Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory71.3 B

Variable types

Categorical2
Numeric6

Alerts

관측소명 is highly overall correlated with 전년 누적 강우량(mm) and 1 other fieldsHigh correlation
댐명 is highly overall correlated with 전년 누적 강우량(mm) and 1 other fieldsHigh correlation
당년 누적 강우량(mm) is highly overall correlated with 전년 누적 강우량(mm) and 1 other fieldsHigh correlation
전년 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
예년 평균 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 1 other fieldsHigh correlation
강우량(mm) has 76 (76.0%) zerosZeros
전년 강우량(mm) has 78 (78.0%) zerosZeros
전년 누적 강우량(mm) has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:59:57.194066
Analysis finished2023-12-10 14:00:04.866258
Duration7.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
남강
21 
괴산
14 
군남
13 
광동
12 
감포
11 
Other values (3)
29 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row감포
2nd row감포
3rd row감포
4th row감포
5th row감포

Common Values

ValueCountFrequency (%)
남강 21
21.0%
괴산 14
14.0%
군남 13
13.0%
광동 12
12.0%
감포 11
11.0%
구천 11
11.0%
군위 10
10.0%
달방 8
 
8.0%

Length

2023-12-10T23:00:05.092381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:00:05.300180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남강 21
21.0%
괴산 14
14.0%
군남 13
13.0%
광동 12
12.0%
감포 11
11.0%
구천 11
11.0%
군위 10
10.0%
달방 8
 
8.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주시(감포댐)
11 
거제시(구천댐)
11 
연천군(군남댐)
9 
괴산댐(FTP)
8 
동해시(달방댐)
8 
Other values (19)
53 

Length

Max length9
Median length8
Mean length7.93
Min length6

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row경주시(감포댐)
2nd row경주시(감포댐)
3rd row경주시(감포댐)
4th row경주시(감포댐)
5th row경주시(감포댐)

Common Values

ValueCountFrequency (%)
경주시(감포댐) 11
11.0%
거제시(구천댐) 11
11.0%
연천군(군남댐) 9
 
9.0%
괴산댐(FTP) 8
 
8.0%
동해시(달방댐) 8
 
8.0%
삼척시(광동댐) 7
 
7.0%
내속리(전) 6
 
6.0%
군위군(양지리) 5
 
5.0%
군위군(군위댐) 5
 
5.0%
태백시(상사미동) 5
 
5.0%
Other values (14) 25
25.0%

Length

2023-12-10T23:00:05.588585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주시(감포댐 11
11.0%
거제시(구천댐 11
11.0%
연천군(군남댐 9
 
9.0%
괴산댐(ftp 8
 
8.0%
동해시(달방댐 8
 
8.0%
삼척시(광동댐 7
 
7.0%
내속리(전 6
 
6.0%
군위군(양지리 5
 
5.0%
군위군(군위댐 5
 
5.0%
태백시(상사미동 5
 
5.0%
Other values (14) 25
25.0%

관측일
Real number (ℝ)

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190319
Minimum20190303
Maximum20190331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:05.785466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190303
5-th percentile20190306
Q120190312
median20190320
Q320190325
95-th percentile20190330
Maximum20190331
Range28
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation7.8648367
Coefficient of variation (CV)3.8953505 × 10-7
Kurtosis-1.0573368
Mean20190319
Median Absolute Deviation (MAD)6.5
Skewness-0.1979889
Sum2.0190319 × 109
Variance61.855657
MonotonicityNot monotonic
2023-12-10T23:00:05.983949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20190309 7
 
7.0%
20190320 6
 
6.0%
20190327 6
 
6.0%
20190322 6
 
6.0%
20190325 5
 
5.0%
20190331 5
 
5.0%
20190311 5
 
5.0%
20190329 5
 
5.0%
20190318 5
 
5.0%
20190323 4
 
4.0%
Other values (16) 46
46.0%
ValueCountFrequency (%)
20190303 3
3.0%
20190306 3
3.0%
20190307 4
4.0%
20190309 7
7.0%
20190310 3
3.0%
20190311 5
5.0%
20190312 2
 
2.0%
20190313 2
 
2.0%
20190314 4
4.0%
20190315 2
 
2.0%
ValueCountFrequency (%)
20190331 5
5.0%
20190330 3
3.0%
20190329 5
5.0%
20190328 2
 
2.0%
20190327 6
6.0%
20190326 3
3.0%
20190325 5
5.0%
20190324 3
3.0%
20190323 4
4.0%
20190322 6
6.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.765
Minimum0
Maximum34
Zeros76
Zeros (%)76.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:06.194815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13.2
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.2816889
Coefficient of variation (CV)2.9924583
Kurtosis16.81724
Mean1.765
Median Absolute Deviation (MAD)0
Skewness3.8948941
Sum176.5
Variance27.896237
MonotonicityNot monotonic
2023-12-10T23:00:06.370809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 76
76.0%
1.0 6
 
6.0%
2.0 4
 
4.0%
0.5 2
 
2.0%
5.0 1
 
1.0%
12.0 1
 
1.0%
17.0 1
 
1.0%
4.0 1
 
1.0%
20.0 1
 
1.0%
19.0 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0.0 76
76.0%
0.5 2
 
