Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Categorical1
Numeric7

Alerts

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 저수위(m) and 4 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 방류량(ms) and 2 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
강우량(mm) has 87 (87.0%) zerosZeros
유입량(ms) has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:21:53.700563
Analysis finished2023-12-10 13:22:03.345002
Duration9.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군위
31 
남강
31 
대청
31 
밀양
7 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군위 31
31.0%
남강 31
31.0%
대청 31
31.0%
밀양 7
 
7.0%

Length

2023-12-10T22:22:03.506934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:22:03.712488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 31
31.0%
남강 31
31.0%
대청 31
31.0%
밀양 7
 
7.0%

일자/시간(t)
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190115
Minimum20190101
Maximum20190131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:03.911053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190101
5-th percentile20190102
Q120190107
median20190115
Q320190123
95-th percentile20190130
Maximum20190131
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5637709 × 10-7
Kurtosis-1.2602055
Mean20190115
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190115 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T22:22:04.126579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190101 4
 
4.0%
20190103 4
 
4.0%
20190104 4
 
4.0%
20190105 4
 
4.0%
20190106 4
 
4.0%
20190107 4
 
4.0%
20190102 4
 
4.0%
20190126 3
 
3.0%
20190122 3
 
3.0%
20190123 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190101 4
4.0%
20190102 4
4.0%
20190103 4
4.0%
20190104 4
4.0%
20190105 4
4.0%
20190106 4
4.0%
20190107 4
4.0%
20190108 3
3.0%
20190109 3
3.0%
20190110 3
3.0%
ValueCountFrequency (%)
20190131 3
3.0%
20190130 3
3.0%
20190129 3
3.0%
20190128 3
3.0%
20190127 3
3.0%
20190126 3
3.0%
20190125 3
3.0%
20190124 3
3.0%
20190123 3
3.0%
20190122 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.4447
Minimum40.75
Maximum200.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:04.359087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.75
5-th percentile40.7595
Q140.78
median75.37
Q3194.7975
95-th percentile200.5635
Maximum200.92
Range160.17
Interquartile range (IQR)154.0175

Descriptive statistics

Standard deviation68.671531
Coefficient of variation (CV)0.62177299
Kurtosis-1.7327832
Mean110.4447
Median Absolute Deviation (MAD)34.6
Skewness0.38132595
Sum11044.47
Variance4715.7791
MonotonicityNot monotonic
2023-12-10T22:22:04.608120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.78 11
 
11.0%
40.77 9
 
9.0%
40.76 6
 
6.0%
40.75 5
 
5.0%
195.42 1
 
1.0%
75.36 1
 
1.0%
75.27 1
 
1.0%
75.28 1
 
1.0%
75.3 1
 
1.0%
75.32 1
 
1.0%
Other values (63) 63
63.0%
ValueCountFrequency (%)
40.75 5
5.0%
40.76 6
6.0%
40.77 9
9.0%
40.78 11
11.0%
75.0 1
 
1.0%
75.02 1
 
1.0%
75.05 1
 
1.0%
75.06 1
 
1.0%
75.09 1
 
1.0%
75.1 1
 
1.0%
ValueCountFrequency (%)
200.92 1
1.0%
200.85 1
1.0%
200.78 1
1.0%
200.7 1
1.0%
200.63 1
1.0%
200.56 1
1.0%
200.49 1
1.0%
195.42 1
1.0%
195.39 1
1.0%
195.36 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.287538
Minimum0
Maximum7.6631
Zeros87
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:04.818298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.982745
Maximum7.6631
Range7.6631
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1391546
Coefficient of variation (CV)3.9617533
Kurtosis23.953743
Mean0.287538
Median Absolute Deviation (MAD)0
Skewness4.7655678
Sum28.7538
Variance1.2976732
MonotonicityNot monotonic
2023-12-10T22:22:05.064933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 87
87.0%
4.7634 1
 
1.0%
4.0 1
 
1.0%
0.0342 1
 
1.0%
5.382 1
 
1.0%
1.1075 1
 
1.0%
0.7582 1
 
1.0%
7.6631 1
 
1.0%
1.9773 1
 
1.0%
0.2879 1
 
1.0%
Other values (4) 4
 
4.0%
ValueCountFrequency (%)
0.0 87
87.0%
0.0342 1
 
1.0%
0.0525 1
 
1.0%
0.2853 1
 
1.0%
0.2879 1
 
1.0%
0.3562 1
 
1.0%
0.7582 1
 
1.0%
1.1075 1
 
1.0%
1.9773 1
 
1.0%
2.0862 1
 
1.0%
ValueCountFrequency (%)
7.6631 1
1.0%
5.382 1
1.0%
4.7634 1
1.0%
4.0 1
1.0%
2.0862 1
1.0%
1.9773 1
1.0%
1.1075 1
1.0%
0.7582 1
1.0%
0.3562 1
1.0%
0.2879 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.19745
Minimum0
Maximum22.173
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:05.531490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.19175
median14.0815
Q314.718
95-th percentile21.9457
Maximum22.173
Range22.173
Interquartile range (IQR)14.52625

