Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

PROPOSAL AND VERIFICATION OF RIP CURRENT DETECTION USING AI
T. Ishikawa1, R. Shimada2, R. Sawagashira2 and T. Komine2
Research and Development Initiative, Chuo University, 1-13-27, Kasuga Bunkyo-ku, Tokyo
112-8551, Japan. ishikawa.195@g.chuo-u.ac.jp.
2
Faculty of Science and Engineering, Chuo University, 1-13-27, Kasuga Bunkyo-ku, Tokyo 1128551, Japan.
1

Number of P.A.

1. Introduction
There are from 2,000 to 3,000 rescues including those of unconscious people every year on thebeaches of Japan, as shown in Fig. 1. The occurrence of drowning accidents is mainly caused by
the rip current (Ishikawa et al., 2014), it accounts for 48 % of drowning accidents, as shown in Fig.
2. Also, in Australia, the United States and the United Kingdom, more than 50 % of rescue
accidents are caused by rip currents (Brighton et al., 2013). In order to reduce the rip current
accidents, beach users need to recognize rip currents, then they have to avoid them using risk
assessment. However, it is the difficulty of risk recognition and judgement under the momentarychange in natural phenomenon for beach users. Especially, almost all beach users understand therisk in the case of high wave conditions due to easy visual understanding, whereas they cannot
understand rip currents the same way. On the other hand, swimming areas along the shore are very
limited, however the number of lifeguards is small at around 1 lifeguard compared to the thousandsof beach users. In addition, beach users sometimes enter unpatrolled areas outside the swimmingareas. Therefore, we developed a new technology that can automatically detect the rip currents by
the Artificial Intelligence (AI), and notify beach users and lifeguards using the Internet of Things(IoT). In this study, we verified the accuracy of the rip current detection by the AI, using a fieldmeasurement, an image analysis and a numerical simulation. Also, we examined the log data of2019 that was actually operated.
5,000
4,000
3,000

Natural factors

2,000

Average; 2003-2019

1,000
0

Number of E.C.

Preventive Action; rescue of conscious people

50

Topography, 103 , 8%

98 99 00 01 02 030405 06 07 08 09 10 11 12 13 14 15 16 17 18 19

Emergency Care; rescue of unconscious people

Wind, 374 ,

40

30%

30
20

Rip
currents,
614 , 48%

Longshorecurrents,

100

Waves, 55 , 4%

126 , 10%98 99 00 01 02 030405 06 07 08 09 10 11 12 13 14 15 16 17 18 19

Figure 1. Number of Rescue from 1998 to 2019

Figure 2. Outbreak factors ofdrowning accidents

2. Summary of Rip Current Detection System using AI and IoTThis technology automatically detects the rip current by the analysis of the AI using the surface
image data of web cameras on the beach in real time. When rip currents are detected, an alert is
automatically provided to digital signage and beach user’s smartphone. It is effective for the risk
awareness of the beach user. Furthermore, if the beach user enters the rip current area, lifeguards
can take early action from the warning on their wearable device. Therefore, AI has 2 primaryPrimary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

functions which are rip currents detection and human detection (Fig. 3).
In order to realize this idea, we developed the system at the 1st study beach which was Onjuku
beach in 2018. The study beach is located in near Tokyo, and faces the Pacific Ocean. The
swimming area of the beach is about 300 m along the coast (Fig. 4), and the maximum number ofvisitors per day in recent years is about 10,000, and about 10 lifeguards are active. In addition, theaverage number of rescues on this beach is 145/year, and rip currents are over 50 % of the natural
factors of drowning accidents, because flash rips are generated on this beach (Fig. 5).
WebcamDigital signage

At time of occurrenceof Rip currents

Beach users

AI

Smartphone

Automatically provides the risk
Inform Risk information on
Digital Signage and Smart phone

Wearable device

Lifeguards
RESCUE

Beach user enters into
the Rip current area
Take photos of the surface image

Inform Rescue information on
Lifeguard’s Smartwatch

Lifeguards can take early action

Figure 3. System overview diagram of rip current detection system using AI and IoT
Cam 01

