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 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 유입량(ms) and 3 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 4 other fieldsHigh correlation
강우량(mm) has 64 (64.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:21:22.708311
Analysis finished2023-12-10 13:21:32.246431
Duration9.54 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

2023-12-10T22:21:32.382690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:21:32.605613image/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%
Mean20190315
Minimum20190301
Maximum20190331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:32.825866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190301
5-th percentile20190302
Q120190307
median20190315
Q320190323
95-th percentile20190330
Maximum20190331
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.214306
Coefficient of variation (CV)4.5637257 × 10-7
Kurtosis-1.2602055
Mean20190315
Median Absolute Deviation (MAD)8
Skewness0.10720475
Sum2.0190315 × 109
Variance84.903434
MonotonicityNot monotonic
2023-12-10T22:21:33.031018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190301 4
 
4.0%
20190303 4
 
4.0%
20190304 4
 
4.0%
20190305 4
 
4.0%
20190306 4
 
4.0%
20190307 4
 
4.0%
20190302 4
 
4.0%
20190326 3
 
3.0%
20190322 3
 
3.0%
20190323 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20190301 4
4.0%
20190302 4
4.0%
20190303 4
4.0%
20190304 4
4.0%
20190305 4
4.0%
20190306 4
4.0%
20190307 4
4.0%
20190308 3
3.0%
20190309 3
3.0%
20190310 3
3.0%
ValueCountFrequency (%)
20190331 3
3.0%
20190330 3
3.0%
20190329 3
3.0%
20190328 3
3.0%
20190327 3
3.0%
20190326 3
3.0%
20190325 3
3.0%
20190324 3
3.0%
20190323 3
3.0%
20190322 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.3654
Minimum40.46
Maximum197.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:33.346125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.46
5-th percentile40.4695
Q140.59
median74.47
Q3193.1825
95-th percentile197.1025
Maximum197.35
Range156.89
Interquartile range (IQR)152.5925

Descriptive statistics

Standard deviation67.958944
Coefficient of variation (CV)0.62139345
Kurtosis-1.7352001
Mean109.3654
Median Absolute Deviation (MAD)33.98
Skewness0.38244606
Sum10936.54
Variance4618.418
MonotonicityNot monotonic
2023-12-10T22:21:33.701231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.59 6
 
6.0%
40.49 5
 
5.0%
40.46 5
 
5.0%
40.48 3
 
3.0%
74.33 2
 
2.0%
40.47 2
 
2.0%
40.52 2
 
2.0%
74.64 2
 
2.0%
197.35 1
 
1.0%
74.57 1
 
1.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
40.46 5
5.0%
40.47 2
 
2.0%
40.48 3
3.0%
40.49 5
5.0%
40.5 1
 
1.0%
40.52 2
 
2.0%
40.53 1
 
1.0%
40.54 1
 
1.0%
40.55 1
 
1.0%
40.56 1
 
1.0%
ValueCountFrequency (%)
197.35 1
1.0%
197.3 1
1.0%
197.25 1
1.0%
197.2 1
1.0%
197.15 1
1.0%
197.1 1
1.0%
197.05 1
1.0%
193.81 1
1.0%
193.8 1
1.0%
193.79 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.986871
Minimum0
Maximum18.5483
Zeros64
Zeros (%)64.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:34.018757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.71875
95-th percentile4.64288
Maximum18.5483
Range18.5483
Interquartile range (IQR)0.71875

Descriptive statistics

Standard deviation2.8159055
Coefficient of variation (CV)2.8533673
Kurtosis23.924434
Mean0.986871
Median Absolute Deviation (MAD)0
Skewness4.6157569
Sum98.6871
Variance7.9293236
MonotonicityNot monotonic
2023-12-10T22:21:34.308765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 64
64.0%
0.7634 2
 
2.0%
0.0798 1
 
1.0%
0.0055 1
 
1.0%
2.8754 1
 
1.0%
0.9668 1
 
1.0%
2.1786 1
 
1.0%
0.6185 1
 
1.0%
0.0729 1
 
1.0%
1.7889 1
 
1.0%
Other values (26) 26
26.0%
ValueCountFrequency (%)
0.0 64
64.0%
0.0055 1
 
1.0%
0.0729 1
 
1.0%
0.0798 1
 
1.0%
0.0852 1
 
1.0%
0.2355 1
 
1.0%
0.238 1
 
1.0%
0.2473 1
 
1.0%
0.3014 1
 
1.0%
0.4181 1
 
1.0%
ValueCountFrequency (%)
18.5483 1
1.0%
16.427 1
1.0%
8.7748 1
1.0%
7.2366 1
1.0%
6.2366 1
1.0%
4.559 1
1.0%
3.9 1
1.0%
3.4329 1
1.0%
2.8754 1
1.0%
2.6348 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8303
Minimum0.008
Maximum40.494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:34.621826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.0339
Q10.31525
median15.066
Q318.31425
95-th percentile26.15395
Maximum40.494
Range40.486
Interquartile range (IQR)17.999

