• Journal of Environmental Science International
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• v.12 no.4
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• pp.399-417
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• 2003

The seasonal variation and frequency of rainfalls of Korea peninsula in Changma period show strong local weather phenomenon because of it's topographical and geographical factors in Northeast side of Asia. Based on weather entropy(statistical parameter)-the amount of average weather information-and information ratio, we can define each area's weather representativeness, which can show us more constant form included topographical and geographical factors and seasonal variation. The data used for this study are the daily precipitation and cloudiness during the recent ten years(1990-1999) at the 73 stations in Korea. To synthesize weather Entropy, information ratio of decaying tendency and half$.$decay distance, Seoul's weather representativeness has the smallest in Summer Changma period. And Puyo has the largest value in September.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.207-229
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• 1995

The characteristics of weather and climate in South Korea has great influences on the annual variation pattern and the appearance of the prevailing weather. The purpose of this paper is to induce the quantity of the weather entropy and annual variation pattern using the information theory and the principal component analysis. And author tried to classify the region according to the variation of its space scale, The raw materials used for this study are the daily cloudiness and precipitation during the years 1990-1994 at 69 stations in South Korea. It is divided into four classes of fine, clear, cloudy and rainy. The rcsults of this study can be summarized as follows: 1. Thc characteristics of annual variation pattern of weather entropy can be chiefly divided into five categories and the accumulated contributory rate of these is 73.1%. 2. Annual variation pattern of the first principal component reaches smaller in May, April and September than national average, and becomes greater when the winter comes. This weather entropy's quantity(Rs1) is positive in most area to the western sife of Soback Mountains and negative in most seaside area to the eastern side of Soback Mountains. 3. The characteristics of annual variation pattern of the second principal component shows that the entropy is more smaller in summer than national average and the rest of seasons shows larger, especially in January, May and September. This weather entropy's quantity(Rs2) is positive in most Honam Inland area to the western side of Soback Mountains and negative in most Youngnam Inland area to the eastern side of Soback Mountains. 4. Eight type regions (S1-S11) are classified based on the occurrences of minimum weather entropy in South Korea, and annual variation pattern of weather entropy by principal component analysis may be classified into sixteen type regions (Rs1-Rs9). Putting these things together, South Korea can be classifieed into thirty one type regions (Rs1S7-Rs9S10).

• Journal of the Korean Geographical Society
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• v.31 no.3
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• pp.469-488
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• 1996

The defining of weather forecasting regions is possible. since the representativeness of regional weather can by reasonably clarified in terms of weather entropy and the use of information ratio. In this paper, the weather entropy and information ratio were derived numerially from using the information theory. The typical weather characteristics were clarified and defined in the homogeneous weather forecasting regions of the southern parts of Korea. The data used for this study are the daily precipitation and cloudiness during the recent five years (1990-1994) at 42 stations in southern parts of Korea. It is divided into four classes of fine, clear, cloudy and rainy. The results are summarized as follows: 1. The maximum value of weather entropy in study area is 2.009 vits in Yosu in July, and the minimum one is 1.624 bits in Kohung in October. The mean value of weather entropy is maximal in July, on the other hand, minimal in October during four season. The less the value of entropy is, the stabler the weather is. While the bigger the value of entropy is, the more changeable the weather is. 2. The deviation from mean value of weather entropy in southern parts of Korea, with the positive and the negative parts, shows remarkably the distributional tendency of the east (positive) and the west (negative) in January but of the south (positive) and the north (negative) in July. It also clearly shows the distributional tendency of the east (postive) and the west(negative) in the coastal region in April, and of X-type (southern west and northern east: negative) in Chiri Mt. in October. 3. In southern parts, the average information ratio maximaly appear 0.618 in Taegu area in July, whereas minimally 0.550 in Kwangju in October. Particularly the average information ratio of Pusan area is the greatest in April, but the smallest in October. And in Taegu, Kwangju, and Kunsan, it is the greatest in April, January, and July, but the smallest in Jyly, July, and pril. 4.The narrowest appreance of weather representativeness is in July when the Kwangju is the center of the weather forecasting. But the broadest one is in April when Taegu is the center of weather forecasting. 5. The defining of weather forecasting regions in terms of the difference of information ratio most broadly shows up in July in Pusan including the whole Honam area and the southern parts of Youngnam when the Pusan-Taegu is the basis of the application of information ratio. Meanwhile, it appears most broadly in January in Taegu including the whole southern parts except southern coastal area.

• Proceedings of the KGS Conference
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• 2005.05a
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• pp.89-91
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• 2005

• Journal of the Korean earth science society
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• v.27 no.2
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• pp.159-176
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• 2006

Geographic factors and mathmatical location of the Korean Peninsula have great influences on the variation patterns and appearances over a period of ten days of summer precipitation. In order to clarify the influence of several climate factors on precise climate classification in the middle part region of the Korea, weather entropy and the information ratio were calculated on the basis of information theory and of the data of 25 site observations. The data used for this study are the daily precipitation phenomenon over a period of ten days of summer during the recent thirteen years (1991-2003) at the 25 stations in the middle part region of the Korea. It is divided into four classes of no rain, $0.1{\sim}10.0mm/day,\;10.1{\sim}30.0mm/day$, 30.1mm over/day. Their temporal and spatial change were also analyzed. The results are as follows: the maximum and minimum value of calculated weather entropy are 1.870 bits at Chuncheon in the latter ten days of July and 0.960 bits at Ganghwa during mid September, respectively. And weather entropy in each observation sites tends to be larger in the beginning of August and smaller towards the end of September. The largest and smallest values of weather representative ness based on information ratio were observed at Chungju in the beginning of June and at Deagwallyeong towards the end of July. However, the largest values of weather representativeness came out during the middle or later part of September when 15 sites were adopted as the center of weather forecasting. The representative core region of weather forecasting and climate classification in the middle part region of the Korea are inside of the triangle region of the Buyeo, Incheon, and Gangneung.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.2
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• pp.7-14
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• 2012

