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 4 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 저수위(m) and 4 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 4 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
강우량(mm) has 86 (86.0%) zerosZeros
유입량(ms) has 14 (14.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:12:46.682833
Analysis finished2023-12-10 14:12:55.878899
Duration9.2 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
감포
28 
광동
28 
구천
28 
달방
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
감포 28
28.0%
광동 28
28.0%
구천 28
28.0%
달방 16
16.0%

Length

2023-12-10T23:12:56.004714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:56.229533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 28
28.0%
광동 28
28.0%
구천 28
28.0%
달방 16
16.0%

일자/시간(t)
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190214
Minimum20190201
Maximum20190228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:56.444350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190201
5-th percentile20190202
Q120190207
median20190213
Q320190220
95-th percentile20190227
Maximum20190228
Range27
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.980304
Coefficient of variation (CV)3.9525605 × 10-7
Kurtosis-1.1135147
Mean20190214
Median Absolute Deviation (MAD)7
Skewness0.17584102
Sum2.0190214 × 109
Variance63.685253
MonotonicityNot monotonic
2023-12-10T23:12:56.675792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20190201 4
 
4.0%
20190210 4
 
4.0%
20190216 4
 
4.0%
20190202 4
 
4.0%
20190214 4
 
4.0%
20190213 4
 
4.0%
20190212 4
 
4.0%
20190211 4
 
4.0%
20190215 4
 
4.0%
20190209 4
 
4.0%
Other values (18) 60
60.0%
ValueCountFrequency (%)
20190201 4
4.0%
20190202 4
4.0%
20190203 4
4.0%
20190204 4
4.0%
20190205 4
4.0%
20190206 4
4.0%
20190207 4
4.0%
20190208 4
4.0%
20190209 4
4.0%
20190210 4
4.0%
ValueCountFrequency (%)
20190228 3
3.0%
20190227 3
3.0%
20190226 3
3.0%
20190225 3
3.0%
20190224 3
3.0%
20190223 3
3.0%
20190222 3
3.0%
20190221 3
3.0%
20190220 3
3.0%
20190219 3
3.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.0544
Minimum38.54
Maximum671.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:56.873115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.54
5-th percentile38.5995
Q138.8575
median88.605
Q3670.485
95-th percentile670.9715
Maximum671.07
Range632.53
Interquartile range (IQR)631.6275

Descriptive statistics

Standard deviation270.47713
Coefficient of variation (CV)1.1220585
Kurtosis-1.0447899
Mean241.0544
Median Absolute Deviation (MAD)49.8
Skewness0.96584594
Sum24105.44
Variance73157.88
MonotonicityNot monotonic
2023-12-10T23:12:57.105581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.12 3
 
3.0%
38.63 3
 
3.0%
88.1 2
 
2.0%
671.03 2
 
2.0%
88.65 2
 
2.0%
88.67 2
 
2.0%
88.45 1
 
1.0%
88.15 1
 
1.0%
88.2 1
 
1.0%
88.25 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
38.54 1
 
1.0%
38.55 1
 
1.0%
38.56 1
 
1.0%
38.57 1
 
1.0%
38.59 1
 
1.0%
38.6 1
 
1.0%
38.61 1
 
1.0%
38.62 1
 
1.0%
38.63 3
3.0%
38.65 1
 
1.0%
ValueCountFrequency (%)
671.07 1
1.0%
671.05 1
1.0%
671.03 2
2.0%
671.0 1
1.0%
670.97 1
1.0%
670.95 1
1.0%
670.93 1
1.0%
670.91 1
1.0%
670.88 1
1.0%
670.85 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.219358
Minimum0
Maximum29
Zeros86
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:57.319886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum29
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.8726033
Coefficient of variation (CV)3.99604
Kurtosis23.125165
Mean1.219358
Median Absolute Deviation (MAD)0
Skewness4.7806551
Sum121.9358
Variance23.742263
MonotonicityNot monotonic
2023-12-10T23:12:57.891824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 86
86.0%
1.0 4
 
4.0%
8.0 2
 
2.0%
3.0 1
 
1.0%
0.5 1
 
1.0%
5.0 1
 
1.0%
23.5 1
 
1.0%
12.4679 1
 
1.0%
0.4679 1
 
1.0%
29.0 1
 
1.0%
ValueCountFrequency (%)
0.0 86
86.0%
0.4679 1
 
1.0%
0.5 1
 
1.0%
1.0 4
 
4.0%
3.0 1
 
1.0%
5.0 1
 
1.0%
8.0 2
 
2.0%
12.4679 1
 
1.0%
23.5 1
 
1.0%
28.0 1
 
1.0%
ValueCountFrequency (%)
29.0 1
 
1.0%
28.0 1
 
1.0%
23.5 1
 
1.0%
12.4679 1
 
1.0%
8.0 2
2.0%
5.0 1
 
1.0%
3.0 1
 
1.0%
1.0 4
4.0%
0.5 1
 
1.0%
0.4679 1
 
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1008
Minimum0
Maximum0.605
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:58.236848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01775
median0.0775
Q30.16025
95-th percentile0.2946
Maximum0.605
Range0.605
Interquartile range (IQR)0.1425