2.0%
1.0 6
 
6.0%
2.0 4
 
4.0%
2.5 1
 
1.0%
4.0 1
 
1.0%
5.0 1
 
1.0%
6.5 1
 
1.0%
10.0 1
 
1.0%
12.0 1
 
1.0%
ValueCountFrequency (%)
34.0 1
1.0%
20.0 1
1.0%
19.0 1
1.0%
18.5 1
1.0%
17.0 1
1.0%
13.0 1
1.0%
12.0 1
1.0%
10.0 1
1.0%
6.5 1
1.0%
5.0 1
1.0%

전년 강우량(mm)
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.385
Minimum0
Maximum54.5
Zeros78
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:06.538568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.25
Maximum54.5
Range54.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.592296
Coefficient of variation (CV)3.6026398
Kurtosis21.969794
Mean2.385
Median Absolute Deviation (MAD)0
Skewness4.6505707
Sum238.5
Variance73.827551
MonotonicityNot monotonic
2023-12-10T23:00:06.700539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 78
78.0%
1.0 6
 
6.0%
3.0 3
 
3.0%
5.0 2
 
2.0%
3.5 1
 
1.0%
2.0 1
 
1.0%
43.0 1
 
1.0%
54.5 1
 
1.0%
34.0 1
 
1.0%
5.5 1
 
1.0%
Other values (5) 5
 
5.0%
ValueCountFrequency (%)
0.0 78
78.0%
1.0 6
 
6.0%
2.0 1
 
1.0%
3.0 3
 
3.0%
3.5 1
 
1.0%
4.0 1
 
1.0%
5.0 2
 
2.0%
5.5 1
 
1.0%
6.0 1
 
1.0%
9.0 1
 
1.0%
ValueCountFrequency (%)
54.5 1
1.0%
43.0 1
1.0%
38.0 1
1.0%
34.0 1
1.0%
14.0 1
1.0%
9.0 1
1.0%
6.0 1
1.0%
5.5 1
1.0%
5.0 2
2.0%
4.0 1
1.0%

당년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.85
Minimum10
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:06.910271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q137
median69
Q381.375
95-th percentile273
Maximum273
Range263
Interquartile range (IQR)44.375

Descriptive statistics

Standard deviation63.108072
Coefficient of variation (CV)0.8320115
Kurtosis4.4491804
Mean75.85
Median Absolute Deviation (MAD)28
Skewness2.1687854
Sum7585
Variance3982.6288
MonotonicityNot monotonic
2023-12-10T23:00:07.164017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 7
 
7.0%
273.0 6
 
6.0%
37.0 4
 
4.0%
78.0 4
 
4.0%
79.0 4
 
4.0%
39.0 3
 
3.0%
41.0 3
 
3.0%
42.0 3
 
3.0%
24.0 3
 
3.0%
75.0 3
 
3.0%
Other values (44) 60
60.0%
ValueCountFrequency (%)
10.0 1
 
1.0%
19.0 2
 
2.0%
20.0 7
7.0%
24.0 3
3.0%
27.5 1
 
1.0%
28.0 2
 
2.0%
29.0 1
 
1.0%
30.0 2
 
2.0%
31.0 2
 
2.0%
32.0 1
 
1.0%
ValueCountFrequency (%)
273.0 6
6.0%
265.0 1
 
1.0%
231.0 1
 
1.0%
128.0 1
 
1.0%
116.0 1
 
1.0%
115.0 2
 
2.0%
108.0 2
 
2.0%
107.0 2
 
2.0%
104.0 1
 
1.0%
102.0 1
 
1.0%

전년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.295
Minimum0
Maximum348
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:07.387858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q194.5
median146.5
Q3178.75
95-th percentile348
Maximum348
Range348
Interquartile range (IQR)84.25

Descriptive statistics

Standard deviation85.708459
Coefficient of variation (CV)0.59398079
Kurtosis0.31123858
Mean144.295
Median Absolute Deviation (MAD)47.5
Skewness0.61570161
Sum14429.5
Variance7345.9399
MonotonicityNot monotonic
2023-12-10T23:00:07.626730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.0 7
 
7.0%
348.0 6
 
6.0%
0.0 6
 
6.0%
260.0 5
 
5.0%
194.0 4
 
4.0%
162.0 4
 
4.0%
156.0 3
 
3.0%
148.0 3
 
3.0%
151.0 3
 
3.0%
140.0 3
 
3.0%
Other values (44) 56
56.0%
ValueCountFrequency (%)
0.0 6
6.0%
40.0 2
 
2.0%
42.0 1
 
1.0%
44.0 2
 
2.0%
49.0 1
 
1.0%
50.0 1
 
1.0%
51.0 7
7.0%
59.0 1
 
1.0%
60.0 1
 
1.0%
72.0 1
 
1.0%
ValueCountFrequency (%)
348.0 6
6.0%
329.0 1
 
1.0%
285.0 1
 
1.0%
260.0 5
5.0%
258.0 1
 
1.0%
220.0 1
 
1.0%
218.0 1
 
1.0%
215.0 1
 
1.0%
195.0 2
 
2.0%
194.0 4
4.0%

예년 평균 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.59
Minimum24.3
Maximum174.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:00:07.851063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.3
5-th percentile36.165
Q145.575
median79.55
Q3118.325
95-th percentile154.33
Maximum174.4
Range150.1
Interquartile range (IQR)72.75