Descriptive statistics

Standard deviation7.7074405
Coefficient of variation (CV)0.83799754
Kurtosis-1.4901154
Mean9.19745
Median Absolute Deviation (MAD)6.17
Skewness-0.080594319
Sum919.745
Variance59.404638
MonotonicityNot monotonic
2023-12-10T22:22:05.856369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.029 3
 
3.0%
0.254 2
 
2.0%
14.249 2
 
2.0%
14.305 2
 
2.0%
0.028 2
 
2.0%
14.802 1
 
1.0%
14.778 1
 
1.0%
7.707 1
 
1.0%
17.434 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.009 1
 
1.0%
0.012 1
 
1.0%
0.013 1
 
1.0%
0.027 1
 
1.0%
0.028 2
 
2.0%
0.029 3
 
3.0%
0.03 1
 
1.0%
0.032 1
 
1.0%
0.041 1
 
1.0%
ValueCountFrequency (%)
22.173 1
1.0%
22.086 1
1.0%
22.066 1
1.0%
22.044 1
1.0%
21.978 1
1.0%
21.944 1
1.0%
21.924 1
1.0%
21.876 1
1.0%
17.434 1
1.0%
17.428 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.84189
Minimum0.873
Maximum30.183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:06.229162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.873
5-th percentile0.89475
Q10.92875
median14.2405
Q329.042
95-th percentile29.3064
Maximum30.183
Range29.31
Interquartile range (IQR)28.11325

Descriptive statistics

Standard deviation11.699769
Coefficient of variation (CV)0.84524361
Kurtosis-1.518542
Mean13.84189
Median Absolute Deviation (MAD)13.316
Skewness0.20719795
Sum1384.189
Variance136.8846
MonotonicityNot monotonic
2023-12-10T22:22:06.558360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.929 2
 
2.0%
13.98 2
 
2.0%
0.924 2
 
2.0%
0.926 2
 
2.0%
14.249 2
 
2.0%
14.292 2
 
2.0%
14.305 2
 
2.0%
0.928 2
 
2.0%
14.236 2
 
2.0%
29.039 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.873 1
1.0%
0.883 1
1.0%
0.884 1
1.0%
0.889 1
1.0%
0.89 1
1.0%
0.895 1
1.0%
0.898 1
1.0%
0.9 1
1.0%
0.903 1
1.0%
0.904 1
1.0%
ValueCountFrequency (%)
30.183 1
1.0%
29.347 1
1.0%
29.323 1
1.0%
29.317 1
1.0%
29.314 1
1.0%
29.306 1
1.0%
29.282 1
1.0%
29.278 1
1.0%
29.264 1
1.0%
29.249 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406.32207
Minimum26.002
Maximum1112.149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:06.969696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.002
5-th percentile26.34415
Q127.765
median176.0198
Q31082.764
95-th percentile1107.1981
Maximum1112.149
Range1086.147
Interquartile range (IQR)1054.999

Descriptive statistics

Standard deviation467.64493
Coefficient of variation (CV)1.1509218
Kurtosis-1.3417773
Mean406.32207
Median Absolute Deviation (MAD)148.6838
Skewness0.78138206
Sum40632.207
Variance218691.78
MonotonicityNot monotonic
2023-12-10T22:22:07.341824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176.296 10
 
10.0%
176.02 8
 
8.0%
175.468 5
 
5.0%
175.744 5
 
5.0%
28.127 1
 
1.0%
1099.719 1
 
1.0%
1092.919 1
 
1.0%
1093.536 1
 
1.0%
1094.771 1
 
1.0%
1096.007 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
26.002 1
1.0%
26.0594 1
1.0%
26.1363 1
1.0%
26.194 1
1.0%
26.271 1
1.0%
26.348 1
1.0%
26.426 1
1.0%
26.503 1
1.0%
26.561 1
1.0%
26.619 1
1.0%
ValueCountFrequency (%)
1112.149 1
1.0%
1111.526 1
1.0%
1110.279 1
1.0%
1109.034 1
1.0%
1107.789 1
1.0%
1107.167 1
1.0%
1105.303 1
1.0%
1104.06 1
1.0%
1102.819 1
1.0%
1101.579 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.102
Minimum53.4
Maximum78.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:22:07.683349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.4
5-th percentile54.09
Q156.8
median57
Q373.325
95-th percentile77.61
Maximum78.5
Range25.1
Interquartile range (IQR)16.525

