Overview

Dataset statistics

Number of variables7
Number of observations189
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory59.7 B

Variable types

Numeric3
Text3
Categorical1

Dataset

Description국토부, 기상청, K-water 관리 강우관측소 중 K-water 관리대상 강우관측소 제원정보(강우관측소 코드, 강우관측소명, 경도, 위도, 도로명주소, 수계 등)를 제공합니다.
Author한국수자원공사
URLhttps://www.data.go.kr/data/15049845/fileData.do

Alerts

강우관측소 코드 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 수계High correlation
위도 is highly overall correlated with 강우관측소 코드High correlation
수계 is highly overall correlated with 강우관측소 코드 and 1 other fieldsHigh correlation
강우관측소 코드 has unique valuesUnique
강우관측소 명 has unique valuesUnique
도로명주소 상세 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:30:55.704438
Analysis finished2023-12-12 04:30:57.705123
Duration2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

강우관측소 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4066826.9
Minimum1001420
Maximum9000240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T13:30:57.811881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001420
5-th percentile1001442.8
Q11011430
median2020448
Q39000013
95-th percentile9000141.8
Maximum9000240
Range7998820
Interquartile range (IQR)7988583

Descriptive statistics

Standard deviation3325624.5
Coefficient of variation (CV)0.8177443
Kurtosis-1.3178015
Mean4066826.9
Median Absolute Deviation (MAD)1018002
Skewness0.70241036
Sum7.6863029 × 108
Variance1.1059779 × 1013
MonotonicityStrictly increasing
2023-12-12T13:30:58.021109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001420 1
 
0.5%
5002442 1
 
0.5%
4001450 1
 
0.5%
4003420 1
 
0.5%
4007450 1
 
0.5%
4007470 1
 
0.5%
4007472 1
 
0.5%
4007474 1
 
0.5%
4009460 1
 
0.5%
4105402 1
 
0.5%
Other values (179) 179
94.7%
ValueCountFrequency (%)
1001420 1
0.5%
1001422 1
0.5%
1001426 1
0.5%
1001430 1
0.5%
1001432 1
0.5%
1001434 1
0.5%
1001436 1
0.5%
1001438 1
0.5%
1001440 1
0.5%
1001442 1
0.5%
ValueCountFrequency (%)
9000240 1
0.5%
9000239 1
0.5%
9000234 1
0.5%
9000233 1
0.5%
9000232 1
0.5%
9000171 1
0.5%
9000170 1
0.5%
9000168 1
0.5%
9000167 1
0.5%
9000143 1
0.5%

강우관측소 명
Text

UNIQUE 

Distinct189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T13:30:58.371712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.1375661
Min length7

Characters and Unicode

Total characters1538
Distinct characters157
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)100.0%

Sample

1st row정선군(송계리)
2nd row평창군(상진부리)
3rd row평창군(수하리)
4th row정선군(여탄리)
5th row정선군(직전리)
ValueCountFrequency (%)
정선군(송계리 1
 
0.5%
진안군(도통리 1
 
0.5%
순창군(시산리 1
 
0.5%
임실군(섬진강댐 1
 
0.5%
보성군(복내리 1
 
0.5%
순천시(주암댐 1
 
0.5%
화순군(동가리 1
 
0.5%
화순군(맹리 1
 
0.5%
순천시(우산리 1
 
0.5%
광양시(어치리 1
 
0.5%
Other values (179) 179
94.7%
2023-12-12T13:30:58.864876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 189
 
12.3%
) 189
 
12.3%
리 142
 
9.2%
군 137
 
8.9%
시 57
 
3.7%
천 39
 
2.5%
산 33
 
2.1%
댐 30
 
2.0%
양 24
 
1.6%
주 23
 
1.5%
Other values (147) 675
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1158
75.3%
Open Punctuation 189
 
12.3%
Close Punctuation 189
 
12.3%
Decimal Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 142
 
12.3%
군 137
 
11.8%
시 57
 
4.9%
천 39
 
3.4%
산 33
 
2.8%
댐 30
 
2.6%
양 24
 
2.1%
주 23
 
2.0%
동 22
 
1.9%
청 19
 
1.6%
Other values (144) 632
54.6%
Open Punctuation
ValueCountFrequency (%)
( 189
100.0%
Close Punctuation
ValueCountFrequency (%)
) 189
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1158
75.3%
Common 380
 
24.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 142
 
12.3%
군 137
 
11.8%
시 57
 
4.9%
천 39
 
3.4%
산 33
 
2.8%
댐 30
 
2.6%
양 24
 
2.1%
주 23
 
2.0%
동 22
 
1.9%
청 19
 
1.6%
Other values (144) 632
54.6%
Common
ValueCountFrequency (%)
( 189
49.7%
) 189
49.7%
2 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1158
75.3%
ASCII 380
 
