• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1748-1755
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• 2015

A linear regression is widely used for prediction problem, but it is hard to manage an irregular nature of nonlinear system. Although nonlinear regression methods have been adopted, most of them are only fit to low and limited structure problem with small number of independent variables. However, real-world problem, such as weather prediction required complex nonlinear regression with large number of variables. GP(Genetic Programming) based evolutionary nonlinear regression method is an efficient approach to attach the challenging problem. This paper introduces the improvement of an GP based nonlinear regression method using ADF(Automatically Defined Function). It is believed ADFs allow the evolution of modular solutions and, consequently, improve the performance of the GP technique. The suggested ADF based GP nonlinear regression methods are compared with UM, MLR, and previous GP method for 3 days prediction of wind speed using MOS(Model Output Statistics) for partial South Korean regions. The UM and KLAPS data of 2007-2009, 2011-2013 years are used for experimentation.

• Journal of Digital Convergence
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• v.20 no.3
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• pp.41-52
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• 2022

Although domestic public libraries achieved quantitative growth based on the 1st and 2nd comprehensive library development plans, there were some qualitative shortcomings, and various studies have been conducted to improve them. Most of the preceding studies have limitations in that they are limited to social and economic factors and statistical analysis. Therefore, in this study, by applying the spatiotemporal concept to quantitatively calculate the decrease in public library loan demand due to rainfall and heatwave, by clustering areas with high demand for book loan due to weather changes and areas where it is not, factors inside and outside public libraries and After the combination, changes in public library loan demand according to weather changes were analyzed. As a result of the analysis, there was a difference in the decrease due to the weather for each public library, and it was found that there were some differences depending on the characteristics and spatial location of the public library. Also, when the temperature was over 35℃, the decrease in book loan demand increased significantly. As internal factors, the number of seats, the number of books, and area were derived. As external factors, the public library access ramp, cafe, reading room, floating population in their teens, and floating population of women in their 30s/40s were analyzed as important variables. The results of this analysis are judged to contribute to the establishment of policies to promote the use of public libraries in consideration of the weather in a specific season, and also suggested limitations of the study.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.34 no.3
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• pp.261-269
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• 1989

Agricultural activities in Chejudo require more specialized weather services in this region. The meteorological information available from the Korea Meteorological Service (KMS) is limited in its areal coverage because the KMS stations are located along the narrow band of coastal area. topoclimatological technique which makes use of empirical relationships between the topography and the weather can be applied to produce reasonable estimates of the climatic variables such as air temperature and precipitation over remote land area where routine observations are rare. Presentation of these estimates in a from of fine-mesh grid map can also be helpful to upgrade the quality of weather services in this region. Altitude values of the 250 m grid points were read from a 1: 25000 topographic map and the mean altitude, the mean slope, and the aspect of the slope were determined for each 1 km$^2$ land area from these altitude data. Daily minimum air temperature data were collected from 18 points in Chejudo during the winter period from November 1987 to February 1988. The data were grouped into 3 sets based on synoptic pressure pattern. Departures from the KMS observations were regressed to the topographical variables to delineate empirical relationships between the local minimum temperature under specific pressure patterns and the site topography. The selected regression equations were used to calculate the daily minimum temperature for each 1 km$^2$ land area under the specific pressure patterns. The outputs were presented in a fine-mesh grid map with a 6-level contour capability.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.21-27
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• 2013

Construction safety is a predominant hindrance in in-situ workflow and considered an unresolved issue. Current methods used for safety optimization and prediction, with limited exceptions, are paper-based, thus error prone, as well as time and cost ineffective. In an attempt to exploit the potential of BIM for safety, the objective of the proposed methodology is to automatically predict hazardous on-site conditions related to the route that the dozers follow during the different phases of the project. For that purpose, safety routes used by construction equipment from an origin to multiple destinations are computed using video cameras and their cycle times are calculated. The cycle times and factors; including weather and light conditions, are considered to be independent and identically distributed random variables (iid); and simulated using the Arena software. The simulation clock is set to 100 to observe the minor changes occurring due to external parameters. The validation of this technology explores the capabilities of BIM combined with simulation for enhancing productivity and improving safety conditions a-priori. Preliminary results of 262 measurements indicate that the proposed methodology has the potential to predict with 87% the location of exclusion zones. Also, the cycle time is estimated with an accuracy of 89%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.391-402
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• 2016

