• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.490-501
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• 2023

Laboratory column tests were conducted to analyze infiltration and water redistribution processes on the basis of rainfall. To efficiently measure moisture content within soil layers, this research developed a predictive model grounded in a convolutional neural network (CNN), a deep learning technique. The digital images obtained during the column tests were incorporated into the established CNN. The moisture content of each soil layer over time was effectively measured. The measured values were also in relatively good agreement with the moisture content determined using the moisture sensors installed for each soil layer. The use of CNN enabled a comprehensive understanding of continuous moisture distribution within the soil layers, as well as the infiltration process according to soil texture and initial moisture content conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1269-1276
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• 2023

Crop estimation is essential for the multinational meal and powerful demand due to its numerous aspects like soil, rain, climate, atmosphere, and their relations. The consequence of climate shift impacts the farming yield products. We operate the dataset with temperature, rainfall, humidity, etc. The current research focuses on feature selection with multifarious classifiers to assist farmers and agriculturalists. The crop yield estimation utilizing the feature selection approach is 96% accuracy. Feature selection affects a machine learning model's performance. Additionally, the performance of the current graph classifier accepts 81.5%. Eventually, the random forest regressor without feature selections owns 78% accuracy and the decision tree regressor without feature selections retains 67% accuracy. Our research merit is to reveal the experimental results of with and without feature selection significance for the proposed ten algorithms. These findings support learners and students in choosing the appropriate models for crop classification studies.

• Journal of Astronomy and Space Sciences
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• v.41 no.1
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• pp.25-33
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• 2024

This paper evaluates the influence of rainfall on propagated signal at different time exceedance percentages of an average year, over the climate zones of the country. Specifically, it demonstrates critical and non critical signal fade or signal outage time exceedance (0.001% to 1%) for Ku, K, and Ka-band systems in an average year. The study was carried out using meteorological data made available by the Nigerian Meteorological Agency (NiMet) over a period of 10 years (2009-2018). The four climate zones in the country were represented by five (5) locations; Maidugiri (warm desert climate), Sokoto (tropical dry climate), Port Harcourt (tropical monsoon climate), Abuja and Enugu (tropical savanna climate). The parameters were simulated into the International Telecommunications Union Recommended (ITU-R) models for rain attenuation over the tropics and results presented using MatLab and Origin Lab. Results of Ku band propagations showed that only locations in the tropical savanna and tropical monsoon climates experienced total signal outage for time percentage exceedance equal to or below 0.01% for both horizontal and vertical polarizations. At K band propagations, the five locations showed to have experienced signal outage at time exceedance equal to and below 0.01%, almost same was recorded for the Ka-band propagation. It was also observed that horizontal and vertical polarization of signal had slightly different rain attenuation values for the studied bands at the five locations, with horizontal polarization having higher values than vertical polarization.

• Journal of Environmental Policy
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• v.9 no.2
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• pp.157-184
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• 2010

Global climate change is destroying the water circulation balance by changing rates of precipitation, recharge and discharge, and evapotranspiration. The Intergovernmental Panel on Climate Change (IPCC 2007) makes "changes in rainfall pattern due to climate system changes and consequent shortage of available water resource" a high priority as the weakest part among the effects of human environment caused by future climate changes. Groundwater, which occupies a considerable portion of the world's water resources, is related to climate change via surface water such as rivers, lakes, and marshes, and "direct" interactions, being indirectly affected through recharge. Therefore, in order to quantify the effects of climate change on groundwater resources, it is necessary to not only predict the main variables of climate change but to also accurately predict the underground rainfall recharge quantity. In this paper, the authors selected a relevant climate change scenario, In this context, the authors selected A1B from the Special Report on Emission Scenario (SRES) which is distributed at Korea Meteorological Administration. By using data on temperature, rainfall, soil, and land use, the groundwater recharge rate for the research area was estimated by period and embodied as geographic information system (GIS). In order to calculate the groundwater recharge quantity, Visual HELP3 was used as main model for groundwater recharge, and the physical properties of weather, temperature, and soil layers were used as main input data. General changes to water circulation due to climate change have already been predicted. In order to systematically solve problems associated with how the groundwater resource circulation system should be reflected in future policies pertaining to groundwater resources, it may be urgent to recalculate the groundwater recharge quantity and consequent quantity for using via prediction of climate change in Korea in the future and then reflection of the results. The space-time calculation of changes to the groundwater recharge quantity in the study area may serve as a foundation to present additional measures for the improved management of domestic groundwater resources.