24.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 189
49.7%
) 189
49.7%
2 2
 
0.5%
Hangul
ValueCountFrequency (%)
리 142
 
12.3%
군 137
 
11.8%
시 57
 
4.9%
천 39
 
3.4%
산 33
 
2.8%
댐 30
 
2.6%
양 24
 
2.1%
주 23
 
2.0%
동 22
 
1.9%
청 19
 
1.6%
Other values (144) 632
54.6%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.13004
Minimum126.33556
Maximum129.51278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T13:30:59.028935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.33556
5-th percentile126.86772
Q1127.68139
median128.08667
Q3128.81083
95-th percentile129.13078
Maximum129.51278
Range3.177222
Interquartile range (IQR)1.129444

Descriptive statistics

Standard deviation0.71132502
Coefficient of variation (CV)0.0055515868
Kurtosis-0.68716283
Mean128.13004
Median Absolute Deviation (MAD)0.545
Skewness-0.27566059
Sum24216.577
Variance0.50598328
MonotonicityNot monotonic
2023-12-12T13:30:59.189457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.563333 2
 
1.1%
128.810833 2
 
1.1%
129.023611 2
 
1.1%
128.932222 1
 
0.5%
128.033889 1
 
0.5%
127.207222 1
 
0.5%
129.006944 1
 
0.5%
126.939444 1
 
0.5%
127.113611 1
 
0.5%
127.136667 1
 
0.5%
Other values (176) 176
93.1%
ValueCountFrequency (%)
126.335556 1
0.5%
126.563333 2
1.1%
126.632778 1
0.5%
126.6689 1
0.5%
126.68 1
0.5%
126.6831 1
0.5%
126.716944 1
0.5%
126.784444 1
0.5%
126.853611 1
0.5%
126.888889 1
0.5%
ValueCountFrequency (%)
129.512778 1
0.5%
129.269444 1
0.5%
129.204167 1
0.5%
129.196389 1
0.5%
129.196111 1
0.5%
129.183056 1
0.5%
129.170556 1
0.5%
129.156944 1
0.5%
129.145 1
0.5%
129.143333 1
0.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.355408
Minimum34.750278
Maximum38.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T13:30:59.370988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.750278
5-th percentile34.950833
Q135.577778
median36.206944
Q337.136111
95-th percentile37.993722
Maximum38.26
Range3.509722
Interquartile range (IQR)1.558333

Descriptive statistics

Standard deviation0.94249536
Coefficient of variation (CV)0.025924489
Kurtosis-1.039676
Mean36.355408
Median Absolute Deviation (MAD)0.752778
Skewness0.23408807
Sum6871.1721
Variance0.88829751
MonotonicityNot monotonic
2023-12-12T13:30:59.566305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.675 2
 
1.1%
36.704167 2
 
1.1%
36.331111 2
 
1.1%
35.554444 2
 
1.1%
35.076389 1
 
0.5%
35.485 1
 
0.5%
35.539444 1
 
0.5%
34.893333 1
 
0.5%
35.058333 1
 
0.5%
34.963333 1
 
0.5%
Other values (175) 175
92.6%
ValueCountFrequency (%)
34.750278 1
0.5%
34.778611 1
0.5%
34.802222 1
0.5%
34.806389 1
0.5%
34.836389 1
0.5%
34.844444 1
0.5%
34.893333 1
0.5%
34.927222 1
0.5%
34.936111 1
0.5%
34.9425 1
0.5%
ValueCountFrequency (%)
38.26 1
0.5%
38.209444 1
0.5%
38.195556 1
0.5%
38.141111 1
0.5%
38.1164 1
0.5%
38.1047 1
0.5%
38.071667 1
0.5%
38.038333 1
0.5%
38.024444 1
0.5%
38.011389 1
0.5%

도로명주소 상세
Text

UNIQUE 

Distinct189
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T13:31:00.100285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20.026455
Min length11

Characters and Unicode

Total characters3785
Distinct characters206
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique189 ?
Unique (%)100.0%

Sample

1st row강원도 정선군 임계면 송계1리 887-2
2nd row강원도 평창군 진부면 상진부리 42-4
3rd row강원도 평창군 도암면 수하리 142-1
4th row강원도 정선군 정선읍 애산3리250
5th row강원도 정선군 사북읍 직전리 산588-2
ValueCountFrequency (%)
강원도 45
 
4.9%
경상북도 34
 
3.7%
경상남도 29
 
3.2%
충청북도 24
 
2.6%
전라북도 19
 
2.1%
전라남도 18
 
2.0%
평창군 9
 
1.0%
인제군 9
 
1.0%
산청군 8
 
0.9%
봉화군 7
 
0.8%
Other values (551) 710
77.9%
2023-12-12T13:31:00.773134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
723
 
