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 2 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 3 other fieldsHigh correlation
강우량(mm) has 59 (59.0%) zerosZeros
유입량(ms) has 11 (11.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:20:35.374870
Analysis finished2023-12-10 13:20:44.778377
Duration9.4 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

2023-12-10T22:20:44.941791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:20:45.178139image/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%
Mean20190614
Minimum20190601
Maximum20190630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:45.357145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190601
5-th percentile20190602
Q120190607
median20190614
Q320190622
95-th percentile20190629
Maximum20190630
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8334763
Coefficient of variation (CV)4.3750408 × 10-7
Kurtosis-1.2329291
Mean20190614
Median Absolute Deviation (MAD)8
Skewness0.16149815
Sum2.0190614 × 109
Variance78.030303
MonotonicityNot monotonic
2023-12-10T22:20:45.705590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190601 4
 
4.0%
20190603 4
 
4.0%
20190604 4
 
4.0%
20190605 4
 
4.0%
20190606 4
 
4.0%
20190607 4
 
4.0%
20190608 4
 
4.0%
20190609 4
 
4.0%
20190610 4
 
4.0%
20190602 4
 
4.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20190601 4
4.0%
20190602 4
4.0%
20190603 4
4.0%
20190604 4
4.0%
20190605 4
4.0%
20190606 4
4.0%
20190607 4
4.0%
20190608 4
4.0%
20190609 4
4.0%
20190610 4
4.0%
ValueCountFrequency (%)
20190630 3
3.0%
20190629 3
3.0%
20190628 3
3.0%
20190627 3
3.0%
20190626 3
3.0%
20190625 3
3.0%
20190624 3
3.0%
20190623 3
3.0%
20190622 3
3.0%
20190621 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.9016
Minimum38.61
Maximum195.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:46.061628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.61
5-th percentile38.886
Q139.72
median72.32
Q3190.085
95-th percentile194.668
Maximum195.45
Range156.84
Interquartile range (IQR)150.365

Descriptive statistics

Standard deviation68.001124
Coefficient of variation (CV)0.61874553
Kurtosis-1.8039133
Mean109.9016
Median Absolute Deviation (MAD)33.11
Skewness0.30912336
Sum10990.16
Variance4624.1528
MonotonicityNot monotonic
2023-12-10T22:20:46.327464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.63 2
 
2.0%
39.52 2
 
2.0%
72.35 2
 
2.0%
190.85 1
 
1.0%
72.39 1
 
1.0%
72.05 1
 
1.0%
72.12 1
 
1.0%
72.18 1
 
1.0%
72.25 1
 
1.0%
72.3 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
38.61 1
1.0%
38.69 1
1.0%
38.75 1
1.0%
38.79 1
1.0%
38.81 1
1.0%
38.89 1
1.0%
38.97 1
1.0%
39.04 1
1.0%
39.11 1
1.0%
39.18 1
1.0%
ValueCountFrequency (%)
195.45 1
1.0%
195.3 1
1.0%
195.14 1
1.0%
194.98 1
1.0%
194.82 1
1.0%
194.66 1
1.0%
194.55 1
1.0%
194.41 1
1.0%
194.26 1
1.0%
194.1 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.238157
Minimum0
Maximum68.8186
Zeros59
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:46.603070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.1249
95-th percentile20.78223
Maximum68.8186
Range68.8186
Interquartile range (IQR)2.1249

Descriptive statistics

Standard deviation10.674927
Coefficient of variation (CV)2.5187663
Kurtosis16.257497
Mean4.238157
Median Absolute Deviation (MAD)0
Skewness3.7308878
Sum423.8157
Variance113.95407
MonotonicityNot monotonic
2023-12-10T22:20:46.824314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 59
59.0%
0.105 2
 
2.0%
8.3429 1
 
1.0%
68.8186 1
 
1.0%
16.9072 1
 
1.0%
42.8689 1
 
1.0%
0.2067 1
 
1.0%
14.042 1
 
1.0%
12.9312 1
 
1.0%
2.6599 1
 
1.0%
Other values (31) 31
31.0%
ValueCountFrequency (%)
0.0 59
59.0%
0.0295 1
 
1.0%
0.0449 1
 
1.0%
0.0453 1
 
1.0%
0.054 1
 
1.0%
0.0748 1
 
1.0%
0.105 2
 
2.0%
0.1091 1
 
1.0%
0.2067 1
 
1.0%
0.2132 1
 
1.0%
ValueCountFrequency (%)
68.8186 1
1.0%
42.8689 1
1.0%
40.5807 1
1.0%
39.4732 1
1.0%
27.6342 1
1.0%
20.4216 1
1.0%
18.2652 1
1.0%
17.0538 1
1.0%
16.9072 1
1.0%
14.042 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.4649
Minimum0
Maximum336.322
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:47.045828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.20625
median11.27
Q318.6285
95-th percentile52.52435
Maximum336.322
Range336.322
Interquartile range (IQR)18.42225