Descriptive statistics

Standard deviation0.10340945
Coefficient of variation (CV)1.0258874
Kurtosis5.5803717
Mean0.1008
Median Absolute Deviation (MAD)0.0615
Skewness1.9228726
Sum10.08
Variance0.010693515
MonotonicityNot monotonic
2023-12-10T23:12:58.488500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.016 4
 
4.0%
0.015 4
 
4.0%
0.019 3
 
3.0%
0.059 3
 
3.0%
0.07 3
 
3.0%
0.175 3
 
3.0%
0.121 3
 
3.0%
0.075 2
 
2.0%
0.181 2
 
2.0%
Other values (54) 59
59.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.006 1
 
1.0%
0.013 1
 
1.0%
0.015 4
 
4.0%
0.016 4
 
4.0%
0.017 1
 
1.0%
0.018 1
 
1.0%
0.019 3
 
3.0%
0.022 1
 
1.0%
0.023 1
 
1.0%
ValueCountFrequency (%)
0.605 1
1.0%
0.415 1
1.0%
0.389 1
1.0%
0.323 1
1.0%
0.306 1
1.0%
0.294 1
1.0%
0.291 1
1.0%
0.273 1
1.0%
0.21 1
1.0%
0.208 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24787
Minimum0.035
Maximum0.415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:58.716486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.03695
Q10.04275
median0.302
Q30.3645
95-th percentile0.39415
Maximum0.415
Range0.38
Interquartile range (IQR)0.32175

Descriptive statistics

Standard deviation0.13720041
Coefficient of variation (CV)0.55351761
Kurtosis-1.1656779
Mean0.24787
Median Absolute Deviation (MAD)0.0725
Skewness-0.71512096
Sum24.787
Variance0.018823953
MonotonicityNot monotonic
2023-12-10T23:12:58.985246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 7
 
7.0%
0.038 6
 
6.0%
0.037 3
 
3.0%
0.302 3
 
3.0%
0.381 3
 
3.0%
0.315 3
 
3.0%
0.036 3
 
3.0%
0.304 3
 
3.0%
0.311 3
 
3.0%
0.039 3
 
3.0%
Other values (54) 63
63.0%
ValueCountFrequency (%)
0.035 2
 
2.0%
0.036 3
3.0%
0.037 3
3.0%
0.038 6
6.0%
0.039 3
3.0%
0.04 7
7.0%
0.042 1
 
1.0%
0.043 1
 
1.0%
0.044 1
 
1.0%
0.045 1
 
1.0%
ValueCountFrequency (%)
0.415 1
1.0%
0.41 1
1.0%
0.406 1
1.0%
0.405 1
1.0%
0.397 1
1.0%
0.394 1
1.0%
0.393 1
1.0%
0.388 2
2.0%
0.386 1
1.0%
0.382 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.90331
Minimum2.107
Maximum8.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:59.258538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.107
5-th percentile2.1179
Q12.1665
median7.183
Q37.85425
95-th percentile8.2733
Maximum8.36
Range6.253
Interquartile range (IQR)5.68775

Descriptive statistics

Standard deviation2.4356051
Coefficient of variation (CV)0.41258296
Kurtosis-1.139958
Mean5.90331
Median Absolute Deviation (MAD)0.908
Skewness-0.80154366
Sum590.331
Variance5.9321724
MonotonicityNot monotonic
2023-12-10T23:12:59.557557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.197 3
 
3.0%
2.124 3
 
3.0%
7.189 2
 
2.0%
8.325 2
 
2.0%
7.414 2
 
2.0%
7.422 2
 
2.0%
7.332 1
 
1.0%
7.21 1
 
1.0%
7.23 1
 
1.0%
7.25 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
2.107 1
 
1.0%
2.109 1
 
1.0%
2.111 1
 
1.0%
2.113 1
 
1.0%
2.116 1
 
1.0%
2.118 1
 
1.0%
2.12 1
 
1.0%
2.122 1
 
1.0%
2.124 3
3.0%
2.128 1
 
1.0%
ValueCountFrequency (%)
8.36 1
1.0%
8.342 1
1.0%
8.325 2
2.0%
8.298 1
1.0%
8.272 1
1.0%
8.255 1
1.0%
8.237 1
1.0%
8.22 1
1.0%
8.194 1
1.0%
8.168 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.755
Minimum59.5
Maximum82.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:59.807653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59.5
5-th percentile60.195
Q163.4
median72.15
Q380.125
95-th percentile81.905
Maximum82.4
Range22.9
Interquartile range (IQR)16.725

