• Atmosphere
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• v.25 no.1
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• pp.155-167
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• 2015

A dynamical seasonal prediction system for boreal winter utilizing cryospheric information was developed. Using the Community Atmospheric Model, version3, (CAM3) as a modeling system, newly developed snow depth initialization method and sea ice concentration treatment were implemented to the seasonal prediction system. Daily snow depth analysis field was scaled in order to prevent climate drift problem before initializing model's snow fields and distributed to the model snow-depth layers. To maximize predictability gain from land surface, we applied one-month-long training procedure to the prediction system, which adjusts soil moisture and soil temperature to the imposed snow depth. The sea ice concentration over the Arctic region for prediction period was prescribed with an anomaly-persistent method that considers seasonality of sea ice. Ensemble hindcast experiments starting at 1st of November for the period 1999~2000 were performed and the predictability gain from the imposed cryospheric informations were tested. Large potential predictability gain from the snow information was obtained over large part of high-latitude and of mid-latitude land as a result of strengthened land-atmosphere interaction in the modeling system. Large-scale atmospheric circulation responses associated with the sea ice concentration anomalies were main contributor to the predictability gain.

• Journal of the Korean Institute of Landscape Architecture
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• v.22 no.4
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• pp.75-80
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• 1995

It is important to estimate the possibility of recovery in physiologically damaged woody plant. It is suggested that C.E.R(cambial electrical resistance) might be a useful method to predict the permanent wilting point. D/A and A/D converter can be used to measure the C.E.R and it took only 10-20 msec for a measurement and the values were stable during this study. A computer could be used for the continual measurement of C.E.R. There were very big daily changes of C.E.R. was changed according to the changes of indoor temperature, but the phase was slightly different. It is reasoned that daily changes in C.E.R. is induced by the changes of water potential and cambial thickness. It was difficult to detect the changes of C.E.R. caused by changes in soil moisture under high soil water potential. Under low soil water potential, the changes in soil moisture under high soil water potential. Under low soil water potential, the changes of C.E.R. can be detected. After wilting, C.E.R. is increased very rapidly. When C.E.R. is not decreased by watering, it will be permanent wilting point. But it takes several days to confirm the permanent wilting point. To predict the possibility of recovery from wilting, the values of C.E.R. have no meaning. But the changes of C.E.R. are significant. Therefore we can predict the permant wilting point in woody plant by monitoring the change of C.E.R. by the computer.

• Journal of Korean Society of Transportation
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• v.29 no.2
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• pp.123-131
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• 2011

This study proposes a model for road surface temperature prediction on basis of the heat-energy balance equation between atmosphere and road surface. The overall model is consisted of two types of modules: 1) Canopy 1 is used to describe heat transfer between soil surface and atmosphere; and 2) Canopy 2 can reflect the characteristics of pavement type. Input data used in the model run is obtained from the Korea Meteorological For model validation, the observed and predicted surface temperature data are compared using data collected on MoonEui Bridge along CheongWon-Sangju Expressway, and the comparison is made on winter and other seasons separately. Analysis results show that average difference between two temperatures lies within ${\pm}2^{\circ}C$ which is considered as appropriate from a micrometeorology point of view. The model proposed in this paper can be adopted as a useful tool in practical applications for winter maintenance. This study being a fundamental research is anticipated to be a starting point for further development of robust surface road temperature prediction algorithms.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.4
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• pp.131-136
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• 2016

Horizontal geothermal heat exchanger is affected by various factors such as pipe length, soil temperature, and outdoor environment. Simulation program is convenient for responding to various factors. The objective of this study was to determine the feasibility of using EnergyPlus to predict exit air temperature through horizontal geothermal heat exchanger in domestic. The correlation coefficient between EnergyPlus results and experimental results was 0.825. The correlation coefficient between EnergyPlus results and mathematical results was 0.722, indicating "The two values can based on Lousi on values can be Our results indicate that it is possible to use EnergyPlus to predict exit air temperature through horizontal geothermal heat exchanger.

