• Journal of Korean Society of Water and Wastewater
• /
• v.14 no.3
• /
• pp.281-290
• /
• 2000

This study investigates the prediction of daily water supply, which is a necessary for the efficient management of water distribution system. ANFIS, namely artificial intelligence, is a neural network into which fuzzy information is inputted and then processed. In this study, daily water supply was predicted through an application of network-based fuzzy inference system(ANFIS) for daily water supply prediction. This study was investigated methods for predicting water supply based on data about the amount of water which supplied in Kwangju city. For variables choice, four analyses of input data were conducted: correlation analysis, autocorrelation analysis, partial autocorrelation analysis, and cross-correlation analysis. Input variables were (a) the amount of water supply, (b) the mean temperature, and (c) the population of the area supplied with water. Variables were combined in an integrated model. Data of the amount of daily water supply only was modelled and its validity was verified in the case that the meteorological office of weather forecast is not always reliable. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 18.46% and the average error was lower than 2.36%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.

• Journal of agriculture & life science
• /
• v.53 no.2
• /
• pp.121-129
• /
• 2019

This study carried out to determine control factors for the improvement of productivity of laying hens suffering heat stress during hot weather. A total of 48,451 ISA Brown layers were housed in a farm located in Gyeongsangnam-do, Republic of Korea. Five thermo-hydrometer loggers were installed inside the house to collect data of dry-bulb temperature and relative humidity. The experiment continued for 81 days when the summer season begins from 19th June to 7th September, 2018. This study analyzed the correlations among layers' production index and daily average, highest, and lowest temperature; daily average, highest, and lowest relative humidity; and daily average, minimum, and maximum THI. The result indicated that feed consumption, hen-day egg production, egg weight, and FCR decreased as the daily average, highest and lowest dry-bulb temperature and THI rise (p<0.01). On the other hand, water intake increased as the daily average, highest and lowest dry-bulb temperature and THI rise (p<0.001). The relative humidity was not considered to have direct correlations to the layers' production index (p>0.05). However, it was noticeable that the mortality did not have significant relations with daily average and highest temperature; THI; or daily average, highest and lowest relative humidity while it was relevant to the daily lowest temperature and THI (p<0.05). In conclusion, to enhance the productivity of laying hens in a hot climate, it is recommended that daily average, highest, and lowest dry-bulb temperature and THI are maintained as low as possible. Especially, the daily lowest temperature is needed to lower to 20℃, which is the lowest critical temperature for layers.

• Korean Journal of Remote Sensing
• /
• v.9 no.1
• /
• pp.1-19
• /
• 1993

A simple bispectral threshold technique which reflects the temporal and spatial characteristics of the analysis area has been developed to classify the cloud type and estimate the cloud cover from GMS/S-VISSR(Stretched Visible and Infrared Spin Scan Radiometer) imagery. In this research, we divided the analysis area into land and sea to consider their different optical properties and used the same time observation data to exclude the solar zenith angle effects included in the raw data. Statistical clear sky radiance(CSRs) was constructed using maximum brightness temperature and minimum albedo from the S-VISSR imagery data during consecutive two weeks. The CSR used in the cloud anaysis was updated on the daily basis by using CSRs, the standard deviation of CSRs and present raw data to reflect the daily variation of temperature. Thresholds were applied to classify the cloud type and estimate the cloud cover from GMS/S-VISST imagery. We used a different thresholds according to the earth surface type and the thresholds were enough to resolve the spatial variation of brightness temperature and the noise in raw data. To classify the ambiguous pixels, we used the time series of 2-D histogram and local standard deviation, and the results showed a little improvements. Visual comparisons among the present research results, KMA's manual analysis and observed sea level charts showed a good agreement in quality.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.159-168
• /
• 2022