Descriptive statistics

Standard deviation10.190937
Coefficient of variation (CV)0.8614268
Kurtosis-0.79048026
Mean11.8303
Median Absolute Deviation (MAD)7.407
Skewness0.22349963
Sum1183.03
Variance103.85521
MonotonicityNot monotonic
2023-12-10T22:21:34.986141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.113 2
 
2.0%
0.427 2
 
2.0%
0.032 2
 
2.0%
0.247 2
 
2.0%
0.332 1
 
1.0%
22.44 1
 
1.0%
22.123 1
 
1.0%
15.398 1
 
1.0%
22.021 1
 
1.0%
22.099 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.008 1
1.0%
0.009 1
1.0%
0.015 1
1.0%
0.032 2
2.0%
0.034 1
1.0%
0.035 1
1.0%
0.036 1
1.0%
0.037 1
1.0%
0.045 1
1.0%
0.047 1
1.0%
ValueCountFrequency (%)
40.494 1
1.0%
37.393 1
1.0%
28.993 1
1.0%
28.973 1
1.0%
26.286 1
1.0%
26.147 1
1.0%
24.653 1
1.0%
24.45 1
1.0%
24.073 1
1.0%
22.628 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.70021
Minimum0.331
Maximum29.555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:35.282482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.331
5-th percentile0.63395
Q11.115
median18.2925
Q329.049
95-th percentile29.42835
Maximum29.555
Range29.224
Interquartile range (IQR)27.934

Descriptive statistics

Standard deviation12.201423
Coefficient of variation (CV)0.77715033
Kurtosis-1.7054171
Mean15.70021
Median Absolute Deviation (MAD)10.988
Skewness-0.20534329
Sum1570.021
Variance148.87473
MonotonicityNot monotonic
2023-12-10T22:21:35.632629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.331 2
 
2.0%
29.093 2
 
2.0%
24.402 2
 
2.0%
29.217 1
 
1.0%
29.079 1
 
1.0%
29.316 1
 
1.0%
28.984 1
 
1.0%
29.064 1
 
1.0%
29.126 1
 
1.0%
29.312 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.331 2
2.0%
0.332 1
1.0%
0.341 1
1.0%
0.386 1
1.0%
0.647 1
1.0%
0.72 1
1.0%
0.797 1
1.0%
1.067 1
1.0%
1.071 1
1.0%
1.076 1
1.0%
ValueCountFrequency (%)
29.555 1
1.0%
29.548 1
1.0%
29.527 1
1.0%
29.509 1
1.0%
29.454 1
1.0%
29.427 1
1.0%
29.426 1
1.0%
29.375 1
1.0%
29.331 1
1.0%
29.316 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.44374
Minimum22.982
Maximum1054.449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:35.997462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.982
5-th percentile23.37645
Q124.80625
median168.257
Q31034.7735
95-th percentile1050.2853
Maximum1054.449
Range1031.467
Interquartile range (IQR)1009.9673

Descriptive statistics

Standard deviation445.39551
Coefficient of variation (CV)1.1525494
Kurtosis-1.3432717
Mean386.44374
Median Absolute Deviation (MAD)143.9455
Skewness0.77849642
Sum38644.374
Variance198377.16
MonotonicityNot monotonic
2023-12-10T22:21:36.223318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171.026 6
 
6.0%
168.257 5
 
5.0%
167.43 5
 
5.0%
167.981 3
 
3.0%
1035.82 2
 
2.0%
167.705 2
 
2.0%
169.085 2
 
2.0%
1054.449 2
 
2.0%
51.484 1
 
1.0%
1050.225 1
 
1.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
22.982 1
1.0%
23.054 1
1.0%
23.145 1
1.0%
23.217 1
1.0%
23.29 1
1.0%
23.381 1
1.0%
23.454 1
1.0%
23.546 1
1.0%
23.619 1
1.0%
23.711 1
1.0%
ValueCountFrequency (%)
1054.449 2
2.0%
1053.241 1
1.0%
1052.638 1
1.0%
1051.431 1
1.0%
1050.225 1
1.0%
1049.02 1
1.0%
1047.816 1
1.0%
1047.214 1
1.0%
1046.613 1
1.0%
1045.41 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.827
Minimum47.2
Maximum70.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:21:36.434567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47.2
5-th percentile47.99
Q150.95
median54.85
Q369.6
95-th percentile70.505
Maximum70.8
Range23.6
Interquartile range (IQR)18.65

