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 유입량(ms) and 3 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
강우량(mm) has 64 (64.0%) zerosZeros
유입량(ms) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:21:06.786717
Analysis finished2023-12-10 13:21:16.019352
Duration9.23 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
30 
남강
30 
대청
30 
밀양
10 

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 30
30.0%
남강 30
30.0%
대청 30
30.0%
밀양 10
 
10.0%

Length

2023-12-10T22:21:16.147691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:16.297179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군위 30
30.0%
남강 30
30.0%
대청 30
30.0%
밀양 10
 
10.0%

일자/시간(t)
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190414
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:16.610396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190402
Q120190407
median20190414
Q320190422
95-th percentile20190429
Maximum20190430
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3750842 × 10-7
Kurtosis-1.2329291
Mean20190414
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0190414 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T22:21:16.858027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190401 4
 
4.0%
20190403 4
 
4.0%
20190404 4
 
4.0%
20190405 4
 
4.0%
20190406 4
 
4.0%
20190407 4
 
4.0%
20190408 4
 
4.0%
20190409 4
 
4.0%
20190410 4
 
4.0%
20190402 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190401 4
4.0%
20190402 4
4.0%
20190403 4
4.0%
20190404 4
4.0%
20190405 4
4.0%
20190406 4
4.0%
20190407 4
4.0%
20190408 4
4.0%
20190409 4
4.0%
20190410 4
4.0%
ValueCountFrequency (%)
20190430 3
3.0%
20190429 3
3.0%
20190428 3
3.0%
20190427 3
3.0%
20190426 3
3.0%
20190425 3
3.0%
20190424 3
3.0%
20190423 3
3.0%
20190422 3
3.0%
20190421 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.7835
Minimum40.35
Maximum196.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:17.078228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.35
5-th percentile40.43
Q140.6375
median74.305
Q3192.335
95-th percentile196.633
Maximum196.92
Range156.57
Interquartile range (IQR)151.6975

Descriptive statistics

Standard deviation68.25599
Coefficient of variation (CV)0.61060881
Kurtosis-1.8006457
Mean111.7835
Median Absolute Deviation (MAD)33.86
Skewness0.30240578
Sum11178.35
Variance4658.8802
MonotonicityNot monotonic
2023-12-10T22:21:17.443558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.45 5
 
5.0%
40.43 4
 
4.0%
74.32 3
 
3.0%
74.31 3
 
3.0%
74.29 3
 
3.0%
74.17 2
 
2.0%
192.01 2
 
2.0%
40.46 2
 
2.0%
40.63 2
 
2.0%
40.52 2
 
2.0%
Other values (65) 72
72.0%
ValueCountFrequency (%)
40.35 1
 
1.0%
40.38 1
 
1.0%
40.39 1
 
1.0%
40.41 1
 
1.0%
40.43 4
4.0%
40.44 2
 
2.0%
40.45 5
5.0%
40.46 2
 
2.0%
40.47 1
 
1.0%
40.49 1
 
1.0%
ValueCountFrequency (%)
196.92 1
1.0%
196.86 1
1.0%
196.81 1
1.0%
196.75 1
1.0%
196.69 1
1.0%
196.63 1
1.0%
196.58 1
1.0%
196.51 1
1.0%
196.45 2
2.0%
192.67 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct37
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.07914
Minimum0
Maximum41.1707
Zeros64
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:17.752468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.776825
95-th percentile21.336305
Maximum41.1707
Range41.1707
Interquartile range (IQR)0.776825

Descriptive statistics

Standard deviation7.5671233
Coefficient of variation (CV)2.4575444
Kurtosis10.392187
Mean3.07914
Median Absolute Deviation (MAD)0
Skewness3.1728503
Sum307.914
Variance57.261354
MonotonicityNot monotonic
2023-12-10T22:21:17.979208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.0 64
64.0%
8.4653 1
 
1.0%
2.6713 1
 
1.0%
0.2484 1
 
1.0%
41.1707 1
 
1.0%
0.3412 1
 
1.0%
29.8244 1
 
1.0%
6.2114 1
 
1.0%
0.8171 1
 
1.0%
13.1274 1
 
1.0%
Other values (27) 27
27.0%
ValueCountFrequency (%)
0.0 64
64.0%
0.0295 1
 
1.0%
0.0342 1
 
1.0%
0.0426 1
 
1.0%
0.0521 1
 
1.0%
0.055 1
 
1.0%
0.1427 1
 
1.0%
0.1894 1
 
1.0%
0.2484 1
 
1.0%
0.3412 1
 
1.0%
ValueCountFrequency (%)
41.1707 1
1.0%
32.0134 1
1.0%
29.8244 1
1.0%
28.473 1
1.0%
25.2903 1
1.0%
21.1282 1
1.0%
13.1274 1
1.0%
13.0406 1
1.0%
9.7537 1
1.0%
9.5909 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.19959
Minimum0
Maximum152.351
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:18.206900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0868
Q10.4995
median14.4055
Q326.673
95-th percentile68.5442
Maximum152.351
Range152.351
Interquartile range (IQR)26.1735

