• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.817-819
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• 2003

Land degradation is a crucial issue in mountainous areas and is manifested in a variety of processes. For its assessment, application of existing models is not straightforward. In addition, data availability might be a problem. In this paper, a procedure for land degradation assessment is described, which follows a four-step approach: (1) detection, inventory and mapping of land degradation features, (2) assessing the magnitude of soil loss, (3) study of causal factors, and (4) hazard assessment by applying decision trees. This approach is applied to a case study in the Middle Mountain region of Nepal. The study shows that individual mass movement features such as debris slides and slumps can be easily mapped by photo interpretation techniques. Application of soil loss estimation models helps get insight on the magnitude of soil losses. In the study area soil losses are higher in rainfed crops on sloping terraces (highest soil loss is 32 tons/ha/yr) and minimal under dense forest and in irrigated rice fields (less than 1 ton/ha/yr). However there is high frequency of slope failures in the form of slumps in the rice fields. Debris slides are more common on south-facing slopes under rainfed agriculture or in degraded forest. Field evidences and analysis of causal factors for land degradation helps in building decision trees, the use of which for modelling land degradation has the advantage that attributes can be ranked and tested according to their importance. In addition, decision trees are simple to construct, easy to implement and very flexible in adaptations.

• Journal of Soil and Groundwater Environment
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• v.23 no.4
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• pp.16-25
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• 2018

The Quaternary volcanic rocks, clastic sedimentary rocks of Kyongsang System, and carbonate rocks of Joseon and Pyongan System are known as good productive and potential aquifer systems in South Korea. National Groundwater Informaton Mangement and Service System (GIMS) indicates that the exploitable, sustainable, and current use of groundwater are about 18.8, 12.9, and $3.73billion\;m^3/a$, respectively. The rest amount ($9.1billion\;m^3/a$) can still be used for an additional water supply source. Therefore. comprehensive groundwater survey work comprising hydrogeological mapping, subsurface investigation and quantitative aquifer test etc. are highly required to establish rational groundwater management strategy.

• Journal of Biosystems Engineering
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• v.30 no.2 s.109
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• pp.127-133
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• 2005

Precision tillage is designed to till lands variably according to their firmness. Therefore, it is necessary to measure soil firmness in fields and present it in a form with which the variable tillage on be performed. Such forms may be classified into two categories: sensor-based and map-based forms. The map-based approach appears to be inevitable until the technology develops high enough to secure the sensor-based approaches. The first step for map-based precision tillage may be to develop a tillage recommendation map. In this study, a tractor-mountable automatic soil firmness measurement system was developed to construct a cone index map. The system is comprised of three ASAE Standard cone penetrometers and a hydraulic unit for controlling operation of the penetrometers. The system is designed to conduct stop-and-go measurements in fields. The measurements from the three penetrometers are transferred to a microcomputer and the average cone index was calculated. This average cone index was taken as soil firmness of the location where the measurement was made. The cone indices thus determined were used to construct a cone index map using the ArcView software. The system also displays the soil penetration resistance, cone index and soil depth as the cone penetrates into the soil. The field performance of the system was evaluated and the cone index maps at different depths were also presented.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.6
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• pp.908-913
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• 2007

Concentrations of major elements in forages were determined in relation to ruminant requirements at a livestock experimental station in Leiah district, Punjab-Pakistan using mapping techniques. The study investigated the influence of sampling periods and pasture types on the concentrations of calcium, magnesium, potassium, and sodium in forages. The implications of these forages for nutrition of ruminants were assessed for the livestock population at that particular experimental station, which are supported by the farm pastures. Within the farm, variations in the element status of the forages were related to soil pasture types. A tentative assessment of the mineral status of available forages at this farm for different pastures using guidelines developed for domestic animals indicated deficiencies of only Na, but forages contained adequate Ca, Mg, and K levels required for grazing ruminants. The concentrations of Na in the forage reserves indicated that the potential supply of this element to plants was limited from the soil to plants and from plants to the animals grazing them. Soil minerals were not measured in this study. The potential use of fertilizers, as pasture amendment as well as supplementation of ruminants, with a specifically tailored mineral mixture is important to livestock producers and environmentalists as well, because their use may improve forage nutritive value and in turn meet requirements of animals.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.47-60
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• 2017