Cam 02
St.1

Cam 03

St.2

Photography area

Figure 4. Study beach and photographing ranges

Figure 5. Example of rip current situation

The AI model for the rip current detection was made by thorough deep learning together withfield observations on the study beach. In the deep learning, we collected 4 months of surface image
data by 3 webcams which were set on the beach. The Image data was at a continuous rate of 3 persecond. Next, we made 64,127 training data with different scale, horizontal rotation, hue,saturation, and lightness using the feature extraction from 100 annotation data of rip current image
to make the AI model. The feature extraction was set by processing the differences of 3 consecutive
images. Therefore, AI repeats the analysis at intervals of about 1 second. Figure 6 shows anexample of image data of Cam01. Because the Cam01 was set diagonally in back of the shoreline
and the height of the camera was low, above 4 m from the ground level, the rip current is shownat an oblique angle view in the image. However, the oblique angle view is limited to a rectangularshaped bounding box due to existing functions in the Annotation. For object detection algorithms,
the Tiny YOLO (Redmon and Farhadi, 2017) was used for the rip current detection by AI, and the
YOLO v3 (Redmon and Farhadi, 2018) was used for the human detection. At the stage of making
the AI model, the precision rate and the recall rate of the rip current detection by AI were 91.1 %and 86.6 % at the final point of the model evolution. Figure 7 shows an example of the result, ablue area of detection by AI coincides well with a red area of judgement by expert lifeguard.
Similarly, we could get a good result for human entrance. The results of precision and recall were96.5 % and 91.5 % respectively.

Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

Annotation area
Rip current

Red area; Annotation by expert lifeguardBlue area; Detection by AI

Figure 6. Example of image data of Cam 01

Figure 7. Example of comparison of rip
current detection area by annotation and AI

3. Method of Verification of Rip current detection by AI
The system was operated as a trial on the study beach on December 2018. Figure 8 shows aresult of incidence of rip currents every 20 minutes by AI detection for image of Cam 01 during
the verification period between 9 and 25 on Dec. 2018. For example, the incidence of rip current
detection was 65 % on 9 Dec., this means that rip currents were detected in 2,340 of 3,600 photos
which were taken during the 20 min. In order to verified the accuracy of the rip current detection
by AI, we investigated the accuracy for 49 cases with the incidence of 10 % or more and 165 caseswith the incidence of 0 % which means a no rip current situation by a field measurement, an image
analysis and a numerical simulation. We actually visually confirmed the presence or absence of rip
currents in the images of each case, but used these methods as an objective evaluation. Figure 9shows an example of the image of rip current detection by AI.

70%
60%
50%
40%
30%
20%
10%
0%

Incidence of rip currents by AI detection

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Dec.

Figure 8. Incidence of rip currents on every 20 Figure 9. Example of the image of rip current
minutes by AI detection for Cam 01
detection by AI (15:17, 19 December 2018)
In the field measurement, we set 2 supersonic wave gages at 2 and 7 m depth in thephotography area of web cams. The wave gage of the St. 2 should have been set in shallow water
to measure nearshore currents, but it was set at a depth of 2 m to avoid contact with surfers (Fig.
4, 10). Figure 11 shows the waves at St.2 during the observation period. The maximum significant
wave was H1/3 = 1.67 m and T1/3 = 6.43 s at 18:40 on 19 December, and the number of waves
exceeding H1/3 = 1 m was large. The energy mean wave during this period was H = 0.59 m and T= 7.23 s, and there were relatively calm conditions where it was possible to swim in the sea on the
Onjuku beach facing the Pacific Ocean. Also, we carried out the rip currents survey using colordye during this period. The rip current was confirmed around 10:00 on 10 December as shown in
Figure 12. Figure 13 shows the results of waves and currents during color dye survey at St. 2. The
rip current directions from 135° to 225° were observed between 9:40 and 10:20. In the verification,
we confirmed the rip current direction was observed at St.2 at the time of the rip current detection
by AI.
In the image analysis, we applied this method which was proposed to observe water depth inPrimary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

the surf zone, and the formation of a bar and trough by Lippmann and Holman (1989). Figure 14
shows an example of the result which was made at an averaged image of approximately 550 images
every 5 minutes using image data of Cam 01. The rip current can be confirmed in the dark coloredarea with an oblique angle (a yellow dotted line of Fig. 14(a)), because the waves are hard to breakin the rip current area. However, the dark colored area cannot be confirmed in the no rip current
situation (Fig. 14(b)). Therefore, if the averaged image has the dark colored area that crosses thesurf zone, we evaluated it as "rip current occurred". On the other hand, when there was not any
dark colored area and the color was uniform, we evaluated it as "no rip current occurred".
6

