Overview

Dataset statistics

Number of variables8
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory75.4 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
강우량(mm) is highly overall correlated with 유입량(ms) and 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 1 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 1 other fieldsHigh 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 5 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-10 10:41:11.002854
Analysis finished2023-12-10 10:41:21.733795
Duration10.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
낙동강하굿둑
30 

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 30
100.0%

Length

2023-12-10T19:41:21.855166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:22.042442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 30
100.0%

일자/시간(t)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190616
Minimum20190601
Maximum20190630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:22.221141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190601
5-th percentile20190602
Q120190608
median20190616
Q320190623
95-th percentile20190629
Maximum20190630
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)4.3601486 × 10-7
Kurtosis-1.2
Mean20190616
Median Absolute Deviation (MAD)7.5
Skewness0
Sum6.0571846 × 108
Variance77.5
MonotonicityStrictly increasing
2023-12-10T19:41:22.463373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190601 1
 
3.3%
20190617 1
 
3.3%
20190630 1
 
3.3%
20190629 1
 
3.3%
20190628 1
 
3.3%
20190627 1
 
3.3%
20190626 1
 
3.3%
20190625 1
 
3.3%
20190624 1
 
3.3%
20190623 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
20190601 1
3.3%
20190602 1
3.3%
20190603 1
3.3%
20190604 1
3.3%
20190605 1
3.3%
20190606 1
3.3%
20190607 1
3.3%
20190608 1
3.3%
20190609 1
3.3%
20190610 1
3.3%
ValueCountFrequency (%)
20190630 1
3.3%
20190629 1
3.3%
20190628 1
3.3%
20190627 1
3.3%
20190626 1
3.3%
20190625 1
3.3%
20190624 1
3.3%
20190623 1
3.3%
20190622 1
3.3%
20190621 1
3.3%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86066667
Minimum0.66
Maximum0.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:22.703934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile0.7245
Q10.8425
median0.87
Q30.8975
95-th percentile0.9455
Maximum0.96
Range0.3
Interquartile range (IQR)0.055

Descriptive statistics

Standard deviation0.066743634
Coefficient of variation (CV)0.077548761
Kurtosis2.21908
Mean0.86066667
Median Absolute Deviation (MAD)0.03
Skewness-1.3231812
Sum25.82
Variance0.0044547126
MonotonicityNot monotonic
2023-12-10T19:41:22.909458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.86 5
16.7%
0.87 4
13.3%
0.92 3
 
10.0%
0.89 3
 
10.0%
0.84 2
 
6.7%
0.96 1
 
3.3%
0.94 1
 
3.3%
0.95 1
 
3.3%
0.88 1
 
3.3%
0.91 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
0.66 1
 
3.3%
0.72 1
 
3.3%
0.73 1
 
3.3%
0.78 1
 
3.3%
0.82 1
 
3.3%
0.83 1
 
3.3%
0.84 2
 
6.7%
0.85 1
 
3.3%
0.86 5
16.7%
0.87 4
13.3%
ValueCountFrequency (%)
0.96 1
 
3.3%
0.95 1
 
3.3%
0.94 1
 
3.3%
0.92 3
10.0%
0.91 1
 
3.3%
0.9 1
 
3.3%
0.89 3
10.0%
0.88 1
 
3.3%
0.87 4
13.3%
0.86 5
16.7%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8551567
Minimum0
Maximum46.0992
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:23.134325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.004725
median0.10185
Q33.1954
95-th percentile26.080165
Maximum46.0992
Range46.0992
Interquartile range (IQR)3.190675

Descriptive statistics

Standard deviation10.684178
Coefficient of variation (CV)2.2005836
Kurtosis7.7214627
Mean4.8551567
Median Absolute Deviation (MAD)0.10185
Skewness2.7482586
Sum145.6547
Variance114.15166
MonotonicityNot monotonic
2023-12-10T19:41:23.366324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 5
 
16.7%
0.0128 1
 
3.3%
0.7718 1
 
3.3%
0.0985 1
 
3.3%
29.6308 1
 
3.3%
4.4673 1
 
3.3%
21.7405 1
 
3.3%
46.0992 1
 
3.3%
0.006 1
 
3.3%
0.0019 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0.0 5
16.7%
0.0004 1
 
3.3%
0.0019 1
 
3.3%
0.0043 1
 
3.3%
0.006 1
 
3.3%
0.009 1
 
3.3%
0.0128 1
 
3.3%
0.025 1
 
3.3%
0.0457 1
 
3.3%
0.046 1
 
3.3%
ValueCountFrequency (%)
46.0992 1
3.3%
29.6308 1
3.3%
21.7405 1
3.3%
18.2131 1
3.3%
12.2198 1
3.3%
4.7139 1
3.3%
4.4673 1
3.3%
3.5599 1
3.3%
2.1019 1
3.3%
0.7718 1
3.3%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.8179
Minimum70.704
Maximum1104.105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:23.687201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70.704
5-th percentile77.4894
Q197.289
median120.3175
Q3153.9985
95-th percentile768.3287
Maximum1104.105
Range1033.401
Interquartile range (IQR)56.7095

