scholarly journals Flood Hazard Zonation Using an Artificial Neural Network Model: A Case Study of Kabul River Basin, Pakistan

2021 ◽  
Vol 13 (24) ◽  
pp. 13953
Author(s):  
Muhammad Saeed ◽  
Huan Li ◽  
Sami Ullah ◽  
Atta-ur Rahman ◽  
Amjad Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
River Discharge ◽  
Flood Hazard ◽  
Soil Surface ◽  
Hazard Mapping ◽  
Drainage Network ◽  
Flood Depth ◽  
Slope Flow ◽  
Surface Geology ◽  
Kabul River

Floods are the most frequent and destructive natural disasters causing damages to human lives and their properties every year around the world. Pakistan in general and the Peshawar Vale, in particular, is vulnerable to recurrent floods due to its unique physiography. Peshawar Vale is drained by River Kabul and its major tributaries namely, River Swat, River Jindi, River Kalpani, River Budhni and River Bara. Kabul River has a length of approximately 700 km, out of which 560 km is in Afghanistan and the rest falls in Pakistan. Looking at the physiography and prevailing flood characteristics, the development of a flood hazard model is required to provide feedback to decision-makers for the sustainability of the livelihoods of the inhabitants. Peshawar Vale is a flood-prone area, where recurrent flood events have caused damages to standing crops, agricultural land, sources of livelihood earnings and infrastructure. The objective of this study was to determine the effectiveness of the ANN algorithm in the determination of flood inundated areas. The ANN algorithm was implemented in C# for the prediction of inundated areas using nine flood causative factors, that is, drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use. For the preparation of spatial geodatabases, thematic layers of the drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use were generated in the GIS environment. A Neural Network of nine, six and one neurons for the first, second and output layers, respectively, were designed and subsequently developed. The output and the resultant product of the Neural Network approach include flood hazard mapping and zonation of the study area. Parallel to this, the performance of the model was evaluated using Root Mean Square Error (RMSE) and Correlation coefficient (R2). This study has further highlighted the applicability and capability of the ANN in flood hazard mapping and zonation. The analysis revealed that the proposed model is an effective and viable approach for flood hazard analysis and zonation.

Use of Geo-Informatics in Flood Hazard Mapping: A Case of Balkhu River

2017 ◽  
Vol 13 ◽  
pp. 52-57
Author(s):  
Susheel Dangol
Keyword(s):  
Flood Hazard ◽  
Hazard Analysis ◽  
System Model ◽  
One Dimension ◽  
Hazard Mapping ◽  
Land Area ◽  
Technical Approach ◽  
Barren Land ◽  
Flood Depth ◽  
Analysis System

Flood is one of the striking water induced disaster that hits most of the part of the world. In Nepal also it is one of the serious disasters which affect the study describes the technical approach of probable flood hazard analysis. Segment of Balkhu River within the Balkhu catchment of area 44.37 km2 from Kirtipur gorge to Bagmati confluence was taken as area of study. The total length of the study segment was 5485.89 m. One dimension HEC-RAS (Hydrologic Engineering Center-River Analysis System) model was used for the analysis. The study shows that higher flood depth increases and low flood depth decreases with increase in intensity of flood. Also, huge area of barren land area is affected by flood and few percentage of settlement area is affected by flood indicating the damages to the human lives. Huge area of barren land indicates that in future human lives are more prone to disasters as those lands have gone through planning for future settlement.Nepalese Journal on Geoinformatics -13, 2014, Page: 52-57

scholarly journals Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2370 ◽  
Author(s):  
Rahmati ◽  
Darabi ◽  
Haghighi ◽  
Stefanidis ◽  
Kornejady ◽  
...  
Keyword(s):  
Neural Network ◽  
Flood Risk ◽  
Goodness Of Fit ◽  
Flood Hazard ◽  
Characteristic Curve ◽  
Urban Environments ◽  
Hazard Mapping ◽  
Self Organizing Map ◽  
Urban Flood ◽  
Self Organizing

Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management.

scholarly journals Flood Hazard Mapping and Vulnerability Analysis of Bishnumati River, Nepal

2017 ◽  
Vol 14 ◽  
pp. 20-24
Author(s):  
Susheel Dangol ◽  
Arnob Bormudoi
Keyword(s):  
Flood Hazard ◽  
Hazard Analysis ◽  
Flood Plain ◽  
Flood Damage ◽  
Hazard Mapping ◽  
Huge Amount ◽  
Technical Approach ◽  
Flood Vulnerability ◽  
Flood Depth ◽  
Flood Intensity

Flood is one of the striking water induced disaster that hits most of the part of the world. In Nepal also it is one of the serious disasters which affect the human lives and huge amount of property. The increase of population and squatter settlements of landless people living at the bank of the river has tremendous pressure in encroachment of flood plain making them vulnerable to the flood damage. The study describes the technical approach of probable flood vulnerability and flood hazard analysis. Bishnumati catchment was taken as area of study. One dimension model of HEC-RAS with HEC-GeoRAS interface in co-ordination with ArcGIS was applied for the analysis. Analysis shows that the flood area increases with flood intensity. Higher flood depth increases and lower flood depth decreases with increase in intensity of flood. Inundation of huge area of urban land indicates that in future human lives are more prone to flood disaster. Thus, the study may help in future planning and management for future probable disaster.Nepalese Journal on Geoinformatics, Vol. 14, 2015, page: 20-24