0

1/100

-2
-4

St. 1

1/30

-6
-8

-10

100

200

300

Y (m)

400

500

1/3
H1/3

Tmax
max

T1/3
1/3

15

4

10

3

5

2

0

1

-5

0

0

Hmax
max

600

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Dec.

T (s)

St. 2

2

St. 2

5

H (m)

Z(m) above MWL

4-10

Figure 10. Longitudinal profile and position of Figure 11. Field measurement results of waves
wave gages
at St. 2

135°

St.2

225°

Rip current
direction

0

Figure 12. Situation of color dye survey

20 m

1/3
Wave Height H1/3(m)1/3Wave Period T1/3(s)102T1/3= 7.9 - 8.5 s

51

H1/3= 0.31 > 0.34 m, WD= 158.6 > 159.4o00

Direction (°)

10:05-10:10:30 (5’30”) on Dec. 10
Offshore distance; 50-100 m
Width; 5-10 m
Current Velocity; 0.1-0.15 m/s

St. 2

0.05

0 1 2 3 4 Eastward
5 6 7 Velocity
8 9 10(m/s)
11 12 13 14 Northward
15 16 17 18 Velocity
19 20 21(m/s)
22 23 0

T (s)

St.1

Velocity (m/s)

Cam 01

H (m)

3

-5

0.00
-0.05
360
270
180
90
0

CV= 0.002 > 0.01 m/s

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Current
17 18 19
20 21 22 23 0
direction(°)
Rip current direction; 135-225o

CD= 140.2 - 226.4o

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0

Figure 13. Field measurement results of waves
and currents at St. 2 on 10 December(a) 15:10-15:15, 19 December 2018 (case 32) (b) 15:40-15:45, 21 December 2018 (case 36)
Figure 14. Example of the result of Image analysis in case 32 and 36
In the numerical simulation, the parabolic approximation to the elliptic mild-slope equation
was used for a calculation of wave field, and the two-dimensional horizontal flow equations wasused for a calculation of nearshore current (DHI, 2017). For the wave conditions, the observed

Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoringdata of St.1 corresponding to each case were used, and for the tide conditions, the observed tidelevel of the Mera observatory of the Japan Meteorological Agency was used. Table 1 shows thecalculation conditions. A validity of the calculation was confirmed by reproducing the occurrenceof rip current within the same area of the color dye survey. Figure 15 shows the reproduction
calculation result, the rip currents can be confirmed in the color dye survey area. Figure 16 is an
example of the calculation results, in case of occurrence of rip currents, the rip currents can be
confirmed in the photographing range of Cam 01. Even though the rip current was detected by AI
in this case, rip currents were not calculated in Figure 17. Therefore, if the rip currents were
calculated in the photographing range of Cam 01 as in Fig. 16, we evaluated it as "rip current
occurred". On the other hand, in case of the no calculated rip currents as in Fig. 17, we evaluatedit as "no rip current occurred".
In addition, the system using the developed AI model was actually operated on the study beachduring summer from 2019. Thus, we examined the log data of 2019, we especially noticed theactual rescue cases. The log data records information for identifying the rip current and humandetection time and the detection area such as the XY coordinates of the base point, width, andlength.
Area
Topography
Case
Waves
Tide
Mesh size
Wave field
Nearshore current

