Abdul ghani, ansari;Irfana, Memon;Fayyaz, Ahmed;Majid Hussain, Memon;Kelash, Kanwar;fareed, Jokhio
International Journal of Computer Science & Network Security
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v.22
no.12
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pp.185-196
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2022
The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.
Proceedings of the Korea Water Resources Association Conference
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2021.06a
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pp.140-140
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2021
Urban flood management is a crucial and challenging task, particularly in developed cities. Therefore, accurate prediction of urban flooding under heavy precipitation is critically important to address such a challenge. In recent years, machine learning techniques have received considerable attention for their strong learning ability and suitability for modeling complex and nonlinear hydrological processes. Moreover, a survey of the published literature finds that hybrid computational intelligent methods using nature-inspired algorithms have been increasingly employed to predict or simulate the streamflow with high reliability. The present study is aimed to propose a novel approach, an ensemble tree, Bayesian Additive Regression Trees (BART) model incorporating a nature-inspired algorithm to predict hourly multi-step ahead streamflow. For this reason, a hybrid intelligent model was developed, namely GA-BART, containing BART model integrating with Genetic algorithm (GA). The Jungrang urban basin located in Seoul, South Korea, was selected as a case study for the purpose. A database was established based on 39 heavy rainfall events during 2003 and 2020 that collected from the rain gauges and monitoring stations system in the basin. For the goal of this study, the different step ahead models will be developed based in the methods, including 1-hour, 2-hour, 3-hour, 4-hour, 5-hour, and 6-hour step ahead streamflow predictions. In addition, the comparison of the hybrid BART model with a baseline model such as super vector regression models is examined in this study. It is expected that the hybrid BART model has a robust performance and can be an optional choice in streamflow forecasting for urban basins.
Journal of the Korean Association of Geographic Information Studies
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v.13
no.1
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pp.101-113
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2010
Recently actual rainfall pattern is decreasing rainy days and increasing in rainfall intensity and the frequency of flood occurrence is also increased. To consider recent situation, Engineers use deterministic methods like a PMP(Probable Maximum Precipitation). If design storm wouldn't occur, increasing of design criteria is extravagant. In addition, the biggest structure cause trouble with residents and environmental problem. And then it is necessary to study considering probability of rainfall parameter in each sub-basin for design of water structure. In this study, stochastic rainfall patterns are generated by using log-ratio method, Johnson system and multivariate Monte Carlo simulation. Using the stochastic rainfall patterns, hydrological analysis, hydraulic analysis and 2nd flooding analysis were performed based on GIS for their applicability. The results of simulations are similar to the actual damage area so the methodology of this study should be used about making a flood risk map or regidental shunting rout map against the region.
The deposition of solids in combined sewer systems results in a loss of flow capacity that may restrict flow and cause a local flooding and enhanced solids deposition. In order to solve these problems and proper pipe management, estimation of solid loads from sewer and surface in a drainage basin is needed but this task is very difficult and extremely expensive. In this study, generalized procedures for estimating sewer solid loads during dry weather in combined sewer systems and for estimating solid loads on surface in a drainage basin developed by the U. S. Environmental Protection Agency were applied and analyzed in Gunja drainage basin in Korea. As result, the estimated solid loads from sewer and surface are 205.8,759kg/yr and 1,321,993kg/yr respectively, and total solid loads is 1,527,752kg/yr. The estimated solid removal from street cleaning, dredging from pipe system and pumping house is 1,486,636kg/yr. Therefore, the applied methods show resonable results. More reliable estimation can be achieved if long-term measurements and adjustment of estimation equations are carried out, and this estimation methods can be used usefully for the management of combined sewer system with reduction of cost and effort.
