• Journal of Environmental Science International
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• v.26 no.10
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• pp.1125-1133
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• 2017

In this study a method for filling in missing data of river water temperature using a pre-constructed mathematical relationship between air and water temperatures is presented. A regression between water temperatures at individual stations and ambient air temperatures at nearby weather stations can provide a practical method for representing missing water temperature data for an entire region. Air and water temperature data that were collected from two test sites (one coastal and, one inland) were individually fitted to a nonlinear regression model. To consider seasonal hysteresis effects, separate functions were fitted to the data in the rising and falling limbs. A single-criterion, multi-parameter optimization technique was used to determine the optimal parameter sets. This method minimizes the differences between the time series of the measured and estimated data. The constructed air-water temperature relationship was subsequently applied to represent missing water temperature data. It was found that the RMSEs(MBEs) were in the range of $1.843-1.976^{\circ}C(-0.329-0.201^{\circ}C)$ and the coefficient of determination were in the range of 0.92-0.96. The results demonstrate that the predicted water temperatures using the regression equations were reasonably accurate.

• Computers and Concrete
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• v.8 no.3
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• pp.293-309
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• 2011

A long-term structural health monitoring (SHM) system comprising over 700 sensors of sixteen types has been implemented on the Guangzhou Television and Sightseeing Tower (GTST) of 610 m high for real-time monitoring of the structure at both construction and service stages. As part of this sophisticated SHM system, 48 temperature sensors have been deployed at 12 cross-sections of the reinforced concrete inner structure of the GTST to provide on-line monitoring via a wireless data transmission system. In this paper, the differential temperature profiles in the reinforced concrete inner structure of the GTST, which are mainly caused by solar radiation, are recognized from the monitoring data with the purpose of understanding the temperature-induced structural internal forces and deformations. After a careful examination of the pre-classified temperature measurement data obtained under sunny days and non-sunny days, common characteristic of the daily temperature variation is observed from the data acquired in sunny days. Making use of 60-day temperature measurement data obtained in sunny days, statistical patterns of the daily rising temperature and daily descending temperature are synthesized, and temperature distribution models of the reinforced concrete inner structure of the GTST are formulated using linear regression analysis. The developed monitoring-based temperature distribution models will serve as a reliable input for numerical prediction of the temperature-induced deformations and provide a robust basis to facilitate the design and construction of similar structures in consideration of thermal effects.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.337-340
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• 2007

Many researches conducted to investigate the relationship between surface emissivity and surface temperature in the past two decades and pointed out that the emissivity play a key role in applying remote sensing data to retrieve surface temperature. The task of surface temperature estimation is so important in many research fields, such as earth energy budgets, evapotranspiration, drought, global change and heat island effect. Therefore, it is indispensable to develop an effective and accurate technique to estimate the emissivity for accurate surface temperature estimations. This study developed an improved emissivity estimation technique for the use of surface temperature retrievals with MODIS data. The result of applying this improved technique using Band 31 of MODIS shows that the accuracy of estimated surface temperatures will be improved. This study also uses MODIS data observed in 2005 to establish the relationship between the surface emissivity correction factor and NDVI. Through the use of these correction factors, the land surface temperature can be retrieved more accurate with MODIS data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.392-393
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• 2005

Due to the temporal and spatial simultaneity and the high-frequency repetition, the data set retrieved from the satellite observation is considered to be the most desirable ones for the study of air-sea interaction. With rapidly developing sensor technology, satellite-retrieved data has experienced improvement in the accuracy and the number of parameters. Nevertheless, since it is still impossible to directly measure the heat fluxes between air and sea, the bulk method is an exclusive way for the evaluation of the heat fluxes at the sea surface. It was noted that the large deviation of air temperature in the winter season by the linear regression despite good correlation coefficients. We propose a new algorithm based on the Fourier series with which the SST and the air temperature. We found that the mean of air temperature is a function of the mean of SST with the monthly gradient of SST inferred from the latitudinal variation of SST and the spectral energy of air temperature is related linearly to that of SST. An algorithm to obtain the air temperature over the sea was completed with a proper analysis on the relation between of air temperature and of SST. This algorithm was examined by buoy data and therefore the air temperature over the sea can be retrieved based on just satellite data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.13-20
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• 2023

