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
저수위(m) is highly overall correlated with 유입량(ms) and 2 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms)High correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
유입량(ms) has unique valuesUnique
방류량(ms) has unique valuesUnique
강우량(mm) has 7 (22.6%) zerosZeros

Reproduction

Analysis started2023-12-10 10:42:37.299043
Analysis finished2023-12-10 10:42:47.397240
Duration10.1 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

2023-12-10T19:42:47.568439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:42:47.741395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 31
100.0%

일자/시간(t)
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190116
Minimum20190101
Maximum20190131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:47.911460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190102
Q120190108
median20190116
Q320190124
95-th percentile20190130
Maximum20190131
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)4.5032535 × 10-7
Kurtosis-1.2
Mean20190116
Median Absolute Deviation (MAD)8
Skewness0
Sum6.258936 × 108
Variance82.666667
MonotonicityStrictly increasing
2023-12-10T19:42:48.183434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190101 1
 
3.2%
20190102 1
 
3.2%
20190131 1
 
3.2%
20190130 1
 
3.2%
20190129 1
 
3.2%
20190128 1
 
3.2%
20190127 1
 
3.2%
20190126 1
 
3.2%
20190125 1
 
3.2%
20190124 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
20190101 1
3.2%
20190102 1
3.2%
20190103 1
3.2%
20190104 1
3.2%
20190105 1
3.2%
20190106 1
3.2%
20190107 1
3.2%
20190108 1
3.2%
20190109 1
3.2%
20190110 1
3.2%
ValueCountFrequency (%)
20190131 1
3.2%
20190130 1
3.2%
20190129 1
3.2%
20190128 1
3.2%
20190127 1
3.2%
20190126 1
3.2%
20190125 1
3.2%
20190124 1
3.2%
20190123 1
3.2%
20190122 1
3.2%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89419355
Minimum0.83
Maximum0.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:48.427079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.83
5-th percentile0.855
Q10.87
median0.89
Q30.92
95-th percentile0.945
Maximum0.95
Range0.12
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.030416464
Coefficient of variation (CV)0.034015526
Kurtosis-0.59908887
Mean0.89419355
Median Absolute Deviation (MAD)0.02
Skewness0.18682123
Sum27.72
Variance0.00092516129
MonotonicityNot monotonic
2023-12-10T19:42:48.715008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.88 6
19.4%
0.87 5
16.1%
0.92 4
12.9%
0.91 3
9.7%
0.89 3
9.7%
0.95 2
 
6.5%
0.94 2
 
6.5%
0.86 2
 
6.5%
0.85 1
 
3.2%
0.83 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
0.83 1
 
3.2%
0.85 1
 
3.2%
0.86 2
 
6.5%
0.87 5
16.1%
0.88 6
19.4%
0.89 3
9.7%
0.9 1
 
3.2%
0.91 3
9.7%
0.92 4
12.9%
0.93 1
 
3.2%
ValueCountFrequency (%)
0.95 2
 
6.5%
0.94 2
 
6.5%
0.93 1
 
3.2%
0.92 4
12.9%
0.91 3
9.7%
0.9 1
 
3.2%
0.89 3
9.7%
0.88 6
19.4%
0.87 5
16.1%
0.86 2
 
6.5%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24344194
Minimum0
Maximum4.0307
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:49.011270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0006
median0.0088
Q30.05085
95-th percentile1.5081
Maximum4.0307
Range4.0307
Interquartile range (IQR)0.05025

Descriptive statistics

Standard deviation0.8596052
Coefficient of variation (CV)3.5310482
Kurtosis15.063293
Mean0.24344194
Median Absolute Deviation (MAD)0.0088
Skewness3.9333592
Sum7.5467
Variance0.7389211
MonotonicityNot monotonic
2023-12-10T19:42:49.295083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 7
22.6%
0.0088 2
 
6.5%
0.0008 2
 
6.5%
0.01 1
 
3.2%
0.2463 1
 
3.2%
4.0307 1
 
3.2%
0.064 1
 
3.2%
0.0421 1
 
3.2%
0.0004 1
 
3.2%
0.0016 1
 
3.2%
Other values (13) 13
41.9%
ValueCountFrequency (%)
0.0 7
22.6%
0.0004 1
 
3.2%
0.0008 2
 
6.5%
0.0015 1
 
3.2%
0.0016 1
 
3.2%
0.0045 1
 
3.2%
0.005 1
 
3.2%
0.0075 1
 
3.2%
0.0088 2
 
6.5%
0.0096 1
 
3.2%
ValueCountFrequency (%)
4.0307 1
3.2%
2.7699 1
3.2%
0.2463 1
3.2%
0.0722 1
3.2%
0.0677 1
3.2%
0.064 1
3.2%
0.0609 1
3.2%
0.0596 1
3.2%
0.0421 1
3.2%
0.0302 1
3.2%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.635032
Minimum45.927
Maximum168.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:49.612823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.927
5-th percentile55.535
Q170.5375
median81.809
Q395.474
95-th percentile144.407
Maximum168.67
Range122.743
Interquartile range (IQR)24.9365

