EVALUATION OF RIVERINE WATER TEMPERATURE IN A-CLASS RIVERS IN ARIAKE BAY
BASIN BY ARTIFICIAL INTELLIGENCENANAKO HARAGUCHI, HAO LIN and SHINICHIRO YANO (Kyushu University)

Introduction

Results and Discussion

In recent years, there have been many phenomena in which environmental
anomalies due to climate change are suspected. With the progress of global
warming, also there is a concern about the rise in riverine water temperature inaddition to air temperature (Shiroiwa et al., 2006; Miyamoto et al., 2010). It is
expected that rising riverine water temperature may affect seawater temperature ina region of freshwater influence (ROFI), but few studies have focused on this. In thisstudy, we focused on evaluating the effects of other significant factors than air
temperature on riverine water temperature. We attempted to develop a noble
riverine water temperature evaluation model using the artificial intelligence (AI)technique.

Figures 2 to 4 show the time series ofpredicted values and measured values(learning data) in May, 2016 in case thatthe NNW model trained only for 11
months excluding a month of May.
✓Figure 2The daily fluctuation is very large(correlation factor R=0.538, root meansquare error (RMSE) value: 2.837).
✓Figure 3
It shows the case that temporal and spatial
elements are added. The learning data
were expanded not only in thedownstream area but also in the upstream
area and the middle. The daily fluctuation
became to be smaller (R=0.759 and RMSE:4.170). However, larger difference of thedaily averaged value can be seen.
✓Figure 4
It shows the case adding the time
information as a variable. The daily
fluctuation was smaller by adding the time,
but R=0.524 and the RMSE was 5.746.

Methodology
The target rivers in the present study are the sevenclass A rivers flowing into the Ariake Sea. (see Fig.1).
Analysis was performed using hourly data of air
temperature, river flow discharge, precipitation, andglobal radiation as learning data.
Using the neural network (NNW) function (neuralnetin the neuralnet package) of the statistical analysis
software R (Ver.3.5.1), the riverine water temperature
was used as the target variable, and the airtemperature, river flow discharge, and global solarradiation were explanatory variables. The training
data was the data for one year of 2016.

Figure 1. Target rivers andwater temperature measurement
stations

Conclusions✓Some explanatory variables were added to suppress diurnal variation. The daily
fluctuation has decreased, but the prediction accuracy is poor.
✓In future studies, we must continue to adjust the explanatory variables and use
other types of NNWs that are better suited for time series data to improve accuracy.
References:
Shiroiwa, J., et al.(2006). Influence of water temperature of river by climate change, J. of JSCE, 50.
Miyamoto, H., et al.(2010). An impact analysis of climate change on stream temperatures in a river
basin, Ann. J. of Hyd. Eng., JSCE, 54.

✓ In both of modifications, the daily
fluctuation decreased, but the error
increased, and the reproducibilitydecreased.
✓From these results, we can understandweather condition at the lower area andriver discharge are significant factors fordaily averaged river water temperature at
the lowest reach, but daily variation
cannot be produced from only thesefactors.

Figure 2. Time series of predicted and measured valuesin May, 2016. (Variables: Temperature, precipitation,river flow, global solar radiation)

Figure 3. Time series of measured and predicted values
in May, 2016. (Including variables for upstream, middle
and downstream areas)

Figure 4. Time series of measured and predicted values
in May, 2016. (the time was added as a variable)