19.1%
도 184
 
4.9%
리 182
 
4.8%
면 160
 
4.2%
군 141
 
3.7%
북 102
 
2.7%
1 102
 
2.7%
- 101
 
2.7%
산 88
 
2.3%
2 85
 
2.2%
Other values (196) 1917
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2453
64.8%
Space Separator 723
 
19.1%
Decimal Number 504
 
13.3%
Dash Punctuation 101
 
2.7%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 184
 
7.5%
리 182
 
7.4%
면 160
 
6.5%
군 141
 
5.7%
북 102
 
4.2%
산 88
 
3.6%
상 84
 
3.4%
경 76
 
3.1%
남 70
 
2.9%
원 62
 
2.5%
Other values (182) 1304
53.2%
Decimal Number
ValueCountFrequency (%)
1 102
20.2%
2 85
16.9%
3 51
10.1%
5 44
8.7%
4 44
8.7%
7 43
8.5%
6 36
 
7.1%
8 34
 
6.7%
0 33
 
6.5%
9 32
 
6.3%
Space Separator
ValueCountFrequency (%)
723
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2453
64.8%
Common 1332
35.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 184
 
7.5%
리 182
 
7.4%
면 160
 
6.5%
군 141
 
5.7%
북 102
 
4.2%
산 88
 
3.6%
상 84
 
3.4%
경 76
 
3.1%
남 70
 
2.9%
원 62
 
2.5%
Other values (182) 1304
53.2%
Common
ValueCountFrequency (%)
723
54.3%
1 102
 
7.7%
- 101
 
7.6%
2 85
 
6.4%
3 51
 
3.8%
5 44
 
3.3%
4 44
 
3.3%
7 43
 
3.2%
6 36
 
2.7%
8 34
 
2.6%
Other values (4) 69
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2453
64.8%
ASCII 1332
35.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
723
54.3%
1 102
 
7.7%
- 101
 
7.6%
2 85
 
6.4%
3 51
 
3.8%
5 44
 
3.3%
4 44
 
3.3%
7 43
 
3.2%
6 36
 
2.7%
8 34
 
2.6%
Other values (4) 69
 
5.2%
Hangul
ValueCountFrequency (%)
도 184
 
7.5%
리 182
 
7.4%
면 160
 
6.5%
군 141
 
5.7%
북 102
 
4.2%
산 88
 
3.6%
상 84
 
3.4%
경 76
 
3.1%
남 70
 
2.9%
원 62
 
2.5%
Other values (182) 1304
53.2%

강우관측소 명2
Text

Distinct188
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T13:31:01.280357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length2.5820106
Min length2

Characters and Unicode

Total characters488
Distinct characters137
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)98.9%

Sample

1st row임계
2nd row진부
3rd row횡계
4th row정선
5th row사북
ValueCountFrequency (%)
창촌 2
 
1.1%
우산 1
 
0.5%
선리 1
 
0.5%
담양홍수조절지 1
 
0.5%
신평 1
 
0.5%
쌍치 1
 
0.5%
섬진강댐 1
 
0.5%
복내 1
 
0.5%
주암댐 1
 
0.5%
동가 1
 
0.5%
Other values (178) 178
94.2%
2023-12-12T13:31:01.964065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
댐 27
 
5.5%
천 23
 
4.7%
산 19
 
3.9%
동 10
 
2.0%
( 10
 
2.0%
수 10
 
2.0%
주 10
 
2.0%
양 10
 
2.0%
) 10
 
2.0%
2 9
 
1.8%
Other values (127) 350
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 456
93.4%
Decimal Number 12
 
2.5%
Open Punctuation 10
 
2.0%
Close Punctuation 10
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
댐 27
 
5.9%
천 23
 
5.0%
산 19
 
4.2%
동 10
 
2.2%
수 10
 
2.2%
주 10
 
2.2%
양 10
 
2.2%
군 8
 
1.8%
상 8
 
1.8%
장 8
 
1.8%
Other values (123) 323
70.8%
Decimal Number
ValueCountFrequency (%)
2 9
75.0%
1 3
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 456
93.4%
Common 32
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
댐 27
 
5.9%
천 23
 
5.0%
산 19
 
4.2%
동 10
 
2.2%
수 10
 
2.2%
주 10
 
2.2%
양 10
 
2.2%
군 8
 
1.8%
상 8
 
1.8%
장 8
 
1.8%
Other values (123) 323
70.8%
Common
ValueCountFrequency (%)
( 10
31.2%
) 10
31.2%
2 9
28.1%
1 3
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 456
93.4%
ASCII 32
 