Among the various remote sensing sensors compared to the electro-optical sensors, SAR (Synthetic Aperture Radar) is very suitable for assessing damaged areas induced by disaster events owing to its all-weather day and night acquisition capability and sensitivity to geometric variables. The conventional CD (Change Detection) method that uses two-date data is typically used for mapping damage over extensive areas in a short time, but because data from only two dates are used, the information used in the conventional CD is limited. In this paper, we propose a novel CD method that is extended to use data consisting of two pre-disaster SAR data and one post-disaster SAR data. The proposed CD method detects changes by using a similarity weight image derived from the neighborhood information of a pixel in the data from the three dates. We conducted an experiment using three single polarization ALOS PALSAR (Advanced Land Observing Satellite/Phased Array Type L-Band) data collected over Miyagi, Japan which was seriously damaged by the 2011 east Japan tsunami. The results demonstrated that the mapping accuracy for damaged areas can be improved by about 26% with an increase of the g-mean compared to the conventional CD method. These improved results prove the performance of our proposed CD method and show that the proposed CD method is more suitable than the conventional CD method for detecting damaged areas induced by disaster.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.271-271
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• 2015

The Ecuadorian coast has two different climate regions. One is humid region where the annual rainfall is above 2000 mm and rain falls in almost all months of the year, and the other is dry region where the annual rainfall can fall below 50 mm and rainfall can be very seasonal. The agriculture is frequently limited by the seasons during the year and the availability of rainfall amounts. The corn fields in Ecuador are cultivated during the rainy season, due to this reason. The weather conditions for optimum development of corn growth require a monthly average rainfall of 120 mm to 140 mm and a temperature range of $22^{\circ}C{\sim}32^{\circ}C$ for the dry region, and a monthly average rainfall of 200 mm to 400 mm and a temperature range of $25^{\circ}C{\sim}30^{\circ}C$ for the humid area. The objective of this study is to predict how the weather conditions are going to change in corn fields of the coastal region of Ecuador in the future decades. For this purpose, this study selected six General Circulation Models (GCM) including BCC-CSM1-1, IPSL-CM5A-MR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRIC-CGC3 with different climate scenarios of the RCP 4.5, RCP 6.0, and RCP 8.5, and applied for the period from 2011 to 2100. The climate variables information was obtained from the INAMHI (National Institute of Meteorology and Hydrology) in Ecuador for the a base line period from 1986 to 2012. The results indicates that two regions would experience significant changes in rainfall and temperature compared to the historical data. In the case of temperature, an increment of $1^{\circ}C{\sim}1.2^{\circ}C$ in 2025s, $1.6^{\circ}C{\sim}2.2^{\circ}C$ in 2055s, $2.1^{\circ}C{\sim}3.5^{\circ}C$ in 2085s were obtained from the dry region while less increment were shown from the humid region with having an increment of $1^{\circ}C$ in 2025s, $1.4^{\circ}C{\sim}1.8^{\circ}C$ in 2055s, $1.9^{\circ}C{\sim}3.2^{\circ}C$ in 2085s. Significant changes in rainfall are also projected. The rainfall projections showed an increment of 8%~11% in 2025s, 21%~33% in 2055s, and 34%~70% in 2085s for the dry region, and an increment of 2%~10%, 14%~30% and 23%~57% in 2025s, 2055s and 2085s decade respectively for humid region.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.312-319
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• 2013

Our understanding of the sensitivities of crop responses to changes in carbon dioxide, temperature, and water is limited, which makes it difficult to fully utilize crop models in assessing the impact of climate change on future agricultural production. Genetic coefficients of CERES-Barley model for major domestic cultivars in South Korea (Olbori at Suwon, Albori at Milyang, Saessalbori at Iksan, and Samdobori at Jinju) were estimated from the observed data for daily weather and field trials for more than 10 years by using GenCalc in DSSAT. Data from 1997-2002 annual crop status report (Rural Development Administration, RDA) were used to validate the crop coefficients. The sitecalibrated CERES-Barley model was used to perform crop growth simulation with the 99 treatments of step change combinations in temperature, precipitation and carbon dioxide concentration with respect to the baseline climate (1981-2010) at four sites. The upper boundary corresponds to the 2071-2100 climate outlook from the RCP 8.5 scenario. The response surface of grain yield showed a distinct pattern of model behavior under the combined change in environmental variables. The simulated grain yield was most sensitive to $CO_2$ concentration, least sensitive to precipitation, and showing a variable response to temperature depending on cultivar. The emulated impacts of response surfaces are expected to facilitate assessment of projected climate impacts on a given cultivar in South Korea.