Flood hazard mapping using fractal dimension of drainage network in Hamadan City, Iran

2017 ◽  
Vol 12 (4) ◽  
pp. 86-92 ◽  
Author(s):  
Hossein Malekinezhad ◽  
Ali Talebi ◽  
Ali Reza Ilderomi ◽  
Seyed Zeynalabedin Hosseini ◽  
Mehdi Sepehri
Keyword(s):  
Fractal Dimension ◽  
Flood Hazard ◽  
Hazard Mapping ◽  
Drainage Network

scholarly journals Predicting coastal urban floods using artificial neural network: The case study of Macau, China

2021 ◽  
Vol 11 (10) ◽  
Author(s):  
Weijun Dai ◽  
Zhiming Cai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Hazard ◽  
Scale Model ◽  
Coefficient Of Determination ◽  
Urban Flood ◽  
Flood Depth ◽  
Flood Disasters ◽  
Artificial Neural

AbstractUsing data-driven models to predict floods in advance is one of the current effective methods and hot researches to reduce urban flood disasters. In order to improve the prediction accuracy, it is necessary to select the appropriate flood hazard factors and the number of training samples to construct the prediction model. In our current research, an artificial neural network (i.e., the back-propagation neural network, BPNN) model was developed to predict the flood depth in the next hour. A case study of the urban flood during six typhoons in Macau of China was conducted to prove the performance of the proposed model. The flood depth was collected as output; after analyzing their correlation to the flood typhoon optimum track, urban weather, tides, geographic height and water depth increment of the submerged area were used as input. As a result, four models trained with different sample numbers were developed for training and testing. The model performances were examined using average absolute error, root mean square error and the coefficient of determination. The results show that in this case study, the 30-min scale model provides reliable predictions and can provide useful decision support for the prevention and mitigation of flood disasters in coastal urban.

scholarly journals Flood Hazard Mapping in Residential Area Using Hydrodynamic Model HEC-RAS 5.0

2020 ◽  
Vol 7 (1) ◽  
pp. 25-36
Author(s):  
Muhammad Baitullah Al Amin ◽  
Reini Silvia Ilmiaty ◽  
Ayu Marlina
Keyword(s):  
Flow Velocity ◽  
Hydrodynamic Model ◽  
Flood Control ◽  
Flood Hazard ◽  
Hazard Mapping ◽  
Residential Areas ◽  
Low Level ◽  
Flood Depth ◽  
Two Dimensional Flow ◽  
Hazard Rating

The flood hazard rating is one of the essential variables in flood risk analysis. The identification of flood-prone areas urgently requires information about flood hazard zones. This research explains the method to develop flood hazard map by using hydrodynamic modeling in the residential areas. The hydrodynamic model used in this research is HEC-RAS 5.0, which can simulate the one- and two-dimensional flow regimes. The study area is Bukit Sejahtera and Tanjung Rawa residences located in Palembang City with a total area of about 200 ha, where the Lambidaro River was frequently overflowing caused flood inundation in the area. There are five indicators of flood hazard being analyzed, i.e., 1) flood depth, 2) flow velocity, 3) energy head, 4) flow force, which is the result of multiplication between flood depth and the square of flow velocity, and 5) intensity, which is the result of multiplication between flood depth and the flow velocity. The simulation results show that the flood hazard rating in the study area ranges from high to low level. The zones with a high flood hazard rating are dominated by the area around or near to the river, whereas the further zones have a moderate and low level of flood hazard rating. The flood depth indicator has a more significant influence than the flow velocity on the flood hazard level in the study area. This research is expected can contribute to the development of flood map and flood control methods in advance.

scholarly journals Measuring compound flood potential from river discharge and storm surge extremes at the global scale

2020 ◽  
Vol 20 (2) ◽  
pp. 489-504 ◽  
Author(s):  
Anaïs Couasnon ◽  
Dirk Eilander ◽  
Sanne Muis ◽  
Ted I. E. Veldkamp ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Storm Surge ◽  
River Discharge ◽  
Flood Hazard ◽  
Joint Probability ◽  
Global Scale ◽  
Statistical Dependence ◽  
Dependence Structure ◽  
Meteorological Forcing ◽  
Continental Scale ◽  
Bivariate Dependence

Abstract. The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

scholarly journals Mapping the Pollution Plume Using the Self-Potential Geophysical Method: Case of Oum Azza Landfill, Rabat, Morocco

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 961
Author(s):  
Meryem Touzani ◽  
Ismail Mohsine ◽  
Jamila Ouardi ◽  
Ilias Kacimi ◽  
Moad Morarech ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Water Table ◽  
Soil Surface ◽  
The Self ◽  
Drainage Network ◽  
Geophysical Method ◽  
Direct Measurements ◽  
Sandy Material ◽  
The City ◽  
Self Potential

The main landfill in the city of Rabat (Morocco) is based on sandy material containing the shallow Mio-Pliocene aquifer. The presence of a pollution plume is likely, but its extent is not known. Measurements of spontaneous potential (SP) from the soil surface were cross-referenced with direct measurements of the water table and leachates (pH, redox potential, electrical conductivity) according to the available accesses, as well as with an analysis of the landscape and the water table flows. With a few precautions during data acquisition on this resistive terrain, the results made it possible to separate the electrokinetic (~30%) and electrochemical (~70%) components responsible for the range of potentials observed (70 mV). The plume is detected in the hydrogeological downstream of the discharge, but is captured by the natural drainage network and does not extend further under the hills.

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