Descriptive statistics

Standard deviation27.537697
Coefficient of variation (CV)0.31785868
Kurtosis2.0828929
Mean86.635032
Median Absolute Deviation (MAD)11.98
Skewness1.3363954
Sum2685.686
Variance758.32474
MonotonicityNot monotonic
2023-12-10T19:42:49.858121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
75.089 1
 
3.2%
91.846 1
 
3.2%
139.095 1
 
3.2%
99.102 1
 
3.2%
82.855 1
 
3.2%
149.719 1
 
3.2%
107.393 1
 
3.2%
116.441 1
 
3.2%
89.726 1
 
3.2%
63.582 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
45.927 1
3.2%
52.014 1
3.2%
59.056 1
3.2%
59.797 1
3.2%
60.676 1
3.2%
62.552 1
3.2%
63.582 1
3.2%
69.829 1
3.2%
71.246 1
3.2%
73.598 1
3.2%
ValueCountFrequency (%)
168.67 1
3.2%
149.719 1
3.2%
139.095 1
3.2%
116.441 1
3.2%
107.393 1
3.2%
103.087 1
3.2%
99.445 1
3.2%
99.102 1
3.2%
91.846 1
3.2%
91.216 1
3.2%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.421484
Minimum48.795
Maximum141.745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:50.189872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.795
5-th percentile56.33
Q168.5085
median76.919
Q3102.423
95-th percentile130.935
Maximum141.745
Range92.95
Interquartile range (IQR)33.9145

Descriptive statistics

Standard deviation24.692115
Coefficient of variation (CV)0.28906212
Kurtosis-0.35173985
Mean85.421484
Median Absolute Deviation (MAD)10.475
Skewness0.7796503
Sum2648.066
Variance609.70055
MonotonicityNot monotonic
2023-12-10T19:42:50.461410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
69.724 1
 
3.2%
48.795 1
 
3.2%
128.327 1
 
3.2%
93.725 1
 
3.2%
109.802 1
 
3.2%
133.543 1
 
3.2%
107.393 1
 
3.2%
111.059 1
 
3.2%
73.592 1
 
3.2%
58.223 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
48.795 1
3.2%
55.314 1
3.2%
57.346 1
3.2%
58.223 1
3.2%
66.444 1
3.2%
67.385 1
3.2%
67.916 1
3.2%
68.16 1
3.2%
68.857 1
3.2%
69.724 1
3.2%
ValueCountFrequency (%)
141.745 1
3.2%
133.543 1
3.2%
128.327 1
3.2%
123.508 1
3.2%
113.851 1
3.2%
111.059 1
3.2%
109.802 1
3.2%
107.393 1
3.2%
97.453 1
3.2%
95.899 1
3.2%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.49574
Minimum300.52
Maximum306.092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:50.683154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300.52
5-th percentile301.6765
Q1302.372
median303.299
Q3304.694
95-th percentile305.8585
Maximum306.092
Range5.572
Interquartile range (IQR)2.322

Descriptive statistics

Standard deviation1.412979
Coefficient of variation (CV)0.0046556797
Kurtosis-0.60063762
Mean303.49574
Median Absolute Deviation (MAD)0.93
Skewness0.19153349
Sum9408.368
Variance1.9965095
MonotonicityNot monotonic
2023-12-10T19:42:50.891853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
302.835 6
19.4%
302.372 5
16.1%
304.694 4
12.9%
304.229 3
9.7%
303.299 3
9.7%
306.092 2
 
6.5%
305.625 2
 
6.5%
301.908 2
 
6.5%
301.445 1
 
3.2%
300.52 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
300.52 1
 
3.2%
301.445 1
 
3.2%
301.908 2
 
6.5%
302.372 5
16.1%
302.835 6
19.4%
303.299 3
9.7%
303.764 1
 
3.2%
304.229 3
9.7%
304.694 4
12.9%
305.159 1
 
3.2%
ValueCountFrequency (%)
306.092 2
 
6.5%
305.625 2
 
6.5%
305.159 1
 
3.2%
304.694 4
12.9%
304.229 3
9.7%
303.764 1
 
3.2%
303.299 3
9.7%
302.835 6
19.4%
302.372 5
16.1%
301.908 2
 
6.5%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.791935
Minimum97.8
Maximum99.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-10T19:42:51.086931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97.8
5-th percentile98.19
Q198.43
median98.7
Q399.2
95-th percentile99.565
Maximum99.64
Range1.84
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation0.46416534
Coefficient of variation (CV)0.0046984133
Kurtosis-0.56063577
Mean98.791935
Median Absolute Deviation (MAD)0.3
Skewness0.17605337
Sum3062.55
Variance0.21544946
MonotonicityNot monotonic
2023-12-10T19:42:51.323448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
99.2 4
12.9%
98.43 3
9.7%
98.6 3
9.7%
98.58 3
9.7%
98.4 2
 
