• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1029-1037
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• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• Journal of the Korean GEO-environmental Society
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• v.3 no.4
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• pp.11-17
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• 2002

The purpose of this study is the prediction of pollutant loads into Seomjin river watershed using neural networks model. The pollutant loads into river watershed depend upon the water quantity of inflow from the upstream as well as the water quality of the inflow into the river. For the estimation of pollutants into river, a neural networks model which has the features of multi-layered structure and parallel multi-connections is used. The used water quality parameters are BOD, COD and SS into Seomjin river. The results of calibration are satisfactory, and proved the availability of a proposed neural networks model to estimate short-term water quality pollutants into river system.

• Journal of KIISE
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• v.44 no.6
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.

• Journal of Korea Water Resources Association
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• v.46 no.7
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• pp.697-706
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• 2013

In recent, Korea has faced on water quality management problems in reservoir and river because of increasing water temperature and rainfall frequency caused by climate change. This study is effectively to manage water quality for establishment of algal bloom forecasting models with artificial neural network. Daecheong reservoir located in Geum river has suitable environment for algal bloom because it has lots of contaminants that are flowed by rainfall. By using back propagation algorithm of artificial neural networks (ANNs), a model has been built to forecast the algal bloom over short-term (1, 3, and 7 days). In the model, input factors considered the hydrologic and water quality factors in Daecheong reservoir were analyzed by cross correlation method. Through carrying out the analysis, input factors were selected for algal bloom forecasting model. As a result of this research, the short term algal bloom forecasting models showed minor errors in the prediction of the 1 day and the 3 days. Therefore, the models will be very useful and promising to control the water quality in various rivers.

• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.67-75
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• 2004

In this study, real-time forecasting model(Neural Dam Inflow Forecasting Model; NDIFM) based on neural network to predict the dam inflow which is occurred by flood runoff is developed and applied to check its availability for the operation of multi-purpose reservoir Developed model Is applied to predict the flood Inflow on dam Nam-Gang in Nak-dong river basin where the rate of flood control dependent on reservoir operation is high. The input data for this model are average rainfall data composed of mean areal rainfall of upstream basin from dam location, observed inflow data, and predicted inflow data. As a result of the simulation for flood inflow forecasting, it is found that NDIFM-I is the best predictive model for real-time operation. In addition, the results of forecasting used on NDIFM-II and NDIFM-III are not bad and these models showed wide range of applicability for real-time forecasting. Consequently, if the quality of observed hydrological data is improved, it is expected that the neural network model which is black-box model can be utilized for real-time flood forecasting rather than conceptual models of which physical parameter is complex.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.393-402
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• 2023

Drought, which has been increasing in frequency and magnitude due to recent abnormal weather events, poses severe challenges in various sectors. To address this issue, it is important to develop technologies for drought monitoring, forecasting, and response in order to implement effective measures and safeguard the ecological health of aquatic systems during water scarcity caused by drought. This study aimed to predict water quality fluctuations during drought periods by integrating the watershed model HSPF and the water quality model QUAL-MEV. The researchers examined the SPI and RCP 4.5 scenarios, and analyzed water quality changes based on flow rates by simulating them using the HSPF and QUAL-MEV models. The study found a strong correlation between water flow and water quality during the low flow. However, the relationship between precipitation and water quality was deemed insignificant. Moreover, the flow rate and SPI6 exhibited different trends. It was observed that the relationship with the mid- to long-term drought index was not significant when predicting changes in water quality influenced by drought. Therefore, to accurately assess the impact of drought on water quality, it is necessary to employ a short-term drought index and develop an evaluation method that considers fluctuations in flow.

• Journal of Korea Water Resources Association
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• v.45 no.7
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• pp.729-741
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• 2012

Among the methods of precipitation data acquisition, a rain gauge station has a distinctive advantage of direct measurement of rainfall itself, but multiple stations should be installed in order to obtain areal precipitation data required for hydrological analysis. On the other hand, a rainfall radar may provide areal distribution of rainfall in real time though it is an indirect measurement of radar echoes on rain drops. Rainfall radars have been shown useful especially for forecasting short-term localized torrential storms that may cause catastrophic flash floods. CAPPI (Constant Altitude Plan Position Indicator), which is one of the several types of radar rainfall image data, has been provided on the Internet in real time by Korea Meteorological Administration (KMA). It is one of the most widely available rainfall data in Korea with fairly high level of confidence as it is produced with bias adjustment and quality control procedures by KMA. The objective of this study is to develop an improved way to extract quantitative rainfall data applicable to even very small watersheds from CAPPI using CIVCOM, which is a new image processing method based on a vector-based scheme proposed in this study rather than raster-based schemes proposed by other researchers. This study shows usefulness of CIVCOM through comparison of rainfall data produced by image processing methods including traditional raster-based schemes and a newly proposed vector-based one.