In this paper, we propose a water temperature prediction method using feature extraction and reconstructed data based on LSTM-Autoencoder. We used multivariate time series data such as sea surface water temperature in the Naksan area of the East Sea where the cold water zone phenomenon occurred, and wind direction and wind speed that affect water temperature. Using the LSTM-Autoencoder model, we used three types of data: feature data extracted through dimensionality reduction of the original data combined with multivariate data of the original data, reconstructed data, and original data. The three types of data were trained by the LSTM model to predict sea surface water temperature and evaluated the accuracy. As a result, the sea surface water temperature prediction accuracy using feature extraction of LSTM-Autoencoder confirmed the best performance with MAE 0.3652, RMSE 0.5604, MAPE 3.309%. The result of this study are expected to be able to prevent damage from natural disasters by improving the prediction accuracy of sea surface temperature changes rapidly such as the cold water zone.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.99-102
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• 2003

In this paper, we have developed a system for monitoring and processing the real time sensor data in remote site through Internet. For realizing this system, measurement equipment and protocol are used to transmit the measurement data to remote server and to process measurement data. In server part, the received data from remote site sensor is converted to text or graphic charts for user. The measurement device in sensor part receives the sensor data form sensor and store the received data to its internal memory for transmitting data to server part through Internet. Also the measurement device can receive data form server. The temperature sensor is corrected to the measurement device located in laboratory and the measurement device measures temperature of laboratory which can be confirmed by user through Internet. We have developed a server program working on the Linux to store measurement data from measurement device to server memory. The program is use for SNMP(Simple Network Management Protocol) to exchange data with measurement device. Also the program changes the measurement data into text and graphic charts for user display. The program is use apache PHP program for user display and inquiry. The real time temperature measurement system can be applly for many parts of industry and living.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.822-833
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• 2010

Temperature increase due to climate changes causes change of water temperature in rivers which results in change of water quality etc. and the change of river ecosystem has a great impact on human life. Analyzing the impact of current climate changes on air and water temperature is an important thing in adapting to the climate changes. This study examined the effect of climate changes through analyzing air temperature trend for Nakdong river basin and analyzed the elasticity of air-water temperature to understand the effect of climate changes on water temperature. For analysis air temperature trend, collecting air temperature data from the National Weather Service on main points in Nakdong river basin, and resampling them at the units of year, season and month, used as data for air temperature trend analysis. Analyzing for elasticity of air-water temperature, the data were collected by the Water Environment Information system for water temperature, while air temperature data were collected at the National Weather Service point nearest in the water temperature point. And using the results of trend analysis and elasticity analysis, the effect of climate changes on water temperature was examined estimating future water temperature in 20 years and 50 years after. It is judged that analysis on mutual impact between factors such as heat budget, precipitation and evapotranspiration on river water temperature affected by climate changes and river water temperature is necessary.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.8
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• pp.353-358
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• 2017

An electrical power utility, like an electrical power pole, includes various kinds of sensors for smart services. Temperature data is considered one of the important factors that can influence the smart operations of this utility. This study suggests a method for temperature data analysis for deciding the status of the smart electrical power utilities by using Kalman Filter and Ensemble Model. The suggested approach separates the temperature data according to the different positions of the temperature sensors of a utility, then uses Kalman Filter and Ensemble Model to analyse the characteristics of the temperature variation. With detailed processes, method explains the variation between an external temperature factor like weather temperature data and the sensed temperature data, and then, analysis the temperature data from each position of electrical power utilities. In this process, the suggested method uses Kalman Filter to remove error data and the ensemble model to find out mean value of every hour of electrical data. The result and discussion of temperature analysis were described clearly with the analysed results of electrical data. Finally, we were able to check the working condition of the power devices and the range of the temperature data foe each devices, which may help to indicate any causalities with respect to the devices in the utility pole.