Descriptive statistics

Standard deviation25.716519
Coefficient of variation (CV)1.2731208
Kurtosis6.9694457
Mean20.19959
Median Absolute Deviation (MAD)13.8985
Skewness2.2283805
Sum2019.959
Variance661.33934
MonotonicityNot monotonic
2023-12-10T22:21:18.473669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.0%
0.706 2
 
2.0%
0.09 1
 
1.0%
15.412 1
 
1.0%
28.888 1
 
1.0%
36.399 1
 
1.0%
43.08 1
 
1.0%
63.982 1
 
1.0%
77.592 1
 
1.0%
97.418 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.048 1
1.0%
0.056 1
1.0%
0.083 1
1.0%
0.087 1
1.0%
0.089 1
1.0%
0.09 1
1.0%
0.101 1
1.0%
0.104 1
1.0%
0.25 1
1.0%
ValueCountFrequency (%)
152.351 1
1.0%
97.418 1
1.0%
83.409 1
1.0%
83.376 1
1.0%
77.592 1
1.0%
68.068 1
1.0%
65.849 1
1.0%
65.393 1
1.0%
63.982 1
1.0%
51.938 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.35577
Minimum1.032
Maximum104.287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:18.739021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.032
5-th percentile1.1067
Q11.12775
median13.8415
Q329.38575
95-th percentile37.67315
Maximum104.287
Range103.255
Interquartile range (IQR)28.258

Descriptive statistics

Standard deviation18.823386
Coefficient of variation (CV)1.0845607
Kurtosis5.0045777
Mean17.35577
Median Absolute Deviation (MAD)12.726
Skewness1.8179712
Sum1735.577
Variance354.31985
MonotonicityNot monotonic
2023-12-10T22:21:18.983879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.118 3
 
3.0%
13.683 2
 
2.0%
1.125 2
 
2.0%
1.119 2
 
2.0%
29.442 1
 
1.0%
29.603 1
 
1.0%
28.888 1
 
1.0%
29.484 1
 
1.0%
29.256 1
 
1.0%
29.465 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
1.032 1
1.0%
1.081 1
1.0%
1.082 1
1.0%
1.085 1
1.0%
1.101 1
1.0%
1.107 1
1.0%
1.108 1
1.0%
1.11 1
1.0%
1.111 1
1.0%
1.112 1
1.0%
ValueCountFrequency (%)
104.287 1
1.0%
74.377 1
1.0%
74.239 1
1.0%
73.843 1
1.0%
57.664 1
1.0%
36.621 1
1.0%
36.03 1
1.0%
31.596 1
1.0%
31.202 1
1.0%
31.192 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371.49425
Minimum21.626
Maximum1043.008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:19.579640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.626
5-th percentile21.81645
Q122.53575
median167.0175
Q31021.8277
95-th percentile1035.222
Maximum1043.008
Range1021.382
Interquartile range (IQR)999.292

Descriptive statistics

Standard deviation437.47945
Coefficient of variation (CV)1.177621
Kurtosis-1.2587923
Mean371.49425
Median Absolute Deviation (MAD)144.5755
Skewness0.82782326
Sum37149.425
Variance191388.27
MonotonicityNot monotonic
2023-12-10T22:21:19.805902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167.155 5
 
5.0%
166.605 4
 
4.0%
1035.222 3
 
3.0%
1034.624 3
 
3.0%
1033.43 3
 
3.0%
1026.281 2
 
2.0%
21.714 2
 
2.0%
167.43 2
 
2.0%
172.139 2
 
2.0%
169.085 2
 
2.0%
Other values (65) 72
72.0%
ValueCountFrequency (%)
21.626 1
1.0%
21.661 1
1.0%
21.714 2
2.0%
21.749 1
1.0%
21.82 1
1.0%
21.855 1
1.0%
21.944 1
1.0%
22.014 1
1.0%
22.103 1
1.0%
22.174 2
2.0%
ValueCountFrequency (%)
1043.008 1
 
1.0%
1040.009 1
 
1.0%
1037.016 1
 
1.0%
1035.82 1
 
1.0%
1035.222 3
3.0%
1034.624 3
3.0%
1034.027 1
 
1.0%
1033.43 3
3.0%
1032.833 1
 
1.0%
1032.237 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.678
Minimum44.4
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:20.086321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.4
5-th percentile44.795
Q146.275
median54.7
Q368.8
95-th percentile69.5
Maximum70
Range25.6
Interquartile range (IQR)22.525