KSCE Journal of Civil and Environmental Engineering Research
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v.33
no.2
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pp.521-528
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2013
Urban drainage systems are generally designed as open channel flow. The system, however, shows a partially surcharged flow in its body, especially at junctions or manholes. Thus, a special case of this condition needs to be explained as pressurized flow condition for designing the sewer system. This study considered the surcharged manhole flows during an unexpected rainfall event or an excess of design frequency. Overflows from surcharged manholes and urban flooding can occur from the effect of surcharged flows. Thus, sewer systems should be designed with the concept of open channel flow and pressurized flow. Also, energy losses in a manhole need to be considered. The aim of this study is to develop the numerical model which can evaluate the effect of the energy losses at the manhole. The numerical model was verified and compared with hydraulic model and SWMM. The results showed that the water depth of numerical model was in good agreement with hydraulic model at the each manhole. However, the SWMM underestimated the water depth because that model ignored the energy losses at manholes. Thus, the developed numerical model in this study could be a useful tool for the assessment of a conveyance of urban drainage system.
Recently, global climate change causes abnormal weather and disaster countermeasures do not provide sufficient defense and mitigation because they were established according to the historical climate condition. Repeated torrential rains, in particular, are causing damage even in the robust urban flood defense system. Therefore, in this study, the change of runoff considering the spatial distribution of rainfall and urban characteristics was analyzed. For rainfall concentrated in small catchment, rainfall in the watershed must be accurately measured. This study is based on the rainfall data observed with Automated Surface Observing System (ASOS) and Automatic Weather Stations (AWS) provided by the Seoul Meteorological Administration. Effluent from the pumping station was estimated using the EPA-SWMM model and compared and analyzed. Catchments with rainwater pumping station are small with large portion of impermeable areas. Thus, when the ASOS data where is located from from the chatchment, runoff is often calculated using rainfall data that is different from rainfall in the catchment. In this study, the difference between rainfall data observed in the AWS near the catchment and ASOS away from the catchment was calculated. It was found that accurate rainfall should be used to operate rainwater pumping stations or forecast urban flooding floods. In addition, the results of this study may be helpful for estimating design rainfall and runoff calculation.
Journal of the Korean Institute of Traditional Landscape Architecture
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v.35
no.4
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pp.126-133
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2017
This study analyzed hydrological changes of stormwater runoff of Seolleung Jeongneung zone according to the application of LID system based on landscape Architectural technology. The results are as follows. First, when flooding occurred in Gwanghwamun in July 27, 2011, the maximum instantaneous rainfall amount was 183 mm/hr recorded at 10:00 on 27th for 10 minutes, and it was confirmed that rainfall intensity more than three times as high as the maximum rainfall of 57.5 mm/hr. Second, it is possible to control peak flow rate in the case of 1,500mm of soil thickness, so that it is possible to improve the vulnerability of flood damage in Seolleung and Jeongneung zone when applying the LID system. Third, in the berm height scenario, peak flow rate control was not controled in all depth level models, but the first stormwater runoff was delayed by 4 hours and 10 minutes compared to the soil thickness scenario. It was interpreted as a relatively important indicator the soil thickness for the initial stromwater runoff reduction and the berm height for the peak runoff. Through this, the systematic adaptation of landscape-friendly ecological factors within the cultural property protection zone could theoretically confirm the effects of flood disaster prevention.
We present the sedimentary sequence and distribution pattern of the late Holocene muddy deposits in the northern East China Sea shelf using the high-resolution 'Chirp' profiles. The seismic sedimentary sequence overlying acoustic basement (basal reflector-B) can be divided into two depositional units (Unit 1 and 2) bounded by erosional bounding surface (mid reflector-M). The lower Unit 1 above basal reflector-H is characterized by the acoustically parallel to subparallel reflections and channel-fill facies. The upper Unit 2, up to 7 m in thickness, shows seismically semi-transparent seismic facies and lenticular body form. On the base of sequence stratigraphic concept, these two sediment units have developed during transgression and highstand period, respectively, since the last sea-level lowstand. The transgressive systems tract (Unit 1) lie directly on the sequence boundary (reflector B) that have farmed during the last glacial maximum. The transgressive systems tract in this study consists mostly of complex of delta, fluvial, and tidal deposits within the incised valley estuary system. The maximum flooding surface (reflector M) corresponding to the top surface of transgressive systems tract is obviously characterized by erosional depression. The highstand systems tract (Unit 2) above maximum flooding surface is made up of the mud patch filled with the erosional depression. The high-stand mud deposits showing a circle shape just like a typhoon symbol locates about 140 km off the south of Cheju Island with water depth of $60\~90m$. Coverage area and total sediment volume of the mud deposits are about $3,200km^2$ and $10.7\times10^9\;m^3$, respectively. The origin of the mud patch is interpreted as a result of accumulating suspended sediments derived from the paleo-Yellow and/or Yangtze Rivers. The circular distribution pattern of the mud patch appears to be largely controlled by the presence of cyclonic eddy in the northern East China Sea.