• Korean Journal of Environment and Ecology
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• v.29 no.3
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• pp.333-343
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• 2015

This research aims at identifying the goshawk's possible and replaceable breeding ground by using the MaxEnt prediction model which has so far been insufficiently used in Korea, and providing evidence to expand possible protection areas for the goshawk's breeding for the future. The field research identified 10 goshawk's nests, and 23 appearance points confirmed during the 3rd round of environmental research were used for analysis. 4 geomorphic, 3 environmental, 7 distance, and 9 weather factors were used as model variables. The final environmental variables were selected through non-parametric verification between appearance and non-appearance coordinates identified by random sampling. The final predictive model (MaxEnt) was structured using 10 factors related to breeding ground and 7 factors related to appearance area selected by statistics verification. According to the results of the study, the factor that affected breeding point structure model the most was temperature seasonality, followed by distance from mixforest, density-class on the forest map and relief energy. The factor that affected appearance point structure model the most was temperature seasonality, followed by distance from rivers and ponds, distance from agricultural land and gradient. The nature of the goshawk's breeding environment and habit to breed inside forests were reflected in this modeling that targets breeding points. The northern central area which is about $189.5 km^2$(2.55 %) is expected to be suitable breeding ground. Large cities such as Cheongju and Chungju are located in the southern part of Chungcheongbuk-do whereas the northern part of Chungcheongbuk-do has evenly distributed forests and farmlands, which helps goshawks have a scope of influence and food source to breed. Appearance point modeling predicted an area of $3,071 km^2$(41.38 %) showing a wider ranging habitat than that of the breeding point modeling due to some limitations such as limited moving observation and non-consideration of seasonal changes. When targeting the breeding points, a specific predictive area can be deduced but it is difficult to check the points of nests and it is impossible to reflect the goshawk's behavioral area. On the other hand, when targeting appearance points, a wider ranging area can be covered but it is less accurate compared to predictive breeding point since simple movements and constant use status are not reflected. However, with these results, the goshawk's habitat can be predicted with reasonable accuracy. In particular, it is necessary to apply precise predictive breeding area data based on habitat modeling results when enforcing an environmental evaluation or establishing a development plan.

• Journal of Korean Society of Forest Science
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• v.109 no.3
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• pp.259-270
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• 2020

Extreme weather events, such as heat and drought, have occurred frequently over the past two decades. This has led to continuous reports of cases of forest damage due to physiological stress, not pest damage. In 2014, pine trees were collectively damaged in the forest genetic resources reserve of Sogwang-ri, Uljin, South Korea. An investigation was launched to determine the causes of the dieback, so that a forest management plan could be prepared to deal with the current dieback, and to prevent future damage. This study aimedto 1) understand the topographic and structural characteristics of the area which experienced pine tree dieback, 2) identify the main causes of the dieback, and 3) predict future risk areas through the use of machine-learning techniques. A model for identifying risk areas was developed using 14 explanatory variables, including location, elevation, slope, and age class. When three machine-learning techniques-Decision Tree, Random Forest (RF), and Support Vector Machine (SVM) were applied to the model, RF and SVM showed higher predictability scores, with accuracies over 93%. Our analysis of the variable set showed that the topographical areas most vulnerable to pine dieback were those with high altitudes, high daily solar radiation, and limited water availability. We also found that, when it came to forest stand characteristics, pine trees with high vertical stand densities (5-15 m high) and higher age classes experienced a higher risk of dieback. The RF and SVM models predicted that 9.5% or 115 ha of the Geumgang Pine Forest are at high risk for pine dieback. Our study suggests the need for further investigation into the vulnerable areas of the Geumgang Pine Forest, and also for climate change adaptive forest management steps to protect those areas which remain undamaged.