Overview

Dataset statistics

Number of variables8
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory75.7 B

Variable types

Categorical1
Numeric7

Alerts

댐이름 has constant value ""Constant
저수위(m) is highly overall correlated with 저수량(백만m3) and 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 방류량(ms)High correlation
방류량(ms) is highly overall correlated with 유입량(ms)High correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
유입량(ms) has unique valuesUnique
방류량(ms) has unique valuesUnique
강우량(mm) has 3 (10.7%) zerosZeros

Reproduction

Analysis started2023-12-10 10:42:18.820035
Analysis finished2023-12-10 10:42:29.776672
Duration10.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
낙동강하굿둑
28 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강하굿둑 28
100.0%

Length

2023-12-10T19:42:29.941950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:42:30.127084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 28
100.0%

일자/시간(t)
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190214
Minimum20190201
Maximum20190228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:30.334606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190201
5-th percentile20190202
Q120190208
median20190214
Q320190221
95-th percentile20190227
Maximum20190228
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)4.0742386 × 10-7
Kurtosis-1.2
Mean20190214
Median Absolute Deviation (MAD)7
Skewness0
Sum5.6532601 × 108
Variance67.666667
MonotonicityStrictly increasing
2023-12-10T19:42:30.840233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20190201 1
 
3.6%
20190216 1
 
3.6%
20190228 1
 
3.6%
20190227 1
 
3.6%
20190226 1
 
3.6%
20190225 1
 
3.6%
20190224 1
 
3.6%
20190223 1
 
3.6%
20190222 1
 
3.6%
20190221 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
20190201 1
3.6%
20190202 1
3.6%
20190203 1
3.6%
20190204 1
3.6%
20190205 1
3.6%
20190206 1
3.6%
20190207 1
3.6%
20190208 1
3.6%
20190209 1
3.6%
20190210 1
3.6%
ValueCountFrequency (%)
20190228 1
3.6%
20190227 1
3.6%
20190226 1
3.6%
20190225 1
3.6%
20190224 1
3.6%
20190223 1
3.6%
20190222 1
3.6%
20190221 1
3.6%
20190220 1
3.6%
20190219 1
3.6%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90964286
Minimum0.86
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:31.225044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.86
5-th percentile0.87
Q10.8975
median0.91
Q30.93
95-th percentile0.9465
Maximum0.95
Range0.09
Interquartile range (IQR)0.0325

Descriptive statistics

Standard deviation0.025456156
Coefficient of variation (CV)0.027984781
Kurtosis-0.69895773
Mean0.90964286
Median Absolute Deviation (MAD)0.02
Skewness-0.37780663
Sum25.47
Variance0.00064801587
MonotonicityNot monotonic
2023-12-10T19:42:31.502596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.93 5
17.9%
0.91 4
14.3%
0.87 4
14.3%
0.92 4
14.3%
0.9 4
14.3%
0.95 2
 
7.1%
0.94 2
 
7.1%
0.89 2
 
7.1%
0.86 1
 
3.6%
ValueCountFrequency (%)
0.86 1
 
3.6%
0.87 4
14.3%
0.89 2
 
7.1%
0.9 4
14.3%
0.91 4
14.3%
0.92 4
14.3%
0.93 5
17.9%
0.94 2
 
7.1%
0.95 2
 
7.1%
ValueCountFrequency (%)
0.95 2
 
7.1%
0.94 2
 
7.1%
0.93 5
17.9%
0.92 4
14.3%
0.91 4
14.3%
0.9 4
14.3%
0.89 2
 
7.1%
0.87 4
14.3%
0.86 1
 
3.6%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93832143
Minimum0
Maximum16.0627
Zeros3
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:31.802174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0064
median0.02755
Q30.125025
95-th percentile5.66237
Maximum16.0627
Range16.0627
Interquartile range (IQR)0.118625

Descriptive statistics

Standard deviation3.3724434
Coefficient of variation (CV)3.5941238
Kurtosis16.569959
Mean0.93832143
Median Absolute Deviation (MAD)0.02675
Skewness4.0411552
Sum26.273
Variance11.373374
MonotonicityNot monotonic
2023-12-10T19:42:32.147146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 3
 
10.7%
0.2636 1
 
3.6%
0.0143 1
 
3.6%
0.1506 1
 
3.6%
0.2413 1
 
3.6%
0.0203 1
 
3.6%
0.0527 1
 
3.6%
0.0134 1
 
3.6%
0.2296 1
 
3.6%
0.0001 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
0.0 3
10.7%
0.0001 1
 