The geological CO2 sequestration in underground geological formation such as deep saline aquifers and depleted hydrocarbon reservoirs is one of the most promising options for reducing the atmospheric CO2 emissions. The process in geological CO2 sequestration involves injection of supercritical CO2 (scCO2) into porous media saturated with pore water and initiates CO2 flooding with immiscible displacement. The CO2 migration and distribution, and, consequently, the displacement efficiency is governed by the interaction of fluids. Especially, the viscous force and capillary force are controlled by geological formation conditions and injection conditions. This study aimed to estimate the effects of surfactant on interfacial tension between the immiscible fluids, scCO2 and porewater, under high pressure and high temperature conditions by using a pair of proxy fluids under standard conditions through pendant drop method. It also aimed to observe migration and distribution patterns of the immiscible fluids and estimate the effects of surfactant concentrations on the displacement efficiency of scCO2. Micromodel experiments were conducted by applying n-hexane and deionized water as proxy fluids for scCO2 and porewater. In order to quantitatively analyze the immiscible displacement phenomena by n-hexane injection in pore network, the images of migration and distribution pattern of the two fluids are acquired through a imaging system. The experimental results revealed that the addition of surfactants sharply reduces the interfacial tension between hexane and deionized water at low concentrations and approaches a constant value as the concentration increases. Also it was found that, by directly affecting the flow path of the flooding fluid at the pore scale in the porous medium, the surfactant showed the identical effect on the displacement efficiency of n-hexane at equilibrium state. The experimental observation results could provide important fundamental information on immiscible displacement of fluids in porous media and suggest the potential to improve the displacement efficiency of scCO2 by using surfactants.
Due to recent global climate change, the scale of flood damage is increasing as rainfall is concentrated and its intensity increases. Rain on a scale that has not been observed in the past may fall, and long-term rainy seasons that have not been recorded may occur. These damages are also concentrated in ASEAN countries, and many people in ASEAN countries are affected, along with frequent occurrences of flooding due to typhoons and torrential rains. In particular, the Bandung region which is located in the Upper Chitarum River basin in Indonesia has topographical characteristics in the form of a basin, making it very vulnerable to flooding. Accordingly, through the Official Development Assistance (ODA), a flood forecasting and warning system was established for the Upper Citarium River basin in 2017 and is currently in operation. Nevertheless, the Upper Citarium River basin is still exposed to the risk of human and property damage in the event of a flood, so efforts to reduce damage through fast and accurate flood forecasting are continuously needed. Therefore, in this study an artificial intelligence-based river flood water level forecasting model for Dayeu Kolot as a target station was developed by using 10-minute hydrological data from 4 rainfall stations and 1 water level station. Using 10-minute hydrological observation data from 6 stations from January 2017 to January 2021, learning, verification, and testing were performed for lead time such as 0.5, 1, 2, 3, 4, 5 and 6 hour and LSTM was applied as an artificial intelligence algorithm. As a result of the study, good results were shown in model fit and error for all lead times, and as a result of reviewing the prediction accuracy according to the learning dataset conditions, it is expected to be used to build an efficient artificial intelligence-based model as it secures prediction accuracy similar to that of using all observation stations even when there are few reference stations.
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