Descriptive statistics

Standard deviation8.9293521
Coefficient of variation (CV)0.14150664
Kurtosis-1.6187291
Mean63.102
Median Absolute Deviation (MAD)1.9
Skewness0.52424987
Sum6310.2
Variance79.733329
MonotonicityNot monotonic
2023-12-10T22:22:08.010258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56.8 11
 
11.0%
57.0 11
 
11.0%
56.9 10
 
10.0%
72.7 2
 
2.0%
74.4 2
 
2.0%
73.9 2
 
2.0%
73.7 2
 
2.0%
73.4 2
 
2.0%
73.1 2
 
2.0%
74.6 2
 
2.0%
Other values (53) 54
54.0%
ValueCountFrequency (%)
53.4 1
1.0%
53.5 1
1.0%
53.7 1
1.0%
53.8 1
1.0%
53.9 1
1.0%
54.1 1
1.0%
54.3 1
1.0%
54.4 1
1.0%
54.5 1
1.0%
54.7 1
1.0%
ValueCountFrequency (%)
78.5 1
1.0%
78.3 1
1.0%
78.1 1
1.0%
77.9 1
1.0%
77.8 1
1.0%
77.6 1
1.0%
77.4 1
1.0%
74.6 2
2.0%
74.5 1
1.0%
74.4 2
2.0%

Interactions

2023-12-10T22:22:01.609353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:54.112410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:55.336165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.494137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.685393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.628094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.217533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.734635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:54.396072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:55.573986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.669418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.821107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.769207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.496718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.867626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:54.541427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:55.731603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.830947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.965111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.257630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.752477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.075019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:54.705129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:55.889970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.012401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.108023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.483753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:00.979524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.300289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:54.893628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.030284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.153469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.243469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.703390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.143990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.432222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:55.043206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.213369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.319367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.382860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.822421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.368419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:02.562706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:55.180202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:56.356116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:57.461670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:58.516131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:59.955194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:22:01.492516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:22:08.178906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.7611.0001.0000.893
일자/시간(t)0.0001.0000.0000.1870.0000.0000.0000.608
저수위(m)1.0000.0001.0000.1680.8391.0001.0000.821
강우량(mm)0.0000.1870.1681.0000.3760.1680.1680.000
유입량(ms)0.7610.0000.8390.3761.0000.8390.8390.592
방류량(ms)1.0000.0001.0000.1680.8391.0001.0000.821
저수량(백만m3)1.0000.0001.0000.1680.8391.0001.0000.821
저수율0.8930.6080.8210.0000.5920.8210.8211.000
2023-12-10T22:22:08.409600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.4010.2160.0940.089-0.196-0.4980.000
저수위(m)-0.4011.000-0.179-0.620-0.514-0.4170.1050.995
강우량(mm)0.216-0.1791.0000.1400.1080.111-0.0070.000
유입량(ms)0.094-0.6200.1401.0000.8000.7950.4100.634
방류량(ms)0.089-0.5140.1080.8001.0000.9020.6210.995
저수량(백만m3)-0.196-0.4170.1110.7950.9021.0000.7580.995
저수율-0.4980.105-0.0070.4100.6210.7581.0000.887
댐이름0.0000.9950.0000.6340.9950.9950.8871.000

Missing values

2023-12-10T22:22:02.743435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:22:03.162503image/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
0군위20190101195.420.00.01.11128.12757.8
1군위20190102195.390.00.2810.97928.06657.6
2군위20190103195.360.00.1850.88328.00657.5
3군위20190104195.320.00.00.88427.92657.3
4군위20190105195.280.00.00.90427.84557.2
5군위20190106195.250.00.1940.8927.78557.1
6군위20190107195.210.00.00.87327.70556.9
7군위20190108195.170.00.00.89827.62556.7
8군위20190109195.140.00.210.90327.56556.6
9군위20190110195.10.00.00.90827.48656.4
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90대청2019012975.050.022.17329.2641079.39272.4
91대청2019013075.020.08.05829.3171077.55572.3
92대청2019013175.02.086215.07929.241076.33272.2
93밀양20190101200.920.00.0131.48757.7578.5
94밀양20190102200.850.00.0291.50157.62378.3
95밀양20190103200.780.00.0121.48257.49678.1
96밀양20190104200.70.00.01.55957.35177.9
97밀양20190105200.630.00.0661.53157.22477.8
98밀양20190106200.560.00.0411.50457.09877.6
99밀양20190107200.490.00.0571.51856.97177.4