Descriptive statistics

Standard deviation9.9322918
Coefficient of variation (CV)0.17220243
Kurtosis-1.65952
Mean57.678
Median Absolute Deviation (MAD)9.25
Skewness0.044207154
Sum5767.8
Variance98.65042
MonotonicityNot monotonic
2023-12-10T22:21:20.365460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.4 7
 
7.0%
54.1 5
 
5.0%
69.5 4
 
4.0%
53.9 4
 
4.0%
46.2 3
 
3.0%
69.2 3
 
3.0%
68.6 3
 
3.0%
68.4 3
 
3.0%
68.8 3
 
3.0%
46.1 3
 
3.0%
Other values (47) 62
62.0%
ValueCountFrequency (%)
44.4 1
1.0%
44.5 1
1.0%
44.6 2
2.0%
44.7 1
1.0%
44.8 1
1.0%
44.9 1
1.0%
45.1 1
1.0%
45.2 1
1.0%
45.4 1
1.0%
45.5 2
2.0%
ValueCountFrequency (%)
70.0 1
 
1.0%
69.8 1
 
1.0%
69.6 1
 
1.0%
69.5 4
4.0%
69.4 7
7.0%
69.3 2
 
2.0%
69.2 3
3.0%
69.0 2
 
2.0%
68.9 2
 
2.0%
68.8 3
3.0%

Interactions

2023-12-10T22:21:14.643746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:07.216698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:08.501461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:09.625865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.009296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:12.138398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:13.243298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:14.829723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:07.385621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:08.659270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:09.770047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.212904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:12.314542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:13.387268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:15.024933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:07.572564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:08.807108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:09.935769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.376694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:12.525269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:13.548574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:15.151351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:07.746282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:08.942428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:10.077424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.529263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:12.667145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:13.723913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:15.293132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:07.936331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:09.111014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:10.251341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.665161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:12.843672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:14.027182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:15.415775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:08.181301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:09.290030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:10.384154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.799144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:12.979601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:14.218639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:15.543224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:08.326581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:09.492232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:10.872634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:11.979623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:13.110078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:14.412073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:20.735640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.7780.8591.0000.917
일자/시간(t)0.0001.0000.0000.3560.5530.0000.0000.000
저수위(m)1.0000.0001.0000.0720.6930.9591.0000.990
강우량(mm)0.0000.3560.0721.0000.6200.3650.0720.000
유입량(ms)0.7780.5530.6930.6201.0000.8100.6930.734
방류량(ms)0.8590.0000.9590.3650.8101.0000.9590.851
저수량(백만m3)1.0000.0001.0000.0720.6930.9591.0000.990
저수율0.9170.0000.9900.0000.7340.8510.9901.000
2023-12-10T22:21:21.009591image/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)1.000-0.1550.3520.3910.1270.085-0.0430.000
저수위(m)-0.1551.000-0.169-0.637-0.562-0.441-0.1300.995
강우량(mm)0.352-0.1691.0000.3230.1480.0740.0140.000
유입량(ms)0.391-0.6370.3231.0000.8340.8030.6180.434
방류량(ms)0.127-0.5620.1480.8341.0000.8780.7690.776
저수량(백만m3)0.085-0.4410.0740.8030.8781.0000.9150.995
저수율-0.043-0.1300.0140.6180.7690.9151.0000.797
댐이름0.0000.9950.0000.4340.7760.9950.7971.000

Missing values

2023-12-10T22:21:15.717489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:15.907816image/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
0군위20190401192.670.00.091.13622.89147.0
1군위20190402192.620.00.0871.13122.80146.8
2군위20190403192.570.00.0831.12522.71146.6
3군위20190404192.520.00.01.03222.62146.5
4군위20190405192.480.00.251.08122.54946.3
5군위20190406192.430.00.0481.08522.4646.1
6군위20190407192.390.00.2541.08222.38846.0
7군위20190408192.330.00.01.10122.28145.8
8군위20190409192.2925.29030.2851.1122.2145.6
9군위20190410192.389.47322.9751.11822.3745.9
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90밀양20190401196.920.00.6921.4750.75969.0
91밀양20190402196.860.00.281.44550.65868.8
92밀양20190403196.810.00.4861.45650.57468.7
93밀양20190404196.750.00.3181.48150.47468.6
94밀양20190405196.690.00.3111.47250.37468.4
95밀양20190406196.630.00.2571.41750.27368.3
96밀양20190407196.580.00.4341.450.1968.2
97밀양20190408196.510.00.0561.40650.07368.0
98밀양20190409196.459.75370.3421.49849.97367.9
99밀양20190410196.459.47991.4691.46949.97367.9