• Korean Journal of Weed Science
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• v.30 no.2
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• pp.94-102
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• 2010

Field and pot experiments were conducted to investigate seedling emergence and early growth of Eleocharis kuroguwai panted on different dates. Non-linear regression analyses of observed data against effective accumulated temperature (EAT) with the Gompertz model showed that the Gompertz model works well in describing seedling emergence and early growth of E. kuroguwai regardless of planting date and soil burial depth. EATs required for 50% of the maximum seedling emergence of E. kuroguwai planted at 1, 3 and 5 cm soil burial depth in the pot experiment were estimated to be 54.5, 84.0 and $118.0^{\circ}C$, respectively, and $56.7^{\circ}C$ when planted at 1 cm in the field experiment. EATs required for 50% of the maximum leaf number of E. kuroguwai planted at 1, 3 and 5 cm soil burial depth in the pot experiment were estimated to be 213.3, 249.0 and $291.6^{\circ}C$, respectively, and $239.5^{\circ}C$ when planted at 1 cm in the field experiment. Therefore, models developed in this study thus predicted that if rotary tillage with water is made on 27 May under $+2^{\circ}C$ elevated temperature condition, dates for 50% of the maximum seedling emergence, 5 leaf stage and 5 cm plant height of E. kuroguwai buried at 3 cm soil depth were predicted to be 2 June, 10 June and 12 June. These dates are 1 day earlier for the seedling emergence and 3 days earlier for the early growth as compared with current temperature condition, suggesting that earlier application of herbicides is required for effective control of E. kuroguwai.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.181-191
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• 2020

In order to produce accurate initial condition of soil moisture for global Numerical Weather Prediction (NWP), spin-up experiment is carried out using Noah Land Surface Model (LSM). The model is run repeatedly through 10 years, under the atmospheric forcing condition of 2008-2017 until climatological land surface state is achieved. Spin-up time for the equilibrium condition of soil moisture exhibited large variability across Koppen-Geiger climate classification zone and soil layer. Top soil layer took the longgest time to equilibrate in polar region. From the second layer to the fourth layer, arid region equilibrated slower (7 years) than other regions. This result means that LSM reached to equilibrium condition within 10 year loop. Also, spin-up time indicated inverse correlation with near surface temperature and precipitation amount. Initialized from the equilibrium state, LSM was spun up to obtain land surface state in 2018. After 6 months from restarted run, LSM simulates soil moisture, skin temperature and evaportranspiration being similar land surface state in 2018. Based on the results, proposed LSM spin-up system could be used to produce proper initial soil moisture condition despite updates of physics or ancillaries for LSM coupled with NWP.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.132-140
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• 2020

It is very important to use appropriate nutrition water for crop growth in hydroponic farming facilities. However, in many cases, the supply of nutrition water is not designed with a precise plan, but is performed in a conventional manner. We proposes a forecasting technique for nutrition water requirements based on a data analysis for optimal strawberry production. To do this, the proposed forecasting technique uses linear regression for correlating strawberry production, soil condition, and environmental parameters with nutrition water demand for the actual two-stage strawberry production soil. Also, it includes predicting the optimal amount of nutrition water requires according to the heterogeneous cultivation environment and variety by comparing the amount of nutrition water needed for the growth and production of different kinds of strawberries. We suggested study uses two types of section beds that are compared to find out the best section bed production of strawberry growth. The dataset includes 233 samples collected from a real strawberry greenhouse, and the four predicted variables consist of the total amounts of nutrition water, average temperature, humidity, and CO₂ in the greenhouse.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.330-334
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• 2022

In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture nowadays is selecting the right crop for the right piece of land at the right time. First problem is that How Farmers can predict the right crop for cultivation because famers have no knowledge about prediction of crop. Second problem is that which algorithm is best that provide the maximum accuracy for crop prediction. Therefore, in this research Author proposed a method that would help to select the most suitable crop(s) for a specific land based on the analysis of the affecting parameters (Temperature, Humidity, Soil Moisture) using machine learning. In this work, the author implemented Random Forest Classifier, Support Vector Machine, k-Nearest Neighbor, and Decision Tree for crop selection. The author trained these algorithms with the training dataset and later these algorithms were tested with the test dataset. The author compared the performances of all the tested methods to arrive at the best outcome. In this way best algorithm from the mention above is selected for crop prediction.