• Korean Journal of Microbiology
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• v.53 no.4
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• pp.257-264
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• 2017

Pseudomonas veronii MS1 and P. migulae MS2 have several mechanisms of copper resistance and plant growth promoting capability, and also can alleviate abiotic stress in plant by hydrolysis of a precursor of stress ethylene, 1-aminocyclopropane-1-carboxylic acid (ACC) by ACC deaminase. In 4-week pot test for tomato growth in soil contained 700 mg/kg Cu, inoculation of MS1 and MS2 significantly increased root and shoot lengths, wet weight and dry weight of tomato plants compared to those of uninoculated control. The inoculated tomato plants contained less amounts of proline that can protect plants from abiotic stress, and malondialdehyde, an oxidative stress marker than those of control. ACC synthase genes, ACS4 and ACS6, and ACC oxidase genes, ACO1 and ACO4, both involved in ethylene synthesis, were strongly expressed in Cu stressed tomato, whereas significantly reduced in tomato inoculated with MS1 and MS2. Also, a gene encoding a metal binding protein metallothionein, MT2 showed similar expression pattern with above genes. All these results indicated that these rhizobacteria could confer Cu resistance to tomato plant under Cu stress and allowed a lower level of Cu stress and growth promotion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1334-1343
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• 2008

Growing needs for efficient management of water resources urge the joint operation of dams and integrated management of whole basin. As one of the tools for supporting above tasks, this study aims to constitute a hydrologic model that can simulate the streamflow discharges at some control points located both upper and down stream of dams. One of the currently available models is being studied to be applied with a least effort in order to support the ongoing project of KWATER (Korea Water Resources Corporation), "Establishment of integrated operation scheme for the dams in Han River Basin". On this study, following works have been carried out : division of Han River Basin into 24 sub-basins, use of rainfall data of 151 stations to make spatial distribution of rainfall, selection of control points such as Soyanggang Dam, Chungju Dam, Chungju Release Control Dam, Heongseong Dam, Hwachun Dam, Chuncheon Dam, Uiam Dam, Cheongpyung Dam and Paldang Dam, selection of SSARR (Streamflow Synthesis and Reservoir Regulation) model as a hydrologic model, preparation of input data of SSARR model, sensitivity analysis of parameter using hydrologic data of 2002. The sensitivity analysis showed that soil moisture index versus runoff percent (SMI-ROP), baseflow infiltration index versus baseflow percent (BII-BFP) and surface-subsurface separation (S-SS) parameters are higher sensitive parameters to the simulation result.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.