3.6%
0.0004 1
 
3.6%
0.0012 1
 
3.6%
0.0061 1
 
3.6%
0.0065 1
 
3.6%
0.0078 1
 
3.6%
0.0134 1
 
3.6%
0.014 1
 
3.6%
0.0143 1
 
3.6%
ValueCountFrequency (%)
16.0627 1
3.6%
8.5694 1
3.6%
0.2636 1
3.6%
0.2413 1
3.6%
0.2297 1
3.6%
0.2296 1
3.6%
0.1506 1
3.6%
0.1165 1
3.6%
0.088 1
3.6%
0.0532 1
3.6%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.604893
Minimum1.425
Maximum174.636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:32.453613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.425
5-th percentile9.8894
Q146.871
median69.3925
Q3100.8525
95-th percentile143.5957
Maximum174.636
Range173.211
Interquartile range (IQR)53.9815

Descriptive statistics

Standard deviation43.291729
Coefficient of variation (CV)0.58816374
Kurtosis-0.1752478
Mean73.604893
Median Absolute Deviation (MAD)28.4915
Skewness0.3353318
Sum2060.937
Variance1874.1738
MonotonicityNot monotonic
2023-12-10T19:42:32.800137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
92.503 1
 
3.6%
174.636 1
 
3.6%
16.522 1
 
3.6%
47.153 1
 
3.6%
83.131 1
 
3.6%
53.491 1
 
3.6%
55.911 1
 
3.6%
105.987 1
 
3.6%
55.287 1
 
3.6%
95.847 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1.425 1
3.6%
6.318 1
3.6%
16.522 1
3.6%
18.722 1
3.6%
22.791 1
3.6%
34.769 1
3.6%
46.025 1
3.6%
47.153 1
3.6%
53.491 1
3.6%
55.287 1
3.6%
ValueCountFrequency (%)
174.636 1
3.6%
154.858 1
3.6%
122.68 1
3.6%
117.052 1
3.6%
116.943 1
3.6%
108.142 1
3.6%
105.987 1
3.6%
99.141 1
3.6%
96.627 1
3.6%
95.847 1
3.6%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.757821
Minimum6.682
Maximum180.025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:33.079371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.682
5-th percentile16.71075
Q143.4365
median69.21
Q3106.3925
95-th percentile135.5173
Maximum180.025
Range173.343
Interquartile range (IQR)62.956

Descriptive statistics

Standard deviation43.933402
Coefficient of variation (CV)0.58767633
Kurtosis-0.46116632
Mean74.757821
Median Absolute Deviation (MAD)34.067
Skewness0.36182587
Sum2093.219
Variance1930.1438
MonotonicityNot monotonic
2023-12-10T19:42:33.840213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
92.503 1
 
3.6%
180.025 1
 
3.6%
16.527 1
 
3.6%
63.269 1
 
3.6%
88.515 1
 
3.6%
26.632 1
 
3.6%
72.008 1
 
3.6%
122.129 1
 
3.6%
44.525 1
 
3.6%
111.995 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
6.682 1
3.6%
16.527 1
3.6%
17.052 1
3.6%
17.587 1
3.6%
26.632 1
3.6%
29.482 1
3.6%
40.171 1
3.6%
44.525 1
3.6%
45.409 1
3.6%
45.592 1
3.6%
ValueCountFrequency (%)
180.025 1
3.6%
138.682 1
3.6%
129.64 1
3.6%
122.327 1
3.6%
122.129 1
3.6%
117.294 1
3.6%
111.995 1
3.6%
104.525 1
3.6%
102.029 1
3.6%
93.374 1
3.6%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.21325
Minimum301.908
Maximum306.092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:34.144875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum301.908
5-th percentile302.372
Q1303.64775
median304.229
Q3305.159
95-th percentile305.92855
Maximum306.092
Range4.184
Interquartile range (IQR)1.51125

Descriptive statistics

Standard deviation1.1831715
Coefficient of variation (CV)0.0038892833
Kurtosis-0.70026645
Mean304.21325
Median Absolute Deviation (MAD)0.93
Skewness-0.37436907
Sum8517.971
Variance1.3998949
MonotonicityNot monotonic
2023-12-10T19:42:34.398131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
305.159 5
17.9%
304.229 4
14.3%
302.372 4
14.3%
304.694 4
14.3%
303.764 4
14.3%
306.092 2
 
7.1%
305.625 2
 
7.1%
303.299 2
 
7.1%
301.908 1
 
3.6%
ValueCountFrequency (%)
301.908 1
 
3.6%
302.372 4
14.3%
303.299 2
 
7.1%
303.764 4
14.3%
304.229 4
14.3%
304.694 4
14.3%
305.159 5
17.9%
305.625 2
 
7.1%
306.092 2
 
7.1%
ValueCountFrequency (%)
306.092 2
 
7.1%
305.625 2
 
7.1%
305.159 5
17.9%
304.694 4
14.3%
304.229 4
14.3%
303.764 4
14.3%
303.299 2
 
7.1%
302.372 4
14.3%
301.908 1
 
3.6%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)39.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.025357
Minimum98.28
Maximum99.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-10T19:42:34.637786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.28
5-th percentile98.43
Q198.8425
median99.03
Q399.34
95-th percentile99.5875
Maximum99.64
Range1.36
Interquartile range (IQR)0.4975