Descriptive statistics

Standard deviation45.17943
Coefficient of variation (CV)2.3210718
Kurtosis30.954827
Mean19.4649
Median Absolute Deviation (MAD)10.8025
Skewness5.23655
Sum1946.49
Variance2041.1809
MonotonicityNot monotonic
2023-12-10T22:20:47.257927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
11.0%
0.103 3
 
3.0%
0.097 2
 
2.0%
19.595 1
 
1.0%
11.934 1
 
1.0%
11.782 1
 
1.0%
11.596 1
 
1.0%
11.894 1
 
1.0%
17.906 1
 
1.0%
11.482 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 11
11.0%
0.082 1
 
1.0%
0.084 1
 
1.0%
0.088 1
 
1.0%
0.093 1
 
1.0%
0.094 1
 
1.0%
0.097 2
 
2.0%
0.103 3
 
3.0%
0.137 1
 
1.0%
0.139 1
 
1.0%
ValueCountFrequency (%)
336.322 1
1.0%
249.4 1
1.0%
139.213 1
1.0%
120.366 1
1.0%
91.215 1
1.0%
50.488 1
1.0%
46.272 1
1.0%
38.96 1
1.0%
31.812 1
1.0%
30.805 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.87241
Minimum0.96
Maximum201.306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:47.497075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.96
5-th percentile1.02685
Q11.05825
median32.89
Q354.589
95-th percentile57.51545
Maximum201.306
Range200.346
Interquartile range (IQR)53.53075

Descriptive statistics

Standard deviation32.986092
Coefficient of variation (CV)1.0349419
Kurtosis7.2919059
Mean31.87241
Median Absolute Deviation (MAD)23.8525
Skewness1.9272674
Sum3187.241
Variance1088.0822
MonotonicityNot monotonic
2023-12-10T22:20:47.792742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.048 3
 
3.0%
1.111 2
 
2.0%
1.021 2
 
2.0%
1.04 2
 
2.0%
56.377 2
 
2.0%
1.044 2
 
2.0%
1.045 2
 
2.0%
1.039 2
 
2.0%
3.194 2
 
2.0%
56.263 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
0.96 1
1.0%
1.021 2
2.0%
1.023 1
1.0%
1.024 1
1.0%
1.027 1
1.0%
1.03 1
1.0%
1.034 1
1.0%
1.036 1
1.0%
1.039 2
2.0%
1.04 2
2.0%
ValueCountFrequency (%)
201.306 1
1.0%
154.178 1
1.0%
111.38 1
1.0%
93.395 1
1.0%
62.255 1
1.0%
57.266 1
1.0%
57.174 1
1.0%
56.914 1
1.0%
56.905 1
1.0%
56.767 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.25299
Minimum17.605
Maximum942.202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:48.021300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.605
5-th percentile17.8072
Q119.32825
median134.6015
Q3862.18075
95-th percentile923.7958
Maximum942.202
Range924.597
Interquartile range (IQR)842.8525

Descriptive statistics

Standard deviation383.02898
Coefficient of variation (CV)1.192297
Kurtosis-1.2396595
Mean321.25299
Median Absolute Deviation (MAD)115.6755
Skewness0.84076346
Sum32125.299
Variance146711.2
MonotonicityNot monotonic
2023-12-10T22:20:48.242829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.716 2
 
2.0%
142.446 2
 
2.0%
921.422 2
 
2.0%
19.717 1
 
1.0%
923.656 1
 
1.0%
904.773 1
 
1.0%
908.642 1
 
1.0%
911.966 1
 
1.0%
915.853 1
 
1.0%
918.635 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
17.605 1
1.0%
17.637 1
1.0%
17.669 1
1.0%
17.716 2
2.0%
17.812 1
1.0%
17.828 1
1.0%
17.909 1
1.0%
18.005 1
1.0%
18.086 1
1.0%
18.183 1
1.0%
ValueCountFrequency (%)
942.202 1
1.0%
938.25 1
1.0%
934.307 1
1.0%
930.375 1
1.0%
926.452 1
1.0%
923.656 1
1.0%
921.98 1
1.0%
921.422 2
2.0%
920.864 1
1.0%
918.635 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.341
Minimum36.2
Maximum65.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:20:48.471412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.2
5-th percentile36.59
Q139.475
median46.15
Q360.55
95-th percentile63.92
Maximum65.7
Range29.5
Interquartile range (IQR)21.075