Descriptive statistics

Standard deviation245.65411
Coefficient of variation (CV)1.148894
Kurtosis6.3469635
Mean213.8179
Median Absolute Deviation (MAD)29.2355
Skewness2.6089944
Sum6414.537
Variance60345.94
MonotonicityNot monotonic
2023-12-10T19:41:23.894407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
90.239 1
 
3.3%
94.334 1
 
3.3%
810.707 1
 
3.3%
522.595 1
 
3.3%
716.533 1
 
3.3%
1104.105 1
 
3.3%
233.843 1
 
3.3%
70.704 1
 
3.3%
110.316 1
 
3.3%
113.689 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
70.704 1
3.3%
75.999 1
3.3%
79.311 1
3.3%
90.239 1
3.3%
91.925 1
3.3%
94.334 1
3.3%
95.45 1
3.3%
96.16 1
3.3%
100.676 1
3.3%
106.979 1
3.3%
ValueCountFrequency (%)
1104.105 1
3.3%
810.707 1
3.3%
716.533 1
3.3%
522.595 1
3.3%
256.425 1
3.3%
233.843 1
3.3%
184.062 1
3.3%
154.237 1
3.3%
153.283 1
3.3%
152.954 1
3.3%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.8998
Minimum69.35
Maximum1194.831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:24.122440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum69.35
5-th percentile90.9656
Q1105.379
median123.014
Q3156.068
95-th percentile774.34585
Maximum1194.831
Range1125.481
Interquartile range (IQR)50.689

Descriptive statistics

Standard deviation258.13722
Coefficient of variation (CV)1.1846602
Kurtosis7.4672106
Mean217.8998
Median Absolute Deviation (MAD)25.486
Skewness2.7754423
Sum6536.994
Variance66634.823
MonotonicityNot monotonic
2023-12-10T19:41:24.352379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
106.336 1
 
3.3%
105.06 1
 
3.3%
847.735 1
 
3.3%
549.176 1
 
3.3%
684.648 1
 
3.3%
1194.831 1
 
3.3%
196.339 1
 
3.3%
97.48 1
 
3.3%
94.245 1
 
3.3%
129.769 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
69.35 1
3.3%
90.071 1
3.3%
92.059 1
3.3%
94.245 1
3.3%
97.311 1
3.3%
97.48 1
3.3%
98.351 1
3.3%
105.06 1
3.3%
106.336 1
3.3%
111.157 1
3.3%
ValueCountFrequency (%)
1194.831 1
3.3%
847.735 1
3.3%
684.648 1
3.3%
549.176 1
3.3%
207.972 1
3.3%
207.1 1
3.3%
196.339 1
3.3%
157.107 1
3.3%
152.951 1
3.3%
148.452 1
3.3%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.94707
Minimum292.719
Maximum306.558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:24.586241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum292.719
5-th percentile295.6661
Q1301.0985
median302.372
Q3303.64775
95-th percentile305.88185
Maximum306.558
Range13.839
Interquartile range (IQR)2.54925

Descriptive statistics

Standard deviation3.0813383
Coefficient of variation (CV)0.010204895
Kurtosis2.1711238
Mean301.94707
Median Absolute Deviation (MAD)1.389
Skewness-1.3058216
Sum9058.412
Variance9.4946454
MonotonicityNot monotonic
2023-12-10T19:41:24.846211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
301.908 5
16.7%
302.372 4
13.3%
304.694 3
 
10.0%
303.299 3
 
10.0%
300.983 2
 
6.7%
306.558 1
 
3.3%
305.625 1
 
3.3%
306.092 1
 
3.3%
302.835 1
 
3.3%
304.229 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
292.719 1
 
3.3%
295.46 1
 
3.3%
295.918 1
 
3.3%
298.215 1
 
3.3%
300.059 1
 
3.3%
300.52 1
 
3.3%
300.983 2
 
6.7%
301.445 1
 
3.3%
301.908 5
16.7%
302.372 4
13.3%
ValueCountFrequency (%)
306.558 1
 
3.3%
306.092 1
 
3.3%
305.625 1
 
3.3%
304.694 3
10.0%
304.229 1
 
3.3%
303.764 1
 
3.3%
303.299 3
10.0%
302.835 1
 
3.3%
302.372 4
13.3%
301.908 5
16.7%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.291333
Minimum95.29
Maximum99.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:25.116574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.29
5-th percentile96.2475
Q198.0175
median98.43
Q398.8425
95-th percentile99.5725
Maximum99.79
Range4.5
Interquartile range (IQR)0.825