Descriptive statistics

Standard deviation40.751867
Coefficient of variation (CV)0.47612883
Kurtosis-1.1081369
Mean85.59
Median Absolute Deviation (MAD)34.3
Skewness0.38833292
Sum8559
Variance1660.7146
MonotonicityNot monotonic
2023-12-10T23:00:08.123007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.9 3
 
3.0%
49.6 2
 
2.0%
37.2 2
 
2.0%
79.2 2
 
2.0%
35.5 2
 
2.0%
42.3 2
 
2.0%
45.0 2
 
2.0%
41.1 1
 
1.0%
24.3 1
 
1.0%
44.4 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
24.3 1
1.0%
30.0 1
1.0%
30.7 1
1.0%
35.5 2
2.0%
36.2 1
1.0%
36.3 1
1.0%
37.2 2
2.0%
40.5 1
1.0%
41.1 1
1.0%
41.5 1
1.0%
ValueCountFrequency (%)
174.4 1
1.0%
164.3 1
1.0%
158.3 1
1.0%
157.5 1
1.0%
154.9 1
1.0%
154.3 1
1.0%
150.4 1
1.0%
149.9 1
1.0%
149.8 1
1.0%
148.0 1
1.0%

Interactions

2023-12-10T23:00:03.521244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:57.777125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:58.984143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.073973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.980041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.995707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:03.668458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:58.015418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:59.238209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.221334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.210847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:02.173037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:03.826959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:58.216209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:59.449878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.374902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.357639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:02.845976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:03.967189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:58.447168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:59.589532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.514458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.496682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:03.026759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:04.146610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:58.594147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:59.728572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.643587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.621044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:03.153825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:04.317074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:58.769995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:59.895961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:00.818937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:01.752172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:00:03.340462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:00:08.320654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
댐명1.0001.0000.0000.0680.1330.7250.8270.671
관측소명1.0001.0000.0000.0000.0000.8080.8950.732
관측일0.0000.0001.0000.0630.1680.1530.1370.193
강우량(mm)0.0680.0000.0631.0000.0000.2200.1730.196
전년 강우량(mm)0.1330.0000.1680.0001.0000.0000.4270.000
당년 누적 강우량(mm)0.7250.8080.1530.2200.0001.0000.8600.577
전년 누적 강우량(mm)0.8270.8950.1370.1730.4270.8601.0000.832
예년 평균 누적 강우량(mm)0.6710.7320.1930.1960.0000.5770.8321.000
2023-12-10T23:00:08.623126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명댐명
관측소명1.0000.909
댐명0.9091.000
2023-12-10T23:00:08.864187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)댐명관측소명
관측일1.000-0.024-0.2370.4080.4330.3720.0000.000
강우량(mm)-0.0241.0000.1100.1330.0950.0400.0940.000
전년 강우량(mm)-0.2370.1101.000-0.0300.1310.1460.0680.000
당년 누적 강우량(mm)0.4080.133-0.0301.0000.8470.5380.4950.463
전년 누적 강우량(mm)0.4330.0950.1310.8471.0000.7670.5830.555
예년 평균 누적 강우량(mm)0.3720.0400.1460.5380.7671.0000.3960.339
댐명0.0000.0940.0680.4950.5830.3961.0000.909
관측소명0.0000.0000.0000.4630.5550.3390.9091.000

Missing values

2023-12-10T23:00:04.540811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:00:04.753690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
0감포경주시(감포댐)201903170.00.074.0159.5120.8
1감포경주시(감포댐)201903270.00.079.0260.0150.4
2감포경주시(감포댐)201903310.00.080.5260.0158.3
3감포경주시(감포댐)201903150.53.573.5159.5119.6
4감포경주시(감포댐)2019031018.50.071.5156.0110.5
5감포경주시(감포댐)201903090.02.053.0156.0107.9
6감포경주시(감포댐)201903290.00.079.0260.0154.3
7감포경주시(감포댐)201903212.543.079.0258.0141.5
8감포경주시(감포댐)201903130.00.073.0156.0117.9
9감포경주시(감포댐)201903250.00.079.0260.0148.0
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
90남강산청군(장천리)201903180.03.069.0133.0106.0
91남강남원시(의지리)201903280.00.0108.0170.076.1
92달방동해시(달방댐)201903304.00.090.0194.0109.3
93달방동해시(달방댐)201903260.00.078.0194.0104.2
94달방동해시(달방댐)2019030717.03.037.0120.075.8
95달방동해시(달방댐)201903170.01.063.0159.092.4
96달방동해시(달방댐)2019032012.00.075.0178.094.7
97달방동해시(달방댐)201903240.00.078.0194.0102.2
98달방동해시(달방댐)201903090.06.037.0145.079.2
99달방동해시(달방댐)201903220.014.078.0194.096.8