Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory75.3 B

Variable types

Categorical1
Numeric7

Alerts

댐이름 has constant value ""Constant
일자/시간(t) is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
저수위(m) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 일자/시간(t) and 1 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 일자/시간(t) and 3 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
방류량(ms) has unique valuesUnique
강우량(mm) has 7 (22.6%) zerosZeros
유입량(ms) has 4 (12.9%) zerosZeros

Reproduction

Analysis started2023-12-10 10:42:02.884926
Analysis finished2023-12-10 10:42:13.177939
Duration10.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
낙동강하굿둑
31 

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 31
100.0%

Length

2023-12-10T19:42:13.289868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:42:13.571101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 31
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190316
Minimum20190301
Maximum20190331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:13.771780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190301
5-th percentile20190302
Q120190308
median20190316
Q320190324
95-th percentile20190330
Maximum20190331
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)4.5032089 × 10-7
Kurtosis-1.2
Mean20190316
Median Absolute Deviation (MAD)8
Skewness0
Sum6.258998 × 108
Variance82.666667
MonotonicityStrictly increasing
2023-12-10T19:42:14.025281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190301 1
 
3.2%
20190302 1
 
3.2%
20190331 1
 
3.2%
20190330 1
 
3.2%
20190329 1
 
3.2%
20190328 1
 
3.2%
20190327 1
 
3.2%
20190326 1
 
3.2%
20190325 1
 
3.2%
20190324 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
20190301 1
3.2%
20190302 1
3.2%
20190303 1
3.2%
20190304 1
3.2%
20190305 1
3.2%
20190306 1
3.2%
20190307 1
3.2%
20190308 1
3.2%
20190309 1
3.2%
20190310 1
3.2%
ValueCountFrequency (%)
20190331 1
3.2%
20190330 1
3.2%
20190329 1
3.2%
20190328 1
3.2%
20190327 1
3.2%
20190326 1
3.2%
20190325 1
3.2%
20190324 1
3.2%
20190323 1
3.2%
20190322 1
3.2%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86903226
Minimum0.71
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:14.269399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.71
5-th percentile0.77
Q10.855
median0.88
Q30.91
95-th percentile0.925
Maximum0.94
Range0.23
Interquartile range (IQR)0.055

Descriptive statistics

Standard deviation0.052176293
Coefficient of variation (CV)0.060039536
Kurtosis1.8348844
Mean0.86903226
Median Absolute Deviation (MAD)0.03
Skewness-1.3335108
Sum26.94
Variance0.0027223656
MonotonicityNot monotonic
2023-12-10T19:42:14.549228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.91 5
16.1%
0.87 4
12.9%
0.88 4
12.9%
0.86 2
 
6.5%
0.92 2
 
6.5%
0.9 2
 
6.5%
0.89 2
 
6.5%
0.77 2
 
6.5%
0.84 1
 
3.2%
0.71 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
0.71 1
 
3.2%
0.77 2
6.5%
0.8 1
 
3.2%
0.81 1
 
3.2%
0.83 1
 
3.2%
0.84 1
 
3.2%
0.85 1
 
3.2%
0.86 2
6.5%
0.87 4
12.9%
0.88 4
12.9%
ValueCountFrequency (%)
0.94 1
 
3.2%
0.93 1
 
3.2%
0.92 2
 
6.5%
0.91 5
16.1%
0.9 2
 
6.5%
0.89 2
 
6.5%
0.88 4
12.9%
0.87 4
12.9%
0.86 2
 
6.5%
0.85 1
 
3.2%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97335484
Minimum0
Maximum10.2392
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:14.790504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0008
median0.0046
Q30.3515
95-th percentile6.2749
Maximum10.2392
Range10.2392
Interquartile range (IQR)0.3507

Descriptive statistics

Standard deviation2.4392132
Coefficient of variation (CV)2.5059856
Kurtosis9.6724802
Mean0.97335484
Median Absolute Deviation (MAD)0.0046
Skewness3.1596169
Sum30.174
Variance5.949761
MonotonicityNot monotonic
2023-12-10T19:42:15.022913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 7
22.6%
0.0012 4
 
12.9%
0.0004 1
 
3.2%
0.0846 1
 
3.2%
3.6989 1
 
3.2%
0.0029 1
 
3.2%
0.0046 1
 
3.2%
0.0233 1
 
3.2%
1.3438 1
 
3.2%
0.1028 1
 
3.2%
Other values (12) 12
38.7%
ValueCountFrequency (%)
0.0 7
22.6%
0.0004 1
 
3.2%
0.0012 4
12.9%
0.0027 1
 
3.2%
0.0029 1
 
3.2%
0.0036 1
 
3.2%
0.0046 1
 
3.2%
0.0233 1
 
3.2%
0.0309 1
 
3.2%
0.0376 1
 
3.2%
ValueCountFrequency (%)
10.2392 1
3.2%
8.8509 1
3.2%
3.6989 1
3.2%
2.1622 1
3.2%
1.7448 1
3.2%
1.3438 1
3.2%
1.0703 1
3.2%
0.523 1
3.2%
0.18 1
3.2%
0.1028 1
3.2%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.950548
Minimum0
Maximum139.783
Zeros4
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:15.366010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.742
median73.83
Q393.4325
95-th percentile117.763
Maximum139.783
Range139.783
Interquartile range (IQR)76.6905

