• The Journal of Engineering Geology
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• v.33 no.3
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• pp.489-502
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• 2023

Urban floods pose significant challenges to cities worldwide, driven by the interplay between urbanization and climate change. This review examines recent studies of urban floods to understand their causes, impacts, and potential mitigation strategies. Urbanization, with its increase in impermeable surfaces and altered drainage patterns, disrupts natural water flow, exacerbating surface runoff during intense rainfall events. The impacts of urban floods are far-reaching, affecting lives, infrastructure, the economy, and the environment. Loss of life, property damage, disruptions to critical services, and environmental consequences underscore the urgency of effective urban flood management. To mitigate urban floods, integrated flood management strategies are crucial. Sustainable urban planning, green infrastructure, and improved drainage systems play pivotal roles in reducing flood vulnerabilities. Early warning systems, emergency response planning, and community engagement are essential components of flood preparedness and resilience. Looking to the future, climate change projections indicate increased flood risks, necessitating resilience and adaptation measures. Advances in research, data collection, and modeling techniques will enable more accurate flood predictions, thus guiding decision-making. In conclusion, urban flooding demands urgent attention and comprehensive strategies to protect lives, infrastructure, and the economy.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.83-90
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• 2009

Since wireless sensor networks are deployed in open environments, an attacker can physically capture some sensor nodes. Using information of compromised nodes, an attacker can launch false data injection attacks that report nonexistent events. False data can cause false alarms and draining the limited energy resources of the forwarding nodes. In order to detect and discard such false data during the forwarding process, various security solutions have been proposed. But since they are prevention-based solutions that involve additional operations, they would be energy-inefficient if the corresponding attacks are not launched. In this paper, we propose a detection method that can detect false data injection attacks without extra overheads. The proposed method is designed based on the signature of false data injection attacks that has been derived through simulation. The proposed method detects the attacks based on the number of reporting nodes, the correctness of the reports, and the variation in the number of the nodes for each event. We show the proposed method can detect a large portion of attacks through simulation.

• Journal of Korea Water Resources Association
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• v.47 no.9
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• pp.767-776
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• 2014

Changes in climate have largely increased concentrated heavy rainfall, which in turn is causing enormous damages to humans and properties. Floods are one of the most deadly and damaging natural disasters known to mankind. The flood forecasting and warning system concentrates on reducing injuries, deaths, and property damage caused by floods. Therefore, the exact relationship and the spatial variability analysis of hydrometeorological elements and characteristic factors is critical elements to reduce the uncertainty in rainfall-runoff model. In this study, grid resolution depending on the topographic factor in rainfall-runoff models presents how to respond. semi-distribution of rainfall-runoff model using the model GRM simulated and calibrated rainfall-runoff in the Gamcheon and Naeseongcheon watershed. To run the GRM model, input grid data used rainfall (two event), DEM, landuse and soil. This study selected cell size of 500 m(basic), 1 km, 2 km, 5 km, 10 km and 12 km. According to the resolution of each grid, in order to compare simulation results, the runoff hydrograph has been made and the runoff has also been simulated. As a result, runoff volume and peak discharge which simulated cell size of DEM 500 m~12 km were continuously reduced. that results showed decrease tendency. However, input grid data except for DEM have not contributed increase or decrease runoff tendency. These results showed that the more increased cell size of DEM make the more decreased slope value because of the increased horizontal distance.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.17-27
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• 2021

Streamflow prediction is a very vital disaster mitigation approach for effective flood management and water resources planning. Lately, torrential rainfall caused by climate change has been reported to have increased globally, thereby causing enormous infrastructural loss, properties and lives. This study evaluates the contribution of rainfall to streamflow prediction in normal and peak rainfall scenarios, typical of the recent flood at Piney Resort in Vernon, Hickman County, Tennessee, United States. Daily streamflow, water level, and rainfall data for 20 years (2000-2019) from two USGS gage stations (03602500 upstream and 03599500 downstream) of the Piney River watershed were obtained, preprocesssed and fitted with Long short term memory (LSTM) model. Tensorflow and Keras machine learning frameworks were used with Python to predict streamflow values with a sequence size of 14 days, to determine whether the model could have predicted the flooding event in August 21, 2021. Model skill analysis showed that LSTM model with full data (water level, streamflow and rainfall) performed better than the Naive Model except some rainfall models, indicating that only rainfall is insufficient for streamflow prediction. The final LSTM model recorded optimal NSE and RMSE values of 0.68 and 13.84 m³/s and predicted peak flow with the lowest prediction error of 11.6%, indicating that the final model could have predicted the flood on August 24, 2021 given a peak rainfall scenario. Adequate knowledge of rainfall patterns will guide hydrologists and disaster prevention managers in designing efficient early warning systems and policies aimed at mitigating flood risks.

• Journal of Practical Agriculture & Fisheries Research
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• v.18 no.1
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• pp.101-112
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• 2016

Monitoring and control of the greenhouse environment play a decisive role in greenhouse crop production processes. The network system for greenhouse control was developed by using recent technologies of networking and wireless communications. In this paper, a remote monitoring and control system for greenhouse using a smartphone and a computer with internet has been developed. The system provides real-time remote greenhouse integrated management service which collects greenhouse environment information and controls greenhouse facilities based on sensors and equipments network. Graphical user interface for an integrated management system was designed with bases on the HMI and the experimental results showed that a sensor data and device status were collected by integrated management in real-time. Because the sensor data and device status can be displayed on a web page, transmitted using the server program to remote computer and mobile smartphone at the same time. The monitored-data can be downloaded, analyzed and saved from server program in real-time via mobile phone or internet at a remote place. Performance test results of the greenhouse control system has confirmed that all work successfully in accordance with the operating conditions. And data collections and display conditions, event actions, crops and equipments monitoring showed reliable results.