• The Journal of Dong Guk Oriental Medicine
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• v.8 no.2
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• pp.1-24
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• 2000

The comparison of climate's character of Yunqi(運氣) with the data of meterological observation were made in the research of climate. 1. The comparison of the average velocity of wind, temperature, rainfall, humidity of Seoul, by late 1954 to 1983, with Yunqi(運氣) was made. Fire-Chi(火氣) and moisture-qi(濕氣) were matched with the attribute of Taiyun(大運). Cold-qi(寒氣) was had some relationship. Dry-qi(燥 氣) and Wind-qi(風氣) were not matched. About the relationship of Spirit-of-official-sky(司天之氣) with climate, when the Moisture-soil(濕土) was added, they were matched and when the King-fire(君火) was added, they have some relationship. But Wind-tree(風木), Dry-metal(燥金), Buble-fire(相火), Cold-water(寒水) was added they were not matched. 2. According to the observation data of rainfall by late 180 years of Seoul; about Taiyun(大運), when the Water-Yun(水運) was greatly exceeded and Fire-Yun(火運) was shorted, in the case of Official-sky(司天), when Wind-Tree(風木) was added, the frequency was highly. So when the Soil-Yun(土運) was greatly exceeded and when Official-sky(司天)was added to the Moisture-soil(濕土), the rainfall was not matched. 3. The relationship of the frequency of the abnormal climate occurrences between Yunqi-promotion-weak(運氣盛衰)and Yunqi-Harmony(運氣同化) and Yunqi-soft-attacking(運氣順逆) in the weather of Korean Peninsula was compared by 1564 to 1863. They were not matched except the case of Yunqi-Harmony(運氣同化). 4. There were some cases which were not matched exactly between the climate predicted by the theory and real climate in 1984, the year of Kap-ga(甲子年). But many correspondence between the observation by the office of meteorology and the prediction by the analysis from Yun-qi-sang-hab(運氣相合) theory. 5. Because meterological phenomena of real world and analysis from the hypothesis of Yunqi(運氣) have no relationship with each other, some of Doctor denied Yunqi(運氣) in the way of matching mechanically. But the thought of Doctor who denied Fortune-spirit(運氣) made promotion for the theory of divination by bringing deeper insight. And it was not only the negative side. 6. In the point of geographical difference, the climate of China, the origination Yunqi theory, is different from the Korea's. Thus some observation errors should be considered. From the basis of this thesis, I hope that the deeper advance would be made into the Korean Yunqi theory.

• Journal of Korean Society of Forest Science
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• v.89 no.3
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• pp.323-334
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• 2000

This study was carried out to investigate the effects of geological and topographical features on landslide and land-creep at the twenty four surveyed sites of Kyungpook province. According to the results obtained, it was concluded that continuous heavy rainfall was one of the primary factors to occur landslide and land-creep. Most of the landslides occurred in the past were concentrated in the granite and granitic gneiss zones, while land-creeps were mainly occurred in the mud-stone zones. Therefore, it was thought that the physical properties such as soil texture, solid phase, moisture contents, density, hardness and porosity rate of weathered granite and granitic gneiss could affect the occurrence of landslide and land-creep. Due to the holding of sand contents in the upper soil layers of weathered granite and granitic gneiss, rainfall could infiltrate into the soil easily. While lower soil layers contained much quantity of clay and silt contents, those soils saturated with rainfall cause to lose viscosity and shear strength. Therefore, it was seemed that landslide was occurred more easily and the saturation of those soils was made much easily by bed rocks under those soils. Landslide and land-creep are slided into lower place by gravitation and slope degree factors. Therefore, prediction of landslide occurrence is very difficult because landslide is occurred abruptly, and physical properties of the soil have to be understood and checking the existence of bed rocks under the soils is not easy, on the other hand, land-creep is progressed very slowly. Therefore, it was suggested that in a degree creeping could be protected by removing of several causing factors.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.919-928
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• 2023