Descriptive statistics

Standard deviation0.38429813
Coefficient of variation (CV)0.0038808053
Kurtosis-0.70440857
Mean99.025357
Median Absolute Deviation (MAD)0.3
Skewness-0.34919332
Sum2772.71
Variance0.14768505
MonotonicityNot monotonic
2023-12-10T19:42:34.884367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
98.43 4
14.3%
99.34 4
14.3%
99.18 4
14.3%
98.88 4
14.3%
99.03 3
10.7%
99.64 2
7.1%
99.49 2
7.1%
98.73 2
7.1%
99.3 1
 
3.6%
99.0 1
 
3.6%
ValueCountFrequency (%)
98.28 1
 
3.6%
98.43 4
14.3%
98.73 2
7.1%
98.88 4
14.3%
99.0 1
 
3.6%
99.03 3
10.7%
99.18 4
14.3%
99.3 1
 
3.6%
99.34 4
14.3%
99.49 2
7.1%
ValueCountFrequency (%)
99.64 2
7.1%
99.49 2
7.1%
99.34 4
14.3%
99.3 1
 
3.6%
99.18 4
14.3%
99.03 3
10.7%
99.0 1
 
3.6%
98.88 4
14.3%
98.73 2
7.1%
98.43 4
14.3%

Interactions

2023-12-10T19:42:28.028523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:19.274907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:20.649174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:22.264179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:23.668680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:25.282158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:26.820512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:28.209020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:19.455319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:20.806639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:22.506113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:23.828600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:25.554563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:26.974686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:28.362236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:19.637623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:21.363036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:22.686089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:24.004158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:25.712809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:27.119121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:28.557370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:19.905589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:21.586843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:22.864582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:24.188442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:25.956760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:27.290087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:28.740225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:20.080828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:21.750870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:23.052192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:24.372719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:26.148486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:27.483459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:28.915228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:20.250883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:21.926642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:23.280591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:24.642219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:26.366686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:27.732033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:29.089243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:20.461513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:22.088189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:23.472270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:24.946919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:26.624311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:27.881193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:42:35.083199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.0000.6230.0000.1010.0000.000
저수위(m)0.0001.0000.0000.1470.0001.0001.000
강우량(mm)0.6230.0001.0000.0000.5690.0000.000
유입량(ms)0.0000.1470.0001.0000.7520.3770.147
방류량(ms)0.1010.0000.5690.7521.0000.0000.000
저수량(백만m3)0.0001.0000.0000.3770.0001.0001.000
저수율0.0001.0000.0000.1470.0001.0001.000
2023-12-10T19:42:35.325636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.3440.136-0.050-0.019-0.344-0.319
저수위(m)-0.3441.000-0.2950.4510.2461.0000.998
강우량(mm)0.136-0.2951.0000.1960.347-0.295-0.319
유입량(ms)-0.0500.4510.1961.0000.9220.4510.456
방류량(ms)-0.0190.2460.3470.9221.0000.2460.244
저수량(백만m3)-0.3441.000-0.2950.4510.2461.0000.998
저수율-0.3190.998-0.3190.4560.2440.9981.000

Missing values

2023-12-10T19:42:29.337141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:42:29.615852image/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

댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
0낙동강하굿둑201902010.930.263692.50392.503305.15999.3
1낙동강하굿둑201902020.910.116546.02556.789304.22999.0
2낙동강하굿둑201902030.878.5694108.142129.64302.37298.43
3낙동강하굿둑201902040.930.0348117.05284.791305.15999.34
4낙동강하굿둑201902050.920.099.141104.525304.69499.18
5낙동강하굿둑201902060.910.007861.02666.412304.22999.03
6낙동강하굿둑201902070.950.006578.81157.253306.09299.64
7낙동강하굿둑201902080.940.01434.76940.171305.62599.49
8낙동강하굿둑201902090.910.00611.42517.587304.22999.03
9낙동강하굿둑201902100.890.08818.72229.482303.29998.73
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
18낙동강하굿둑201902190.9216.0627116.943122.327304.69499.18
19낙동강하굿둑201902200.930.0393122.68117.294305.15999.34
20낙동강하굿둑201902210.90.229795.847111.995303.76498.88
21낙동강하굿둑201902220.920.000155.28744.525304.69499.18
22낙동강하굿둑201902230.890.2296105.987122.129303.29998.73
23낙동강하굿둑201902240.860.013455.91172.008301.90898.28
24낙동강하굿둑201902250.910.052753.49126.632304.22999.03
25낙동강하굿둑201902260.90.020383.13188.515303.76498.88
26낙동강하굿둑201902270.870.241347.15363.269302.37298.43
27낙동강하굿둑201902280.870.150616.52216.527302.37298.43