To analyze any given situation, it is necessary to have information on elements which affect the situation. Particularly, there is greater variability in both frequency and magnitude of agricultural product prices as they are affected by various unpredictable factors such as weather conditions etc. This is the reason why it is difficult for the farmers to maintain their stable income through agricultural production and marketing. In this research, attempts are made to quantify the entropy of various situations inherent in the price changes so that the stability of farmers' income can be increased. Through this research, we developed an entropy model which can quantify the uncertainties of price changes using the probability distribution of price changes. The model was tested for its significance by comparing its simulation outcomes with actual ranges and standard deviations of price variations of the past using monthly agricultural product prices data. We confirmed that the simulation results reflected the features of the ranges and standard deviations of actual price variations. Also, it is possible for us to predict standard deviations for changed prices which will occur after a certain time using the information entropy obtained from relevant agricultural product price data before the time.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.1025-1032
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• 2020

In this paper, we propose a method to classify road weather conditions into rain, fog, and sun using a SVM (Support Vector Machine) classifier after extracting weather features from images acquired in real time using an optical sensor installed on a roadside post. A multi-dimensional weather feature vector consisting of factors such as image sharpeness, image entropy, Michelson contrast, MSCN (Mean Subtraction and Contrast Normalization), dark channel prior, image colorfulness, and local binary pattern as global features of weather-related images was extracted from road images, and then a road weather classifier was created by performing machine learning on 700 sun images, 2,000 rain images, and 1,000 fog images. Finally, the classification performance was tested for 140 sun images, 510 rain images, and 240 fog images. Overall classification performance is assessed to be applicable in real road services and can be enhanced further with optimization along with year-round data collection and training.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.49-73
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• 1996

This study produces quantitatively weather entropy and information ratio using information theory about frequency in the appearance of precipitation phenomenon and monthly change, and then applies them to observation of the change of their space scale by time. As a result of these, this study defines Pusan, Chongju and Kwangju's weather representativeness and then establishes the range of weather representativeness. Based on weather entropy (statistical parameter)-the amount of average weather information-and information ratio, we can define each area's weather representativeness, which can show us more constant form included topographical, geographical factors and season change. The data used for this study are the daily precipitotion and cloudiness during the recent five years($1990{\sim}1994$) at the 69 stations in Korea. It is divided into class of no precipitation, that of precipitation. The results of this study can be summarized as follows: (1) The four season's mean value of information ratio is the highest value. as 0.641, on the basis of Chongju. It is the lowest as 0.572, on the basis of Pusan. On a seasonal basis, the highest mean value of information rate is April's (spring) in Chongju, and the lowest is October's(fall) in Pusan. Accordingly weather representativeness has the highest in Chongju and the lowest in Pusan. (2) To synthesize information ratio of decaying tendancy and half-decay distance, Chonju's weather representativeness has the highest in April, July and October. And kwangju has the highest value in January and the lowest in April and July. Pusan's weather representativeness is not high, that of Pusan's October is the lowest in the year. (3) If we establish the weather representative character on the basis of Chongju-Pusan, the domain of Chongju area is larger than that of Pusan area in October, July and April in order. But Pusan's is larger than Chongju's in January. In the case of Chongju and Kwangju, the domain of Chongju area is larger than that of Kwangju in October, July and April in order, but it is less than that of Kwangju area in January. In the case of Kwangju-Pusan, the domain of Kwangju is larger than that of Pusan in October, July in order. But in April it is less than Pusan's.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.67-81
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• 2020

The rapid climatic changes being caused by global warming are resulting in abnormal weather conditions worldwide, which in some regions have increased the frequency of landslides. This study was aimed to analyze and compare the landslide susceptibility using the Frequency Ratio (FR), Statistical Index, Weight of Evidence, Certainty Factor, and Index of Entropy (IoE) at Woomyeon Mountain in South Korea. Through the construction of a landslide inventory map, 164 landslide locations in total were found, of which 50 (30%) were reserved to validate the model after 114 (70%) had been chosen at random for model training. The sixteen landslide conditioning factors related to topography, hydrology, pedology, and forestry factors were considered. The results were evaluated and compared using relative operating characteristic curve and the statistical indexes. From the analysis, it was shown that the FR and IoE models were better than the other models. The FR model, with a prediction rate of 0.805, performed slightly better than the IoE model with a prediction rate of 0.798. These models had the same sensitivity values of 0.940. The IoE model gave a specific value of 0.329 and an accuracy value of 0.710, which outperforms the FR model which gave 0.276 and 0.680, respectively, to predict the spatial landslide in the study area. The generated landslide susceptibility maps can be useful for disaster and land use planning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.325-333
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• 2020

Current methods of measuring the sediment concentration of natural streams can be affected by weather conditions and have lower reliability in bed-load sections due to mechanical limits. Theoretical methods have to be used to solve this problem, but they have low reliability compared to the measured values and diverse results for the bed-load sediment concentration. This study proposes a new way to reliably determine the bed-load sediment concentration from the relation with theoretical depth-integrated concentration based on the informational entropy concept. Sediment distribution shows a uniform probability distribution under maximized entropy conditions under some constraints, so a function can be calculated for the sediment distribution and depth-integrated concentration. The parameters of a stream were estimated by a nonlinear regression method using the concentration data from a past experiment. Equilibrium N (EN) was estimated using the relation between two different formulas proposed in this study, which can ease the estimation of both the total sediment distribution and depth-integrated sediment concentration with high reliable results with an average R2 of 0.924.