Table 1 Calculation conditions

Onjyuku Beach (2.4 km×1.2 km)
Observed at 2017
Reproduction; Color dye survey (9:40-10:40, 10 Dec. 2018)
Prediction; 46 cases of Rip current detection of 10% or more by AI
156 cases of No rip current detection by AI
Measured value at St.1
Reproduction; H= 0.34 m, T= 8.47 s, WD= 154.4°
Prediction; Measured value for each case
Measured value at Mera observatory
Reproduction; T.P.+0.24 m
Prediction; Measured value for each caseΔx = Δy = 2.5 m
Parabolic approximation to the elliptic mild-slope equation
γ1= 1, γ2= 0.8，Kn= 0.002
two-dimensional horizontal flow equations
ε = 0.0, n = 0.031, Δt = 0.5 s，600 steps

Figure 15. Reproduction calculation result for the color dye survey at 10:05 on 10 December

Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

Figure 16. Example of the calculation result in case 32 of 28% of rip current detection by AI at
15:00-15:20 on 19 December 2018

Figure 17. Example of the calculation result in case 36 of 39.7% of rip current detection by AI
at 15:40-16:00 on 21 December 2018
4. Result
Table 2 shows verification results of the rip current detection of the 49 cases. Only 2 cases
were able to confirm rip currents using all methods. 38 cases clearly had confirmed the dark coloredarea, and 5 cases had confirmed the dark colored area with part of the image in the image analysis. 31
cases had calculated predominant rip currents in the photographing range of Cam 01 in the
numerical simulation, and 5 cases had calculated rip currents in a part of the photographing range.
The accuracy rate of 88 % (43/49 cases) for the image analysis and 78 % (36/46 cases) for thenumerical simulation respectively were confirmed during the observation period. However, the rip
current direction was not observed in most cases by the field measurement. As for the reason, St.
2 was located offshore from the breaking zone during the observation period except during times
with high waves. Also, it may have been difficult to observe a changing rip current in only one
spot.
In the 165 cases in the no rip current situation, AI did not detect rip currents, but the occurrenceof rip currents was confirmed in some cases as show in Figure 18 and 19. As a result, 95 cases had

Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

not confirmed the rip currents, except for 9 cases that could not be confirmed due to the backlightin the image analysis. 149 cases had not calculated the rip currents in the photographing range of
Cam 01 in the numerical simulation. The accuracy rate of 61 % (95/156 cases) for the image
analysis and 90 % (149/165 cases) for the numerical simulation respectively were confirmed. This
study has made it clear that the rip currents detection by AI was generally correct, but not all rip
currents could be detected.
Table 2 Verification results of rip currents detection of 10 % or more by AI
Case m/d h:m-h:m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41

12/9 9:00-9:20
12/9 9:20-9:40
12/9 10:00-10:20
12/9 11:20-11:40
12/9 11:40-12:00
12/9 15:00-15:20
12/9 15:20-15:40
12/10 14:40-15:00
12/10 15:00-15:20
12/10 15:20-15:40
12/11 13:20-13:40
12/12 13:40-14:00
12/13 9:20-9:40
12/13 15:40-16:00
12/14 9:00-9:20
12/14 9:20-9:40
12/14 10:00-10:20
12/14 11:00-11:20
12/14 11:20-11:40
12/14 13:40-14:00
12/16 15:00-15:20
12/17 10:20-10:40
12/17 10:40-11:00
12/17 11:00-11:20
12/18 10:40-11:00
12/18 14:00-14:20
12/18 14:20-14:40
12/18 14:40-15:00
12/18 15:00-15:20
12/18 15:20-15:40
12/19 14:40-15:00
12/19 15:00-15:20
12/19 15:20-15:40
12/19 15:40-16:00
12/21 9:20-9:40
12/21 15:40-16:00
12/23 13:00-13:20
12/23 13:20-13:40
12/23 13:40-14:00
12/23 14:00-14:20
12/23 14:20-14:40