Descriptive statistics

Standard deviation8.9740125
Coefficient of variation (CV)0.15254921
Kurtosis-1.6838857
Mean58.827
Median Absolute Deviation (MAD)6
Skewness0.31332381
Sum5882.7
Variance80.5329
MonotonicityNot monotonic
2023-12-10T22:21:36.791380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.2 7
 
7.0%
55.3 6
 
6.0%
54.4 5
 
5.0%
69.5 4
 
4.0%
70.0 4
 
4.0%
54.3 3
 
3.0%
69.4 3
 
3.0%
69.6 3
 
3.0%
69.7 3
 
3.0%
69.8 3
 
3.0%
Other values (48) 59
59.0%
ValueCountFrequency (%)
47.2 1
1.0%
47.3 1
1.0%
47.5 1
1.0%
47.7 1
1.0%
47.8 1
1.0%
48.0 1
1.0%
48.2 1
1.0%
48.4 1
1.0%
48.5 1
1.0%
48.7 2
2.0%
ValueCountFrequency (%)
70.8 2
2.0%
70.7 2
2.0%
70.6 1
 
1.0%
70.5 1
 
1.0%
70.4 1
 
1.0%
70.3 2
2.0%
70.2 2
2.0%
70.1 1
 
1.0%
70.0 4
4.0%
69.9 1
 
1.0%

Interactions

2023-12-10T22:21:30.434206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.320191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.594423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.818394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.006112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.063828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.982540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.712886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.560631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.749816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.958432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.172334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.171382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.136754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.875100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.826799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.904665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.173262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.321573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.292831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.617985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.049060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:23.982459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.054122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.326593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.490904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.439741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.772772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.231030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.173320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.297898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.483924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.652470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.604024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:29.924471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.426879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.323853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.534401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.624429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.790353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.748318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.151976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:31.604363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:24.470397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:25.674432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:26.780763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:27.943097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:28.881276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:21:30.290618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:21:36.943133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.9480.9811.0000.835
일자/시간(t)0.0001.0000.0000.1710.2190.2590.0000.274
저수위(m)1.0000.0001.0000.1890.8981.0001.0000.866
강우량(mm)0.0000.1710.1891.0000.1980.0630.1890.318
유입량(ms)0.9480.2190.8980.1981.0000.9640.8980.711
방류량(ms)0.9810.2591.0000.0630.9641.0001.0000.785
저수량(백만m3)1.0000.0001.0000.1890.8981.0001.0000.866
저수율0.8350.2740.8660.3180.7110.7850.8661.000
2023-12-10T22:21:37.144792image/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)1.000-0.3430.2120.1670.102-0.141-0.3000.000
저수위(m)-0.3431.000-0.205-0.625-0.493-0.414-0.1530.995
강우량(mm)0.212-0.2051.0000.2200.0770.0580.0040.000
유입량(ms)0.167-0.6250.2201.0000.7720.7890.6670.686
방류량(ms)0.102-0.4930.0770.7721.0000.9210.8300.810
저수량(백만m3)-0.141-0.4140.0580.7890.9211.0000.9170.995
저수율-0.300-0.1530.0040.6670.8300.9171.0000.804
댐이름0.0000.9950.0000.6860.8100.9950.8041.000

Missing values

2023-12-10T22:21:31.865830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:21:32.156327image/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
0군위20190301193.810.00.3320.33224.99451.3
1군위20190302193.80.00.1130.33124.97551.3
2군위20190303193.790.00.1130.33124.95651.3
3군위20190304193.780.00.1680.38624.93851.2
4군위20190305193.760.00.3620.79724.951.1
5군위20190306193.720.00.221.0924.82551.0
6군위20190307193.680.00.2191.08724.7550.8
7군위20190308193.630.00.0321.11624.65650.6
8군위20190309193.590.00.2651.13124.58150.5
9군위20190310193.566.23660.4811.12924.52550.4
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90대청2019032974.250.015.429.2051031.04469.2
91대청2019033074.244.55922.62829.5271030.44869.2
92대청2019033174.220.247315.34229.1321029.25669.1
93밀양20190301197.350.00.4651.44651.48470.0
94밀양20190302197.30.00.471.4551.39969.8
95밀양20190303197.250.00.4271.40651.31569.7
96밀양20190304197.20.00.4691.44751.2369.6
97밀양20190305197.150.00.4971.47451.14669.5
98밀양20190306197.12.63480.4271.40351.06269.4
99밀양20190307197.050.00.4611.43650.97869.3