The recent outbreak of COVID-19 pandemic cases around the globe has affected Saudi Arabia with around 15, 00,000 confirmed cases within the initial 4 months of transmission. The present investigation analyzed the relationship between daily COVID-19 confirmed cases and meteorological parameters in seventeen cities of KSA. We used secondary published data from the Ministry of Health, KSA daily dataset of COVID-19 confirmed case counts. The meteorological parameters used in the present investigation are temperature, humidity, dew point, and wind speed. Pearson correlation and Spearman rank correlation tests were utilized for data analysis. The incubation period of COVID-19 varies from 1 day to 14 days as per available information. Therefore, an attempt has been made to analyze the effects of meteorological factors with bins of 1, 3, 7, and 14 days. The results suggested that the highest number of correlations (15 cities) was observed for temperature (maximum, minimum, and average) and humidity (12 cities) (minimum and average). The dew point showed relationships for 7 cities and wind showed moderate correlations only for 2 cities. The study results might be useful for authorities and stakeholders in taking specific measures to combat the Covid-19 pandemic.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.183-185
• /
• 2015

Recently, big data analysis based on machine learning has gained popularity and many machine learning techniques have been applied to the field of agriculture. By using machine learning technique to analyze huge number of samples of biological and environmental data, new observations can be found. In this research, we consider the estimation of proportional daily weight increase (PDWI) based on measurement data from experimental swine farm. In order to derive the exact formulation for PDWI estimation, we have used measured value of mean, daily maximum, daily minimum of temperature, humidity, CO2, wind speed and measured PDWI values. Based on collected data, we have derived equation for PDWI estimation using tree-based algorithm. In the derived formulation, we have found that the daily average temperature is the most dominant factor that affects PDWI. Our results can be applied to pig farms to estimate the PDWI of swine.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.6 no.3
• /
• pp.177-182
• /
• 2004

A chill unit has been used to estimate chilling requirement for dormancy release and risk of freezing damage. A system that calculates chill units was developed to obtain site-specific estimates of dormancy release date for grapes and evaluated in Baekgu myun near Kimje City, Chunbuk, Korea from September 2002 to March 2003. The system utilized daily minimum and maximum temperature maps generated from spatial interpolation with temperature correction for topography. Hourly temperature was temporally interpolated from the daily data using a sine-exponential equation (Patron and Logan, 1981). Hourly chill units were determined from sigmoid, reverse sigmoid, and negatively increasing sigmoid functions based on temperature ranges and summed for 24 h. Cumulative daily chill units obtained from measurements did not increase until 20 October 2002, which was used as a start date for accumulation to estimate the dormancy release date. As a result, a map of dormancy release date in the study area was generated, assuming 800 chill units as a threshold for the chilling requirement. The chill unit accumulation system, implemented using Microsoft Visual Basic and C++ (Microsoft, Redmond, WA, USA), runs in the Windows environment with ArcView (ESRl Inc., Redlands, CA, USA).

• Journal of Animal Environmental Science
• /
• v.3 no.2
• /
• pp.77-85
• /
• 1997

Recently, local swine producers are rapidly adopting the indoor production system which developed in foreign countries. However, this imported system is reported as not functioning properly because of different climate conditions. The objective of this project was to investigate the environment characteristics of a windowless delivery swine building. The parameters studied were the heating and cooling loads, the daily changes of indoor temperature and relative humidity, the horizontal and the vertical distributions of indoor temperature, and the effect of mist cooling on indoor temperature. From this study, the following are founded : 1. The maximum cooling and heating loads were - 317.0kcal/㎡$.$h and 336.5kal/㎡$.$h in summer and in winter. The large loads seems to be on account of inappropriate operations of ventilating fans. 2. The daily variations of relative humidity in indoor were smaller than those in outside. Those values both in summer and in winter as relative humidities in door was lower than optimum for growing pigs, the additional humidifier might be helpful to increase the relative humidity in indoor. 3. The horizontal distribution of the indoor temperature was found to be uniform in the variation range of 1$^{\circ}C$. 4. The vertical distribution of the indoor temperature was not found to be uniform; the temperature of upper part was higher than that of slot part. 5. Average values of indoor temperature became lower by 3$^{\circ}C$ by mist cooling. But the variation of temperature was found to be larger; The middle part of the room was cooled down, but the corner part of the room was not affected by misting due to uneven nozzle configuration.

