• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• Advances in environmental research
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• v.7 no.2
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• pp.109-120
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• 2018

In the present study research air quality analyses for $PM_{10}$, were conducted in Shiraz, a city in the south of Iran. The measurements were taken from 2011 through 2012 in two different locations to prepare average data in the city. The averages concentrations were calculated for every 24 hours, each month and each season. Results showed that the highest concentration of $PM_{10}$ occurs generally in the night while the least concentration was found at the afternoon. Monthly concentrations of $PM_{10}$ showed highest value in August, while least value was found in January. The seasonal concentrations showed the least amounts in autumn while the highest amounts in summer. Relations between the air pollutant and some meteorological parameters were calculated statistically using the daily average data. The wind data (velocity, direction), relative humidity, temperature, sunshine periods, evaporation, dew point and rainfall were considered as independent variables. The relationships between concentration of pollutant and meteorological parameters were expressed by multiple linear regression equations for both annual and seasonal conditions SPSS software. RMSE test showed that among different prediction models, stepwise model is the best option.

• Korean Journal of Agricultural Science
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• v.46 no.4
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• pp.843-856
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• 2019

Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.10 no.2
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• pp.57-62
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• 1992

As population is increased, land use types are changed mountainous areas from flatland in Korea. Because natural disaster as landslides occur of life, property, and environmental damage, prediction of landslides have become increasingly important. We focus on the issue for assessment of landslides, not slope stability analysis for a simple slope site. In this study, we could know the correlations of mean, standard deviation for brightness value of imagery by aerial photo scanning. The range of brightness values and standard deviation of landslide area is 35~65 and tend to increment of value, in the every years. When evaluating large regions with past occurrence of landslides, it is possible to search for correlation of site conditions such as degree of slope, soil characteristics, vegetative cover, and rainfall conditions in aerial photo interpretation data.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.5
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• pp.163-171
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• 2001

Reaeration phenomena, the physical process of absorption of oxygen from atmosphere, is one of the important parameters of dissolved oxygen simulation in streams. This study was aimed at predicting reaeration coefficients in rural small streams, examining the influence of drop structure on reaeration and the seasonal fluctuation of reaeration coefficients. Reaeration coefficients of five streams including four tributaries of Bokha watershed in Gyeonggi Ichon and Onyang stream in Chungnam Onyang were measured. Constant rate injection (CRI) method using propane and Rhodamine-WT as gas and dye tracer was adopted. Reaeration coefficients ranged between 6.16 and 29.16 reciprocal day, higher than those in USGS database. Prediction equation,$k_2=CV^{0.593}$, was regressed from the measured data at 95% confidence level, with an absolute error of 21.2% and a standard error of 4.0 reciprocal days. Reaeration coefficients of experimental reaches with drop structure showed percentile increases of 42.3 to 159.2 compared to those without it, an indication that drop structure plays an important role on stream reaeration. Taking into consideration the seasonal fluctuation of reaeration coefficients, the values measured during September and October were the highest, mainly due to the removal of aquatic plants. by intensive rainfall during summer.

• Smart Media Journal
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• v.8 no.1
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• pp.82-91
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• 2019

This study provides the analysis and prediction of fruits diseases related to weather conditions (temperature, wind speed, solar power, rainfall and humidity) using Linear Model and Poisson Regression. The main goal of the research is to control the method of fruits diseases and also to prevent diseases using less agricultural pesticides. So, it is needed to predict the fruits diseases with weather data. Initially, fruit data is used to detect the fruit diseases. If diseases are found, we move to the next process and verify the condition of the fruits including their size. We identify the growth of fruit and evidence of diseases with Linear Model. Then, Poisson Regression used in this study to fit the model of fruits diseases with weather conditions as an input provides the predicted diseases as an output. Finally, the residuals plot, Q-Q plot and other plots help to validate the fitness of Linear Model and provide correlation between the actual and the predicted diseases as a result of the conducted experiment in this study.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.211-211
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• 2018

미해양대기청 기후예측센터(Climate Prediction Center, NOAA)에서 제공하고 있는 기후지수(climate indices)를 예측인자로 하고 금강유역의 5~6월의 강우량을 예측대상으로 하는 원격상관기반 통계모형을 구축하였다. 1988년부터 2017년까지의 30년 자료에 대해 예측인자와 예측대상간의 시간지연상관분석을 수행한 결과 NAO(North Atlantic Oscillation), EP/NP(East Pacific/North Pacific Oscillation), EA(East Atlantic Pattern), WP(Western Pacific Index) 등과 상관성이 높은 것으로 분석되었으며, 이러한 시간지연 기후지수를 이용하여 4개월전에 5,6월 강수량을 예측할 수 있는 다중회귀모형을 개발하였다. 관측 강우량 아노말리가 큰 경우에는 다소 과소 예측되고, 아노말리가 작은 경우에는 다소 과다 예측되는 경향을 보였지만 관측 강우량과 예측 강우량간의 상관계수가 0.75로서 비교적 우수한 예측 결과를 나타내었다. 5~6월 강우량 아노말리의 3분위 예측성을 평가한 결과 평년이상 적중률은 77.8%, 평년수준은 81.8%로서 예측 성공률이 높았으며, 5, 6월 누적강우량이 매우 작았던 92년과 95년을 제외하고는 강우량이 적은 해에도 예측성이 우수하여 평년이하 적중률이 70.0%를 나타내었다. 따라서 본 개발모형은 최소 4개월 이전 선행시간을 가지고 늦봄, 초여름강우량을 예측할 수 있는 저비용의 가뭄 예측 도구로 유용하게 활용될 수 있을 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.249-249
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• 2023

Spring drought forecasting in South Korea is essential due to the sknewness of rainfall which could lead to water shortage especially in spring when managed without prediction. Therefore, drought forecasting over South Korea was performed in the current study by thoroughly searching appropriate predictors from the lagged global climate variable, mean sea level pressure(MSLP), specifically in winter season for forecasting time lag. The target predictand defined as accumulated spring precipitation(ASP) was driven by the median of 93 weather stations in South Korea. Then, it was found that a number of points of the MSLP data were significantly cross-correlated with the ASP, and the points with high correlation were regionally grouped. The grouped variables with three regions: the Arctic Ocean (R1), South Pacific (R2), and South Africa (R3) were determined. The generalized linear model(GLM) was further applied for skewed marginal distribution in drought prediction. It was shown that the applied GLM presents reasonable performance in forecasting ASP. The results concluded that the presented regionalization of the climate variable, MSLP can be a good alternative in forecasting spring drought.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.17-28
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• 2023

Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.

• Atmosphere
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• v.32 no.1
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• pp.17-25
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• 2022

As climate change due to global warming continues to be accelerated, various extreme events become more intense, more likely to occur and longer-lasting on a much larger scale. Recent studies show that global warming acts as the primary driver of extreme events and that heat-related extreme events should be attributed to anthropogenic global warming. Among them, both terrestrial and marine heat waves are great concerns for human beings as well as ecosystems. Taking place around the world, one of those events appeared over East Sea in July 2021 with record-breaking high temperature. Meanwhile, climate condition around East Sea was favorable for anomalous warming with less total cloud cover, more incoming solar radiation, and shorter period of Changma rainfall. According to the results of wave activity flux analysis, highly activated meridional mode of teleconnection that links western North Pacific to East Asia caused localized warming over East Sea to become stronger.