Descriptive statistics

Standard deviation10.394648
Coefficient of variation (CV)0.21066958
Kurtosis-1.6367909
Mean49.341
Median Absolute Deviation (MAD)8.95
Skewness0.22061992
Sum4934.1
Variance108.04871
MonotonicityNot monotonic
2023-12-10T22:20:48.831013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.8 3
 
3.0%
36.2 2
 
2.0%
39.9 2
 
2.0%
47.2 2
 
2.0%
36.4 2
 
2.0%
62.7 2
 
2.0%
36.6 2
 
2.0%
46.1 2
 
2.0%
38.8 2
 
2.0%
37.8 2
 
2.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
36.2 2
2.0%
36.3 1
1.0%
36.4 2
2.0%
36.6 2
2.0%
36.8 1
1.0%
37.0 1
1.0%
37.1 1
1.0%
37.3 1
1.0%
37.5 1
1.0%
37.7 1
1.0%
ValueCountFrequency (%)
65.7 1
1.0%
65.3 1
1.0%
65.0 1
1.0%
64.6 1
1.0%
64.3 1
1.0%
63.9 1
1.0%
63.7 1
1.0%
63.4 1
1.0%
63.2 1
1.0%
63.1 1
1.0%

Interactions

2023-12-10T22:20:43.227592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:35.992495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:37.252736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:38.381958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.550856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.618374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.803837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:43.360120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:36.152153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:37.433598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:38.539876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.712681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.782680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.959188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:43.508109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:36.356297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:37.597234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:38.785391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.871122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.962965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:42.142910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:43.640627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:36.519216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:37.762198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:38.959679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.060560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.117347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:42.640339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:43.820544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:36.774226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:37.926833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.125003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.217487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.285248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:42.784715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:43.985132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:36.957792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:38.089813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.275642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.355884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.452099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:42.986518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:44.118604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:37.106108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:38.223782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:39.404111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:40.473274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:41.665258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:20:43.104960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:20:49.072942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.0000.9401.0000.992
일자/시간(t)0.0001.0000.0000.2460.2180.0000.0000.561
저수위(m)1.0000.0001.0000.0000.1960.8831.0000.901
강우량(mm)0.0000.2460.0001.0000.9240.6630.0000.000
유입량(ms)0.0000.2180.1960.9241.0000.9230.1960.356
방류량(ms)0.9400.0000.8830.6630.9231.0000.8830.844
저수량(백만m3)1.0000.0001.0000.0000.1960.8831.0000.901
저수율0.9920.5610.9010.0000.3560.8440.9011.000
2023-12-10T22:20:49.421395image/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)1.000-0.4010.2900.1170.021-0.161-0.4220.000
저수위(m)-0.4011.000-0.274-0.748-0.534-0.4400.0510.995
강우량(mm)0.290-0.2741.0000.4200.1010.051-0.0530.000
유입량(ms)0.117-0.7480.4201.0000.7090.6890.3780.000
방류량(ms)0.021-0.5340.1010.7091.0000.9060.6480.665
저수량(백만m3)-0.161-0.4400.0510.6890.9061.0000.7460.995
저수율-0.4220.051-0.0530.3780.6480.7461.0000.860
댐이름0.0000.9950.0000.0000.6650.9950.8601.000

Missing values

2023-12-10T22:20:44.368421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:20:44.670170image/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
0군위20190601190.850.00.1371.11119.71740.5
1군위20190602190.80.00.1391.11119.63340.3
2군위20190603190.740.00.01.0819.53340.1
3군위20190604190.680.00.01.05619.43239.9
4군위20190605190.630.00.0941.0619.34939.7
5군위20190606190.5810.52690.0841.04819.26639.6
6군위20190607190.5511.41960.4711.04819.21639.5
7군위20190608190.510.00.2721.0419.14939.3
8군위20190609190.450.00.01.05219.0539.1
9군위20190610190.40.00.0881.04418.96739.0
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90밀양20190601195.450.00.6032.49248.32665.7
91밀양20190602195.30.00.4643.29148.08265.3
92밀양20190603195.140.00.1883.19447.82365.0
93밀양20190604194.980.00.1033.09947.56464.6
94밀양20190605194.820.00.2043.19147.30664.3
95밀양20190606194.669.63910.2273.20447.04863.9
96밀양20190607194.5518.26521.1973.23846.87263.7
97밀양20190608194.410.00.6023.19446.64863.4
98밀양20190609194.260.00.4023.17146.40963.1
99밀양20190610194.10.00.2963.2446.15462.7