Wave conditions Incidence ofVerification results(St.1)
Rip current
H1/3 T1/3 WD by AI
Field measure Image
Numerical(%)(m) (s)(o )ment (St. 2)
analysis
simulation
1
―
―
―
63.6
―*
Confirmed
―*3
1
―
―
―
23.6
―*
Confirmed
―*3
1
―
―
―
12.5
―*
Confirmed―*3
1
0.54 8.1 164.6
22.3―*
Confirmed Calculated
0.53 8.1 165.4
28.0
Not
Confirmed Calculated
0.58 8.3 164.0
13.3
Not
Not
Not
0.54 7.9 162.2
10.4
Not
Confirmed Calculated
0.35 7.3 153.1
10.6
Not
Confirmed
Not
0.37 7.6 150.4
32.6
Not
Not
Not
0.39 7.5 151.1
12.7
Not
Confirmed*2 Calculated
0.46 7.7 150.3
28.4
Not
Confirmed Calculated
0.78 8.0 154.8
17.0
Not
Confirmed Calculated
0.38 7.9 153.1
29.5
Not
Confirmed Calculated
0.46 8.0 151.8
13.0
Not
Confirmed Calculated*4
0.66 6.9 164.2
65.8
Not
Confirmed Calculated*40.56 7.1 162.1
20.1
Not
Confirmed Calculated
0.58 7.5 163.7
12.9
Not
Not
Calculated
0.43 7.5 162.7
19.7
Not
Not
Calculated*4
0.39 7.1 167.0
10.3
Not
Confirmed Calculated*4
0.35 7.9 162.1
16.5
Not
Confirmed
Not
0.72 3.9 159.6
10.7
Not
Confirmed Calculated
0.65 6.0 159.0
15.1
Not
Confirmed Calculated
0.65 6.2 157.6
37.8
Not
Confirmed Calculated
0.65 6.1 158.7
13.2
Not
Confirmed Calculated
0.66 8.4 161.8
32.4
Not
Confirmed Calculated
0.59 7.7 165.2
33.2
Not
Confirmed Calculated
0.62 7.5 165.5
50.1
Observed
Confirmed Calculated
0.68 7.6 164.0
56.2
Observed
Confirmed Calculated
0.64 7.3 166.3
44.6
Not
Confirmed Calculated
0.64 7.5 166.4
14.8
Not
Confirmed Calculated
1.28 5.6 174.1
16.0
Not
Confirmed Calculated
1.26 5.8 173.0
27.9
Not
Confirmed Calculated
1.24 6.1 173.5
24.8
Not
Confirmed Calculated
1.34 6.3 173.8
11.0
Not
Confirmed Calculated
0.27 7.8 160.9
16.8
Not
Not
Calculated*4
0.33 7.4 157.2
39.7
Not
Not
Not
0.68 6.6 154.4
13.0
Not
Confirmed Calculated
0.72 6.6 153.8
16.8
Not
Confirmed Calculated
0.77 6.8 152.8
30.8
Not
Confirmed
Not0.74 6.4 152.3
11.1
Not
Confirmed
Not0.73 6.2 150.7
27.2
Not
Confirmed
NotPrimary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

Wave conditions Incidence ofVerification results(St.1)
Rip current
Case m/d h:m-h:m
H1/3 T1/3 WD by AI
Field measure Image
Numerical(%)(m) (s)(o )ment (St. 2)
analysis
simulation
42 12/23 14:40-15:00 0.71 6.3 157.4
13.8
Not
Confirmed
Not
43 12/23 15:00-15:20 0.77 6.3 156.2
15.2
Not
Confirmed Calculated
44 12/23 15:20-15:40 0.72 6.3 155.8
19.8
Not
Confirmed
Not
45 12/24 9:00-9:20
0.49 7.7 163.4
29.7
Not
Confirmed Calculated
46 12/24 9:20-9:40
0.50 7.5 164.7
22.2
Not
Confirmed*2 Calculated
47 12/24 10:20-10:40 0.40 7.4 164.5
18.3
Not
Confirmed*2 Calculated
48 12/24 14:20-14:40 0.49 8.3 165.6
10.0
Not
Confirmed*2 Calculated
49 12/24 14:40-15:00 0.54 8.1 167.0
11.5
Not
Confirmed*2 Calculated
1* No data for St. 2 (before observation started of St. 2)*2 Difficult to confirm due to backlight, but a dark colored area was confirmed in a part of the image.
*3 No data for St. 1 (before observation started of St. 1)*4 Predominant rip currents were not calculated, but the rip currents in a part of thephotographing range of Cam 01 were calculated.

