this information with infrastructural data in a GIS environment contributes to the detection of flooding hazard prone areas and, thus, to hazard preparedness.</p>

scholarly journals In Pursuit of Flash Flood Data

Eos ◽  
2017 ◽  
Vol 98 ◽  
Author(s):  
Jonathan Gourley
Keyword(s):  
Remote Sensing ◽  
Flash Flood ◽  
Flash Floods ◽  
Valuable Data

How remote sensing of streams provides valuable data for the characterization, prediction, and warning of impending flash floods.

scholarly journals Application of Backpack-Mounted Mobile Mapping System and Rainfall–Runoff–Inundation Model for Flash Flood Analysis

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 963 ◽  
Author(s):  
Takahiro Sayama ◽  
Koji Matsumoto ◽  
Yuji Kuwano ◽  
Kaoru Takara
Keyword(s):  
Remote Sensing ◽  
Flash Flood ◽  
Flash Floods ◽  
Large River ◽  
Flood Inundation ◽  
Rainfall Runoff ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Flood Analysis

Satellite remote sensing has been used effectively to estimate flood inundation extents in large river basins. In the case of flash floods in mountainous catchments, however, it is difficult to use remote sensing information. To compensate for this situation, detailed rainfall–runoff and flood inundation models have been utilized. Regardless of the recent technological advances in simulations, there has been a significant lack of data for validating such models, particularly with respect to local flood inundation depths. To estimate flood inundation depths, this study proposes using a backpack-mounted mobile mapping system (MMS) for post-flood surveys. Our case study in Northern Kyushu Island, which was affected by devastating flash floods in July 2017, suggests that the MMS can be used to estimate the inundation depth with an accuracy of 0.14 m. Furthermore, the landform change due to deposition of sediments could be estimated by the MMS survey. By taking into consideration the change of topography, the rainfall–runoff–inundation (RRI) model could reasonably reproduce the flood inundation compared with the MMS measurements. Overall, this study demonstrates the effective application of the MMS and RRI model for flash flood analysis in mountainous river catchments.

scholarly journals Modeling Flash Floods and Induced Recharge into Alluvial Aquifers Using Multi-Temporal Remote Sensing and Electrical Resistivity Imaging

2020 ◽  
Vol 12 (23) ◽  
pp. 10204
Author(s):  
Omnia El-Saadawy ◽  
Ahmed Gaber ◽  
Abdullah Othman ◽  
Abotalib Z. Abotalib ◽  
Mohammed El Bastawesy ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Electrical Resistivity ◽  
Flash Flood ◽  
Flood Hazard ◽  
Flash Floods ◽  
Water Infiltration ◽  
Mitigation Measures ◽  
Aquifer Recharge ◽  
Basin Scale ◽  
Alluvium Aquifer

Flash flood hazard assessments, mitigation measures, and water harvesting efforts in desert environments are often challenged by data scarcity on the basin scale. The present study, using the Wadi Atfeh catchment as a test site, integrates remote sensing datasets with field and geoelectrical measurements to assess flash flood hazards, suggest mitigation measures, and to examine the recharge to the alluvium aquifer. The estimated peak discharge of the 13 March 2020 flood event was 97 m3/h, which exceeded the capacity of the culverts beneath the Eastern Military Highway (64 m3/h), and a new dam was suggested, where 75% of the catchment could be controlled. The monitoring of water infiltration into the alluvium aquifer using time-lapse electrical resistivity measurements along a fixed profile showed a limited connection between the wetted surficial sediments and the water table. Throughflow is probably the main source of recharge to the aquifer rather than vertical infiltration at the basin outlet. The findings suggest further measures to avoid the negative impacts of flash floods at the Wadi Atfeh catchment and similar basins in the Eastern Desert of Egypt. Furthermore, future hydrological studies in desert environments should take into consideration the major role of the throughflow in alluvium aquifer recharge.

Non-sequential response in Mountainous Area of Southwest China

2021 ◽  
Author(s):  
Lin Liu ◽  
Qihua Ran
Keyword(s):  
Southwest China ◽  
Flash Flood ◽  
Lower Layer ◽  
Soil Layer ◽  
Monitoring Network ◽  
Rainfall Event ◽  
Time Interval ◽  
Mountainous Area ◽  
Topographic Wetness Index ◽  
Storage Change

&lt;p&gt;Non-sequential response, the phenomenon that the water storage change in the lower layer bigger than that of the adjacent upper layer within a set time interval, is often ignored because of lacking of distributed measured data at watershed scale, especially in mountainous area where extensive monitoring network is expensive and difficult to deploy. In this study, the subsurface non-sequential response in a mountainous watershed in Southwest China was investigated, combining field monitoring and numerical simulation. A physics-based numerical model (InHM) was employed to simulate the proportion and position of occurrence of the subsurface non-sequential response. The topographic wetness index (TWI = ln(a/tan b)) was adopted to distinguish the topographic zone corresponding to the non-sequential response occurrence at different depths. The results showed that the storage change in deep layer is not as fast as that in shallow and middle layers due to less disturbance, and the non-sequential response mainly came from the subsurface lateral flow which accumulated at the soil-bedrock interface. During a rainfall event, the shallow soil layer responded rapidly, the saturation increased, the non-sequential response moved from the hillslope zone in the middle layer to the channel zone in the shallow layer, and accumulated gradually at the soil-bedrock interface. In case of double-peak rainfall event, the occurrence proportion of non-sequential response increased, the depth expanded vertically, and the accumulated response shifted from the channel zone to the hillslope zone, which would affect the outlet runoff. Counter to the subconscious, non-sequential response could still happen even when precipitation stopped. The results improve our understanding of non-sequential response and provide a scientific basis for flash flood research in mountainous areas.&lt;/p&gt;

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