Descriptive statistics

Standard deviation7.409404
Coefficient of variation (CV)0.10325976
Kurtosis-1.1594988
Mean71.755
Median Absolute Deviation (MAD)8.25
Skewness-0.19008738
Sum7175.5
Variance54.899268
MonotonicityNot monotonic
2023-12-10T23:13:00.089971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.8 3
 
3.0%
71.9 3
 
3.0%
74.1 3
 
3.0%
80.6 3
 
3.0%
63.4 2
 
2.0%
71.4 2
 
2.0%
74.0 2
 
2.0%
80.4 2
 
2.0%
71.1 2
 
2.0%
73.0 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
59.5 1
1.0%
59.6 1
1.0%
59.8 1
1.0%
59.9 1
1.0%
60.1 1
1.0%
60.2 1
1.0%
60.4 1
1.0%
60.6 1
1.0%
60.7 1
1.0%
60.8 1
1.0%
ValueCountFrequency (%)
82.4 1
1.0%
82.3 1
1.0%
82.2 1
1.0%
82.1 1
1.0%
82.0 1
1.0%
81.9 1
1.0%
81.7 1
1.0%
81.6 1
1.0%
81.5 1
1.0%
81.4 1
1.0%

Interactions

2023-12-10T23:12:54.379718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:47.067179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.352611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:49.416535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:50.576032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:51.883347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:53.077185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:54.516273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:47.190375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.504049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:49.557894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:50.742959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:52.034811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:53.205122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:54.659626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:47.316456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.651914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:49.712898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:50.952460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:52.184067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:53.369721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:54.800962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:47.518544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.797454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:49.893848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:51.126891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:52.331955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:53.706094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:54.975012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:47.641154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.946638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:50.100881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:51.268981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:52.504345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:53.911564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:55.158103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.103681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:49.115037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:50.269642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:51.449713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:52.745438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:54.070561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:55.300035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:48.224072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:49.276720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:50.438644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:51.671227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:52.930113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:54.236476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:13:00.244752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.1680.8100.9781.0000.903
일자/시간(t)0.0001.0000.0000.3510.3310.0000.0000.475
저수위(m)1.0000.0001.0000.0000.4880.8061.0000.851
강우량(mm)0.1680.3510.0001.0000.5830.0000.0000.000
유입량(ms)0.8100.3310.4880.5831.0000.7030.5790.537
방류량(ms)0.9780.0000.8060.0000.7031.0000.8180.784
저수량(백만m3)1.0000.0001.0000.0000.5790.8181.0000.838
저수율0.9030.4750.8510.0000.5370.7840.8381.000
2023-12-10T23:13:00.450700image/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)1.000-0.309-0.155-0.004-0.117-0.194-0.2860.000
저수위(m)-0.3091.0000.0120.5120.8610.882-0.8120.995
강우량(mm)-0.1550.0121.0000.078-0.0650.0070.1000.105
유입량(ms)-0.0040.5120.0781.0000.6090.615-0.5190.458
방류량(ms)-0.1170.861-0.0650.6091.0000.802-0.8040.790
저수량(백만m3)-0.1940.8820.0070.6150.8021.000-0.7580.995
저수율-0.286-0.8120.100-0.519-0.804-0.7581.0000.849
댐이름0.0000.9950.1050.4580.7900.9950.8491.000

Missing values

2023-12-10T23:12:55.526143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:55.783620image/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
0감포2019020138.890.00.00.0382.17282.4
1감포2019020238.880.00.0160.0382.1782.3
2감포2019020338.863.00.00.0422.16782.2
3감포2019020438.850.00.0230.0452.16582.1
4감포2019020538.830.00.00.0432.16182.0
5감포2019020638.820.00.0220.0442.15981.9
6감포2019020738.790.00.00.042.15481.7
7감포2019020838.780.00.0160.0382.15281.6
8감포2019020938.760.00.00.0392.14881.5
9감포2019021038.750.00.0160.0382.14681.4
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달방20190207110.591.00.1050.3076.30772.1
91달방20190208110.540.00.070.3226.28671.8
92달방20190209110.490.00.080.3316.26471.6
93달방20190210110.440.00.0650.3156.24271.4
94달방20190211110.390.00.0910.3416.22171.1
95달방20190212110.330.00.0810.386.19570.8
96달방20190213110.260.00.00.2116.16570.5
97달방20190214110.220.00.0520.256.14870.3
98달방20190215110.160.00.0060.3026.12270.0
99달방20190216110.120.00.1180.3156.10569.8