6.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
댐 27
 
5.9%
천 23
 
5.0%
산 19
 
4.2%
동 10
 
2.2%
수 10
 
2.2%
주 10
 
2.2%
양 10
 
2.2%
군 8
 
1.8%
상 8
 
1.8%
장 8
 
1.8%
Other values (123) 323
70.8%
ASCII
ValueCountFrequency (%)
( 10
31.2%
) 10
31.2%
2 9
28.1%
1 3
 
9.4%

수계
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
낙동강
69 
한강
59 
금강
25 
섬진강
12 
섬진강남해
 
4
Other values (9)
20 

Length

Max length5
Median length4
Mean length2.6560847
Min length2

Unique

Unique4 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강 69
36.5%
한강 59
31.2%
금강 25
 
13.2%
섬진강 12
 
6.3%
섬진강남해 4
 
2.1%
영산강 4
 
2.1%
태화강 4
 
2.1%
금강서해 3
 
1.6%
탐진강 3
 
1.6%
낙동강남해 2
 
1.1%
Other values (4) 4
 
2.1%

Length

2023-12-12T13:31:02.173708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강 69
36.5%
한강 59
31.2%
금강 25
 
13.2%
섬진강 12
 
6.3%
섬진강남해 4
 
2.1%
영산강 4
 
2.1%
태화강 4
 
2.1%
금강서해 3
 
1.6%
탐진강 3
 
1.6%
낙동강남해 2
 
1.1%
Other values (4) 4
 
2.1%

Interactions

2023-12-12T13:30:56.821570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.154272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.516712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.953209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.277476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.620164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:57.079762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.401426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:56.720725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:31:02.269042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우관측소 코드경도위도수계
강우관측소 코드1.0000.7090.6900.933
경도0.7091.0000.6870.857
위도0.6900.6871.0000.760
수계0.9330.8570.7601.000
2023-12-12T13:31:02.385591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강우관측소 코드경도위도수계
강우관측소 코드1.000-0.300-0.5740.785
경도-0.3001.0000.4050.565
위도-0.5740.4051.0000.430
수계0.7850.5650.4301.000

Missing values

2023-12-12T13:30:57.492877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:30:57.650096image/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

강우관측소 코드강우관측소 명경도위도도로명주소 상세강우관측소 명2수계
01001420정선군(송계리)128.8237.494167강원도 정선군 임계면 송계1리 887-2임계한강
11001422평창군(상진부리)128.57805637.658889강원도 평창군 진부면 상진부리 42-4진부한강
21001426평창군(수하리)128.707537.659167강원도 평창군 도암면 수하리 142-1횡계한강
31001430정선군(여탄리)128.69944437.381389강원도 정선군 정선읍 애산3리250정선한강
41001432정선군(직전리)128.81944437.234722강원도 정선군 사북읍 직전리 산588-2사북한강
51001434영월군(북쌍리)128.41138937.191389강원도 영월군 남면 북상리 252-1영월1한강
61001436강릉시(대기리)128.74416737.573056강원도 강릉시 왕산면 대기3리 산1656왕산한강
71001438삼척시(광동리)128.93666737.35강원도 삼척시 하장면 광동리 120하장한강
81001440평창군(율치리)128.492537.325강원도 평창군 미탄면 율치리 산20-4미탄한강
91001442평창군(수항리)128.567537.561111강원도 평창군 진부면 수항리 96-1수항한강
강우관측소 코드강우관측소 명경도위도도로명주소 상세강우관측소 명2수계
1799000143춘천시(소양강댐)127.810837.9444강원도 춘천시 신북읍 천전리 산4춘천시(소양강댐)한강
1809000167울산시(구미리)129.19638935.656944울산광역시 울주군 두동면 구미리 566-1대곡(상)태화강
1819000168울산시(천전리)129.17055635.618889울산광역시 울주군 두동면 천전리 306대곡(하)태화강
1829000170경주시(대현리)129.05805635.726667경상북도 경주시 산내면 대현리 3127-32대현낙동강
1839000171포항시(지동리)129.06611136.136944경상북도 포항시 북구 죽장면 지동리 산49-5지동낙동강
1849000232경주시(감포댐)129.51277835.833056경상북도 경주시 감포읍 오류리 산 16-2감포댐낙동강동해
1859000233장흥군(봉동리)127.00944434.778611전라남도 장흥근 장동면 봉동리 245-1장동섬진강
1869000234보성군(보성강댐)127.14722234.802222전라남도 보성군 겸백면 용산리 산 35-1보성섬진강
1879000239연천군(군남댐)127.021438.1047경기도 연천군 군남면 선곡리 606-2연천군(군남댐)한강
1889000240연천군(횡산리)126.986938.1164경기도 연천군 중면 횡산리 238연천군(횡산리)한강