Descriptive statistics

Standard deviation41.85376
Coefficient of variation (CV)0.66486729
Kurtosis-1.060091
Mean62.950548
Median Absolute Deviation (MAD)21.979
Skewness-0.32485807
Sum1951.467
Variance1751.7373
MonotonicityNot monotonic
2023-12-10T19:42:15.680789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 4
 
12.9%
1.702 1
 
3.2%
52.037 1
 
3.2%
18.549 1
 
3.2%
57.051 1
 
3.2%
73.83 1
 
3.2%
95.323 1
 
3.2%
76.365 1
 
3.2%
94.516 1
 
3.2%
122.041 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
0.0 4
12.9%
1.537 1
 
3.2%
1.702 1
 
3.2%
13.603 1
 
3.2%
14.935 1
 
3.2%
18.549 1
 
3.2%
51.851 1
 
3.2%
52.037 1
 
3.2%
57.051 1
 
3.2%
61.294 1
 
3.2%
ValueCountFrequency (%)
139.783 1
3.2%
122.041 1
3.2%
113.485 1
3.2%
103.59 1
3.2%
101.526 1
3.2%
98.827 1
3.2%
95.323 1
3.2%
94.516 1
3.2%
92.349 1
3.2%
91.305 1
3.2%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.091161
Minimum1.46
Maximum127.408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:15.920573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.46
5-th percentile1.669
Q118.231
median72.017
Q387.723
95-th percentile122.4895
Maximum127.408
Range125.948
Interquartile range (IQR)69.492

Descriptive statistics

Standard deviation39.801693
Coefficient of variation (CV)0.65151312
Kurtosis-1.182045
Mean61.091161
Median Absolute Deviation (MAD)22.449
Skewness-0.19369132
Sum1893.826
Variance1584.1748
MonotonicityNot monotonic
2023-12-10T19:42:16.199057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1.707 1
 
3.2%
1.637 1
 
3.2%
23.935 1
 
3.2%
51.671 1
 
3.2%
84.598 1
 
3.2%
68.421 1
 
3.2%
49.568 1
 
3.2%
121.305 1
 
3.2%
127.408 1
 
3.2%
66.672 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1.46 1
3.2%
1.637 1
3.2%
1.701 1
3.2%
1.707 1
3.2%
11.494 1
3.2%
12.98 1
3.2%
14.937 1
3.2%
17.523 1
3.2%
18.939 1
3.2%
23.935 1
3.2%
ValueCountFrequency (%)
127.408 1
3.2%
123.674 1
3.2%
121.305 1
3.2%
102.723 1
3.2%
101.75 1
3.2%
93.022 1
3.2%
89.507 1
3.2%
89.506 1
3.2%
85.94 1
3.2%
85.396 1
3.2%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302.33161
Minimum295.002
Maximum305.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:16.417267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum295.002
5-th percentile297.755
Q1301.6765
median302.835
Q3304.229
95-th percentile304.9265
Maximum305.625
Range10.623
Interquartile range (IQR)2.5525

Descriptive statistics

Standard deviation2.4121058
Coefficient of variation (CV)0.0079783447
Kurtosis1.7966255
Mean302.33161
Median Absolute Deviation (MAD)1.394
Skewness-1.3222168
Sum9372.28
Variance5.8182544
MonotonicityNot monotonic
2023-12-10T19:42:16.637521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
304.229 5
16.1%
302.372 4
12.9%
302.835 4
12.9%
301.908 2
 
6.5%
304.694 2
 
6.5%
303.764 2
 
6.5%
303.299 2
 
6.5%
297.755 2
 
6.5%
300.983 1
 
3.2%
295.002 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
295.002 1
 
3.2%
297.755 2
6.5%
299.136 1
 
3.2%
299.597 1
 
3.2%
300.52 1
 
3.2%
300.983 1
 
3.2%
301.445 1
 
3.2%
301.908 2
6.5%
302.372 4
12.9%
302.835 4
12.9%
ValueCountFrequency (%)
305.625 1
 
3.2%
305.159 1
 
3.2%
304.694 2
 
6.5%
304.229 5
16.1%
303.764 2
 
6.5%
303.299 2
 
6.5%
302.835 4
12.9%
302.372 4
12.9%
301.908 2
 
6.5%
301.445 1
 
3.2%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.416129
Minimum96.03
Maximum99.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:16.845364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96.03
5-th percentile96.93
Q198.205
median98.58
Q399.03
95-th percentile99.26
Maximum99.49
Range3.46
Interquartile range (IQR)0.825