6.5%
99.64 2
 
6.5%
99.0 2
 
6.5%
98.7 2
 
6.5%
99.49 2
 
6.5%
98.28 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
97.8 1
 
3.2%
98.1 1
 
3.2%
98.28 1
 
3.2%
98.3 1
 
3.2%
98.4 2
6.5%
98.43 3
9.7%
98.58 3
9.7%
98.6 3
9.7%
98.7 2
6.5%
98.73 1
 
3.2%
ValueCountFrequency (%)
99.64 2
6.5%
99.49 2
6.5%
99.34 1
 
3.2%
99.2 4
12.9%
99.03 1
 
3.2%
99.0 2
6.5%
98.88 1
 
3.2%
98.73 1
 
3.2%
98.7 2
6.5%
98.6 3
9.7%

Interactions

2023-12-10T19:42:45.272417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:37.662894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:39.197725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:40.511218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:41.684936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:42.945769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:44.024858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:45.445523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:37.851493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:39.407772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:40.694224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:41.853400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:43.078762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:44.184356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:45.636295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:38.042230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:39.585140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:40.850920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:42.002513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:43.225316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:44.424889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:45.817929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:38.252824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:39.779456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:41.031627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:42.175356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:43.412214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:44.598441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:45.974623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:38.456973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:40.023103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:41.189127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:42.351134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:43.595302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:44.766794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:46.112403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:38.647104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:40.202236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:41.344962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:42.578197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:43.728397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:44.911165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:46.651851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:38.854884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:40.361211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:41.497808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:42.773296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:43.863724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:42:45.090288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:42:51.492733image/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)1.0000.3420.1930.5920.3300.3000.280
저수위(m)0.3421.0000.8180.6680.4060.9980.999
강우량(mm)0.1930.8181.0000.8920.6420.9470.818
유입량(ms)0.5920.6680.8921.0000.7480.8080.639
방류량(ms)0.3300.4060.6420.7481.0000.6120.336
저수량(백만m3)0.3000.9980.9470.8080.6121.0000.999
저수율0.2800.9990.8180.6390.3360.9991.000
2023-12-10T19:42:51.707542image/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)1.0000.2270.1960.2080.2380.2270.218
저수위(m)0.2271.0000.1470.6800.3281.0000.995
강우량(mm)0.1960.1471.000-0.0870.1060.1470.150
유입량(ms)0.2080.680-0.0871.0000.6210.6800.674
방류량(ms)0.2380.3280.1060.6211.0000.3280.343
저수량(백만m3)0.2271.0000.1470.6800.3281.0000.995
저수율0.2180.9950.1500.6740.3430.9951.000

Missing values

2023-12-10T19:42:46.912090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:42:47.299170image/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
0낙동강하굿둑201901010.870.0175.08969.724302.37298.43
1낙동강하굿둑201901020.950.091.84648.795306.09299.64
2낙동강하굿둑201901030.940.008873.59879.0305.62599.49
3낙동강하굿둑201901040.880.091.216123.508302.83598.6
4낙동강하굿둑201901050.860.007585.1795.899301.90898.3
5낙동강하굿둑201901060.880.008879.58968.857302.83598.58
6낙동강하굿둑201901070.870.000862.55267.916302.37298.43
7낙동강하굿둑201901080.860.004581.80987.177301.90898.28
8낙동강하굿둑201901090.880.088.14177.409302.83598.58
9낙동강하굿둑201901100.880.019471.24671.246302.83598.6
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
21낙동강하굿둑201901220.830.001559.05669.762300.5297.8
22낙동강하굿둑201901230.870.000878.78157.346302.37298.4
23낙동강하굿둑201901240.880.067763.58258.223302.83598.6
24낙동강하굿둑201901250.910.001689.72673.592304.22999.0
25낙동강하굿둑201901260.920.0004116.441111.059304.69499.2
26낙동강하굿둑201901270.920.0107.393107.393304.69499.2
27낙동강하굿둑201901280.950.0421149.719133.543306.09299.64
28낙동강하굿둑201901290.90.06482.855109.802303.76498.88
29낙동강하굿둑201901300.910.099.10293.725304.22999.03
30낙동강하굿둑201901310.934.0307139.095128.327305.15999.34