• 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.

• Clean Technology
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• v.28 no.4
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• pp.269-277
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• 2022

With the recent development of the biological enzymatic reaction industry, lactic acid (LA) can be mass-produced from biomass sources. In particular, a catalytic process that converts LA into acrylic acid (AA) is receiving much attention because AA is used widely in the petrochemical industry as a monomer for superabsorbent polymers (SAP) and as an adhesive for displays. In the LA conversion process, NaY zeolites have been previously shown to be a high-activity catalyst, which improves AA selectivity and long-term stability. However, NaY zeolites suffer from fast deactivation due to severe coking. Therefore, the aim of this study is to modify the acid-base properties of the NaY zeolite to address this shortcoming. First, base promoters, Ca ions, were introduced to the NaY zeolites to tune their acidity and basicity via ion exchange (IE) and incipient wetness impregnation (IWI). The IWI method showed superior catalyst selectivity and stability compared to the IE method, maintaining a high AA yield of approximately 40% during the 16 h reaction. Based on the NH₃- and CO₂-TPD results, the calcium salts that impregnated into the NaY zeolites were proposed to exit as an oxide form mainly at the exterior surface of NaY and act as additional base sites to promote the dehydration of LA to AA. The NaY zeolites were further treated with KOH before calcium impregnation to reduce the total acidity and improve the dispersion of calcium through the mesopores formed by KOH-induced desilication. However, this KOH treatment did not lead to enhanced AA selectivity. Finally, calcium loading was increased from 1wt% to 5wt% to maximize the amount of base sites. The increased basicity improved the AA selectivity substantially to 65% at 100% conversion while maintaining high activity during a 24 h reaction. Our results suggest that controlling the basicity of the catalyst is key to obtaining high AA selectivity and high catalyst stability.