Figure 18. Example of the image analysis result of no rip current detection by AI at 11:4011:45 on 19 December 2018

Figure 19. Example of the calculation result of no rip current detection by AI at 11:40-12:00
on 19 December 2018
The system using the developed AI model was actually operated on the study beach during
summer from 2019. As a result of the hearing surveys with lifeguards (n = 20) in 2019, the rip
current detection of 65 % and the human detection of 80 % were mostly correct. The total number

Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoringof rip current alerts by the system were 2,425 for a total of 51 days from July to August 2019. In
fact, some swimmers were rescued by lifeguards under the system. Figure 20 shows an example
of a rip current accident. It can be confirmed that one swimmer was drowning in the rip current
area. Figure 21 shows the detection areas by the analysis of the system log data on the images atsame time as Fig. 20. It was confirmed AI could detect rip currents and human entrance into ripcurrent areas. Also, the detected rip current areas mostly accorded with actual rip current areas
which shift due to wave conditions. In this rip current accident, although the swimmer was neardrowning, lifeguards could take early action by receiving the information quickly from the system
to their smartwatches. The life of the swimmer was saved by approximately 50 seconds. As in thisexample, some swimmers were rescued by lifeguards under the system in 2019.
+0’30”

Swimmer Swimmer+1’48”

Swimmer Swimmer

Drowning+2’35”+2’23”+2’07”

Lifeguard

Drowning

Saved life

Drowning
Lifeguard

Lifeguard

Figure 20. Example of a rip current accident on 31 Aug. 2019+0’30”

Informed to signage
AI detected Rip current

Informed toLifeguard’s smartwatch+1’48”AI detected Swimmer+2’35”+2’23”+2’07”

Saved life

Lifeguard

Lifeguard

Lifeguard

Figure 21. Example of a rip current accident with detection areas by the analysis of the system
log data on 31 Aug. 2019

Primary Topic: Coastal management and risk assessments
Secondary Topic: Coastal observations and monitoring

5. Conclusion
In this study, we verified the accuracy of the AI function which detects the rip currents using 3
methods. As a result, it was confirmed that the accuracy of rip current generation by AI was 88 %in the image analysis and 78 % in the numerical simulation, and the accuracy of no rip current
generation by AI was 61 % and 90 % respectively. Therefore, it was clear that AI could not detect
all rip currents. However, considering the actual operation results, it is generally beneficial and itis thought that it can contribute to the drowning prevention.
The system has actually been operated at 3 beaches up to 2021.The AI model eventually must
be able to detect all rip currents. Also, it should be created accordingly on individual beaches. It
has not been a general-purpose technology yet. We think these issues can be solved by it being
applied to many beaches.
Acknowledgements
This study was funded by the Ministry of Internal Affairs and Communications of Japan in
2018. It was also funded by the Nippon Foundation and the Japan Lifesaving Association in 2019and 2020.
References
Ishikawa, T., Komine, T., Aoki, S., and Okabe, T. 2014. “Characteristics of Rip Current Drowningon the Shores of Japan.” Journal of Coastal Research, 72(10072), 44-49.
Brighton, B., Sherker, S., Brander, R., Thompson, M., & Bradstreet, A. 2013. “Rip current related
drowning deaths and rescues in Australia 2004-2011.” Natural. Hazards and Earth System
Sciences, 13, 1069-1075.
Redmon, J. and Farhadi, A. 2017. “Yolo9000: Better, faster, stronger.” In Computer Vision and
Pattern Recognition (CVPR), IEEE Conference, 6517-6525.
Redmon, J. and Farhadi, A. 2018. “Yolov3: An incremental improvement.” arXiv, 1-6.
Lippmann, T. C. and Holman. R. A. 1989. “Quantification of Sand Bar Morphology - A Video
Technique Based on Wave Dissipation.” JGR, Vol. 94, 995-1011.
DHI. 2017. MIKE21 Parabolic Mild-Slope Wave Module Scientific Documentation.
DHI. 2017. MIKE21 Flow Model & Flood Screening Tool Hydrodynamic Module ScientificDocumentation.