• Korean Journal of Environmental Agriculture
• /
• v.27 no.4
• /
• pp.441-446
• /
• 2008

This study was conducted to determine the effect of heat stress on laying hen performance during summer. A total of 180 Hyline-brown layers, 98 days of age, were housed in a enclosed house. The daily maximum temperature in the house was ranged $26{\sim}36^{\circ}C$. The egg production was markedly fallen than other days when daily maximum temperature in the layer house became more than $33^{\circ}C$. As water intake and feed intake increased to $490\;mL\;bird^{-1}\;day^{-1}$ and $240\;g\;bird^{-1}\;day^{-1}$, the egg production soared. But it was hardly increased more than them. Based on these results, layer house roof should be amply insulated to improve the egg production of layers. If not insulated, the shade curtain should be installed above roof and cool water sprayed before and after 2 p.m. And layers should be provided cool drinking water of about $15^{\circ}C$ in the day time.

• Atmosphere
• /
• v.29 no.5
• /
• pp.671-687
• /
• 2019

This study evaluates the long-term seasonal predictability of summer (June, July and August) heatwaves over South Korea using 30-year (1989~2018) Hindcast data of the Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. Heatwave indices such as Number of Heatwave days (HWD), Heatwave Intensity (HWI) and Heatwave Warning (HWW) are used to explore the long-term seasonal predictability of heatwaves. The prediction skills for HWD, HWI, and HWW are evaluated in terms of the Temporal Correlation Coefficient (TCC), Root Mean Square Error (RMSE) and Skill Scores such as Heidke Skill Score (HSS) and Hit Rate (HR). The spatial distributions of daily maximum temperature simulated by WRF are similar overall to those simulated by NCEP-R2 and PNU CGCM. The WRF tends to underestimate the daily maximum temperature than observation because the lateral boundary condition of WRF is PNU CGCM. According to TCC, RMSE and Skill Score, the predictability of daily maximum temperature is higher in the predictions that start from the February and April initial condition. However, the PNU CGCM-WRF chain tends to overestimate HWD, HWI and HWW compared to observations. The TCCs for heatwave indices range from 0.02 to 0.31. The RMSE, HR and HSS values are in the range of 7.73 to 8.73, 0.01 to 0.09 and 0.34 to 0.39, respectively. In general, the prediction skill of the PNU CGCM-WRF chain for heatwave indices is highest in the predictions that start from the February and April initial condition and is lower in the predictions that start from January and March. According to TCC, RMSE and Skill Score, the predictability is more influenced by lead time than by the effects of topography and/or terrain feature because both HSS and HR varies in different leads over the whole region of South Korea.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.53 no.1
• /
• pp.42-49
• /
• 2008

GDD models of corn were developed in bare soil, while sweet and super sweet corns are grown under black polyethylene (P. E.) film mulch in Korea. To develop a suitable GDD model under black P. E. film mulch, a super sweet com hybrid "Cambella-90" was planted from 1 April to 30 June in 2003 at the 10-day intervals under black P. E. film mulch and in bare soil. In bare soil the best GDD model was $GDD\;=\;{\sum}[H"+L')/2\;-\;10^{\circ}C]$, where H" was daily maximum temperature but is was substituted for $30^{\circ}C$ - (daily maximum temperature - $30^{\circ}C$) when higher than $30^{\circ}C$ and L' was daily minimum temperature, but it was substituted for $10^{\circ}C$ when lower than $10^{\circ}C$. The same GDD model could be adapted for com grown under black P. E. film mulch, but base temperature was substituted for $9^{\circ}C$. To determine planting date for the scheduled harvests, accumulated GDDs were calculated using 30-year average temperature data during the growing season. Under black P. E. film mulch planting dates were determined by subtracting GDD of the hybrid, $970^{\circ}C$, from accumulated GDD of scheduled harvest dates.