The permafrost active layer plays an important role in permafrost dynamics. Ecological patterns, processes, and water and ice contents in the active layer are spatially and temporally complex depending on landscape heterogeneity and local-scale variations in hydrological processes. Although there has been emerging interest in the application of optical remote sensing techniques to permafrost environments, optical sensors are significantly limited in accessing information on near surface geo-cryological conditions. The primary objective of this study was to investigate capability of L-band SAR data for monitoring spatio-temporal variability of permafrost ecosystems and underlying soil conditions. This study exploits information from different polarimetric SAR observables in relation to permafrost environmental conditions. Experimental results show that each polarimetric radar observable conveys different information on permafrost environments. In the case of the dual-pol mode, the radar observables consist of two backscattering powers and one correlation coefficient between polarimetric channels. Among them, the dual-pol scattering powers are highly sensitive to freeze/thaw transition and can discriminate grasslands or ponds in thermokarst area from other permafrost ecosystems. However, it is difficult to identify the ground conditions with dual-pol observables. Additional backscattering powers and correlation coefficients obtained from quad-pol mode help understanding seasonal variations ofradar scattering and assessing geo-cryological information on soil layers. In particular, co-pol coherences atHV-basis and circular-basis were found to be very usefultools for mapping and monitoring near surface soil properties.

• Geotechnical Engineering
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• v.12 no.4
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• pp.21-32
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• 1996

To predict the stress-strain behavior of the soil more approximately, the concept of the critical state soil mechanics was added to the plasticity increment theory in the bounding surface Plasticity model. This model was constituted with two ellipse and one hyperbola in older to describe the behaviour of the isotropically consolidated soil. Thus, this model is very complicate due to the various parameters used. Therefore, the accurate understanding and skill of the theory is required in order to apply this model to the practical geotechnical problems. In the present paper, the bounding surface shape paraiheter R and A, the mapping center parameter C among various parameters used were varied and the results were numerically analized. Finally, each sensitivity with respect to monotonic and cyclic loading was analized and the range of the value of the each parameter was proposed.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.148-148
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• 2019

The 2006 Millennium Ecosystem Assessment (MA) defines ecosystem services (ES) as "the benefits people obtain from ecosystems". Identifying where ES originates, whom it benefits and how it is changing over a period of time is critical in rapidly developing country like Nepal, where the risk of ES loss is high. In the context of various ecosystem services provided by watershed, this study, particularly deals with water yield, Soil loss and Carbon sequestration computation and evaluation in Bagmati Basin of Nepal. As Bagmati Basin incorporates capital city Kathmandu of nepal, land use change is significant over decades and mapping of ES is crucial for sustainable development of Basin in future. In this regard, the objectives of this study are 1) To compute the total and sub-watershed scale water yield of the basin, 2) Computation of soil loss and sediment retention in the basin, and 3) Computation of carbon sequestration in the basin. Integrated Valuation of Environmental Services and Tradeoffs (InVEST), a popular model for ecosystem service assessment based on Budyko hydrological method is used to compute Ecosystem services. The scenario of ES in two periods of time can be referenced for various approaches of prioritization and incorporation of their value into local and regional decision making for management of basin.

• Geomechanics and Engineering
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• v.32 no.3
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• pp.293-305
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• 2023

To ensure the safety of underground infrastructures, ground can sometimes be first treated by cement slurry and then stabilized using artificial ground freezing (AGF) technique before excavation. The hydration heat produced by cement slurry increases the soil temperature before freezing and results in an extension of the active freezing time (AFT), especially when the Metro Jet System (MJS) treatment is adopted due to a high cement-soil ratio. In this paper, by taking advantage of an on-going project, a case study was performed to evaluate the influence of MJS and AGF on the ground temperature variation through on-site measurement and numerical simulation. Both on-site measurement and simulation results reveal that MJS resulted in a significant increase in the soil temperature after treatment. The ground temperature gradually decreases and then stabilized after completion of MJS. The initiation of AGF resulted in a quick decrease in ground temperature. The ground temperature then slowly decreased and stabilized at later freezing. A slight difference in ground temperature exists between the on-site measurements and simulation results due to limitations of numerical simulation. For the AGF system, numerical simulation is still strongly recommended because it is proven to be cost-effective for predicting the ground temperature variation with reasonable accuracy.