Descriptive statistics

Standard deviation0.78349081
Coefficient of variation (CV)0.0079610001
Kurtosis1.8262641
Mean98.416129
Median Absolute Deviation (MAD)0.45
Skewness-1.3274127
Sum3050.9
Variance0.61385785
MonotonicityNot monotonic
2023-12-10T19:42:17.071227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
99.03 5
16.1%
98.43 4
12.9%
98.58 4
12.9%
98.28 2
 
6.5%
99.18 2
 
6.5%
98.88 2
 
6.5%
98.73 2
 
6.5%
96.93 2
 
6.5%
97.98 1
 
3.2%
96.03 1
 
3.2%
Other values (6) 6
19.4%
ValueCountFrequency (%)
96.03 1
 
3.2%
96.93 2
6.5%
97.38 1
 
3.2%
97.53 1
 
3.2%
97.83 1
 
3.2%
97.98 1
 
3.2%
98.13 1
 
3.2%
98.28 2
6.5%
98.43 4
12.9%
98.58 4
12.9%
ValueCountFrequency (%)
99.49 1
 
3.2%
99.34 1
 
3.2%
99.18 2
 
6.5%
99.03 5
16.1%
98.88 2
 
6.5%
98.73 2
 
6.5%
98.58 4
12.9%
98.43 4
12.9%
98.28 2
 
6.5%
98.13 1
 
3.2%

Interactions

2023-12-10T19:42:11.477678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:03.366168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:04.608968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:05.921411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:07.319259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:08.579558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:10.204291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:11.648368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:03.526044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:04.860789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:06.134426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:07.506004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:08.758672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:10.397807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:11.817331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:03.689256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:05.052223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:06.326660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:07.697692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:09.311257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:10.574214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.052534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:03.945977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:05.208401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:06.589236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:07.858492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:09.480167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:10.794268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.246500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:04.117254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:05.363607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:06.822532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:08.027393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:09.678650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:10.967609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.450964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:04.271502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:05.527367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:06.986400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:08.206593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:09.840777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:11.154132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:12.630874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:04.429377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:05.753013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:07.156306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:08.350357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:10.022429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:11.306045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:42:17.253444image/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)1.0000.4910.0000.4420.6840.5050.491
저수위(m)0.4911.0000.0000.0000.5241.0001.000
강우량(mm)0.0000.0001.0000.5450.3910.0000.000
유입량(ms)0.4420.0000.5451.0000.7920.0000.000
방류량(ms)0.6840.5240.3910.7921.0000.4780.524
저수량(백만m3)0.5051.0000.0000.0000.4781.0001.000
저수율0.4911.0000.0000.0000.5241.0001.000
2023-12-10T19:42:17.461535image/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)1.0000.6250.2650.5270.5600.6250.625
저수위(m)0.6251.0000.3760.6180.4121.0001.000
강우량(mm)0.2650.3761.0000.3690.3380.3760.376
유입량(ms)0.5270.6180.3691.0000.7850.6180.618
방류량(ms)0.5600.4120.3380.7851.0000.4120.412
저수량(백만m3)0.6251.0000.3760.6180.4121.0001.000
저수율0.6251.0000.3760.6180.4121.0001.000

Missing values

2023-12-10T19:42:12.857131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:42:13.093790image/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
0낙동강하굿둑201903010.870.01.7021.707302.37298.43
1낙동강하굿둑201903020.860.00.01.637301.90898.28
2낙동강하굿둑201903030.850.00.01.701301.44598.13
3낙동강하굿둑201903040.810.00.011.494299.59797.53
4낙동강하굿둑201903050.80.003613.60318.939299.13697.38
5낙동강하굿둑201903060.770.5231.53717.523297.75596.93
6낙동강하굿둑201903070.770.062714.93514.937297.75596.93
7낙동강하굿둑201903080.710.00270.012.98295.00296.03
8낙동강하굿둑201903090.840.070.6811.46300.98397.98
9낙동강하굿둑201903100.8910.239298.82772.017303.29998.73
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
21낙동강하굿둑201903220.920.1028113.485102.723304.69499.18
22낙동강하굿둑201903230.91.343882.25693.022303.76498.88
23낙동강하굿둑201903240.890.023361.29466.672303.29998.73
24낙동강하굿둑201903250.880.0122.041127.408302.83598.58
25낙동강하굿둑201903260.830.001294.516121.305300.5297.83
26낙동강하굿둑201903270.880.004676.36549.568302.83598.58
27낙동강하굿둑201903280.930.001295.32368.421305.15999.34
28낙동강하굿둑201903290.910.002973.8384.598304.22999.03
29낙동강하굿둑201903300.923.698957.05151.671304.69499.18
30낙동강하굿둑201903310.910.084618.54923.935304.22999.03