Descriptive statistics

Standard deviation1.0015083
Coefficient of variation (CV)0.010189182
Kurtosis2.1860817
Mean98.291333
Median Absolute Deviation (MAD)0.45
Skewness-1.3100574
Sum2948.74
Variance1.0030189
MonotonicityNot monotonic
2023-12-10T19:41:25.319193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
98.28 5
16.7%
98.43 4
13.3%
99.18 3
 
10.0%
98.73 3
 
10.0%
97.98 2
 
6.7%
99.79 1
 
3.3%
99.49 1
 
3.3%
99.64 1
 
3.3%
98.58 1
 
3.3%
99.03 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
95.29 1
 
3.3%
96.18 1
 
3.3%
96.33 1
 
3.3%
97.08 1
 
3.3%
97.68 1
 
3.3%
97.83 1
 
3.3%
97.98 2
 
6.7%
98.13 1
 
3.3%
98.28 5
16.7%
98.43 4
13.3%
ValueCountFrequency (%)
99.79 1
 
3.3%
99.64 1
 
3.3%
99.49 1
 
3.3%
99.18 3
10.0%
99.03 1
 
3.3%
98.88 1
 
3.3%
98.73 3
10.0%
98.58 1
 
3.3%
98.43 4
13.3%
98.28 5
16.7%

Interactions

2023-12-10T19:41:20.056195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.443971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.857534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.133675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.587209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.907301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.750567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.312740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.624906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.043789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.336142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.776661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.073098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.951719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.487474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.786505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.241132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.577959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.999401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.239490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.105341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.636142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:11.941257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.392929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:14.850427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.250005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.466972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.249377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.778778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.111625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.603951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.091476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.393017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.701347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.536324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:20.933432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.308932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.802072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.283731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.608985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:17.904265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.701726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:21.110551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:12.586032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:13.981450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:15.447578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:16.772732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:18.105587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:19.882994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:41:25.585664image/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)1.0000.5060.0000.4890.4890.6030.480
저수위(m)0.5061.0000.4160.8950.8630.9290.948
강우량(mm)0.0000.4161.0000.8050.8050.7550.738
유입량(ms)0.4890.8950.8051.0000.9980.9390.991
방류량(ms)0.4890.8630.8050.9981.0000.9060.975
저수량(백만m3)0.6030.9290.7550.9390.9061.0001.000
저수율0.4800.9480.7380.9910.9751.0001.000
2023-12-10T19:41:25.813313image/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)1.000-0.3950.4600.2810.291-0.395-0.395
저수위(m)-0.3951.0000.007-0.057-0.2351.0001.000
강우량(mm)0.4600.0071.0000.5870.6070.0070.007
유입량(ms)0.281-0.0570.5871.0000.865-0.057-0.057
방류량(ms)0.291-0.2350.6070.8651.000-0.235-0.235
저수량(백만m3)-0.3951.0000.007-0.057-0.2351.0001.000
저수율-0.3951.0000.007-0.057-0.2351.0001.000

Missing values

2023-12-10T19:41:21.341837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:41:21.638100image/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
0낙동강하굿둑201906010.860.012890.239106.336301.90898.28
1낙동강하굿둑201906020.860.009152.954152.951301.90898.28
2낙동강하굿둑201906030.830.075.99992.059300.5297.83
3낙동강하굿둑201906040.860.0114.41898.351301.90898.28
4낙동강하굿둑201906050.880.0004128.823118.091302.83598.58
5낙동강하굿둑201906060.8712.2198151.743157.107302.37298.43
6낙동강하굿둑201906070.9618.2131256.425207.972306.55899.79
7낙동강하굿둑201906080.860.046153.283207.1301.90898.28
8낙동강하굿둑201906090.940.0184.062141.038305.62599.49
9낙동강하굿둑201906100.950.4538128.334122.933306.09299.64
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
20낙동강하굿둑201906210.924.7139154.237121.994304.69499.18
21낙동강하굿둑201906220.870.5897121.572148.452302.37298.43
22낙동강하굿둑201906230.840.2144113.689129.769300.98397.98
23낙동강하굿둑201906240.870.0019110.31694.245302.37298.43
24낙동강하굿둑201906250.820.00670.70497.48300.05997.68
25낙동강하굿둑201906260.8946.0992233.843196.339303.29998.73
26낙동강하굿둑201906270.7221.74051104.1051194.831295.4696.18
27낙동강하굿둑201906280.784.4673716.533684.648298.21597.08
28낙동강하굿둑201906290.7329.6308522.595549.176295.91896.33
29낙동강하굿둑201906300.660.0985810.707847.735292.71995.29