Rainfall characteristics in Korea are concentrated during the summer flood season. In particular, when a large amount of turbid water flows into the dam due to the increasing trend of concentrated rainfall due to abnormal rainfall and abnormal weather conditions, prolonged turbid water phenomenon occurs due to the overturning phenomenon. Much research is being conducted on turbid water prediction to solve these problems. To predict turbid water, turbid water data from the upstream inflow is required, but spatial and temporal data resolution is currently insufficient. To improve temporal resolution, the development of the Turbidity-SS conversion equation is necessary, and to improve spatial resolution, multi-item water quality measurement instrument (YSI), Laser In-Situ Scattering and Transmissometry (LISST), and hyperspectral sensors are needed. Sensor-based measurement can improve the spatial resolution of turbid water by measuring line and surface unit data. In addition, in the case of LISST-200X, it is possible to collect data on particle size, etc., so it can be used in the Turbidity-SS conversion equation for fraction (Clay: Silt: Sand). In addition, among recent remote sensing methods, the spatial distribution of turbid water can be presented when using UAVs with higher spatial and temporal resolutions than other payloads and hyperspectral sensors with high spectral and radiometric resolutions. Therefore, in this study, the Turbidity-SS conversion equation was calculated according to the fraction through laboratory analysis using LISST-200X and YSI-EXO, and sensor-based field measurements including UAV (Matrice 600) and hyperspectral sensor (microHSI 410 SHARK) were used. Through this, the spatial distribution of turbidity and suspended sediment concentration, and the turbidity calculated using the Turbidity-SS conversion equation based on the measured suspended sediment concentration, was presented. Through this, we attempted to review the applicability of the Turbidity-SS conversion equation and understand the current status of turbid water occurrence.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.262-276
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• 2018

Models to predict Leaf Wetness Duration (LWD) were evaluated using the observed meteorological and dew data at the 11 citrus orchards in Jeju, South Korea from 2016 to 2017. The sensitivity and the prediction accuracy were evaluated with four models (i.e., Number of Hours of Relative Humidity (NHRH), Classification And Regression Tree/Stepwise Linear Discriminant (CART/SLD), Penman-Monteith (PM), Deep-learning Neural Network (DNN)). The sensitivity of models was evaluated with rainfall and seasonal changes. When the data in rainy days were excluded from the whole data set, the LWD models had smaller average error (Root Mean Square Error (RMSE) about 1.5hours). The seasonal error of the DNN model had the similar magnitude (RMSE about 3 hours) among all seasons excluding winter. The other models had the greatest error in summer (RMSE about 9.6 hours) and the lowest error in winter (RMSE about 3.3 hours). These models were also evaluated by the statistical error analysis method and the regression analysis method of mean squared deviation. The DNN model had the best performance by statistical error whereas the CART/SLD model had the worst prediction accuracy. The Mean Square Deviation (MSD) is a method of analyzing the linearity of a model with three components: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). Better model performance was determined by lower SB and LC and higher NU. The results of MSD analysis indicated that the DNN model would provide the best performance and followed by the PM, the NHRH and the CART/SLD in order. This result suggested that the machine learning model would be useful to improve the accuracy of agricultural information using meteorological data.

• Journal of Wetlands Research
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• v.19 no.3
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• pp.345-352
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• 2017

The average global temperature on Earth has increased by about $0.85^{\circ}C$ since 1880 due to the global warming. The temperature increase affects hydrologic phenomenon and so the world has been suffered from natural disasters such as floods and droughts. Therefore, especially, in the aspect of water deficit, we may require the accurate prediction of water demand considering the uncertainty of climate in order to establish water resources planning and to ensure safe water supply for the future. To do this, the study evaluated future water balance and water deficit under the climate change for Anseong river basin in Korea. The future rainfall was simulated using RCP 8.5 climate change scenario and the runoff was estimated through the SLURP model which is a semi-distributed rainfall-runoff model for the basin. Scenario and network for the water balance analysis in sub-basins of Anseong river basin were established through K-WEAP model. And the water demand for the future was estimated by the linear regression equation using amounts of water uses(domestic water use, industrial water use, and agricultural water use) calculated by historical data (1965 to 2011). As the result of water balance analysis, we confirmed that the domestic and industrial water uses will be increased in the future because of population growth, rapid urbanization, and climate change due to global warming. However, the agricultural water use will be gradually decreased. Totally, we had shown that the water deficit problem will be critical in the future in Anseong river basin. Therefore, as the case study, we suggested two alternatives of pumping station construction and restriction of water use for solving the water deficit problem in the basin.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.