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 74 (74.0%) zerosZeros
유입량(ms) has 9 (9.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:12:31.010090
Analysis finished2023-12-10 14:12:40.535607
Duration9.53 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
31 
광동
31 
구천
31 
달방
7 

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 31
31.0%
광동 31
31.0%
구천 31
31.0%
달방 7
 
7.0%

Length

2023-12-10T23:12:40.633667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:40.794856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 31
31.0%
광동 31
31.0%
구천 31
31.0%
달방 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
2023-12-10T23:12:40.966166image/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
2023-12-10T23:12:41.256837image/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 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.5014
Minimum38.17
Maximum670.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:41.535463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.17
5-th percentile38.239
Q138.4475
median87.65
Q3670.17
95-th percentile670.541
Maximum670.61
Range632.44
Interquartile range (IQR)631.7225

Descriptive statistics

Standard deviation281.05102
Coefficient of variation (CV)1.1043201
Kurtosis-1.3355618
Mean254.5014
Median Absolute Deviation (MAD)49.255
Skewness0.8127284
Sum25450.14
Variance78989.677
MonotonicityNot monotonic
2023-12-10T23:12:41.764370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.54 3
 
3.0%
38.42 3
 
3.0%
38.22 2
 
2.0%
87.57 2
 
2.0%
670.13 2
 
2.0%
670.16 2
 
2.0%
670.17 2
 
2.0%
670.33 2
 
2.0%
38.3 2
 
2.0%
87.71 2
 
2.0%
Other values (77) 78
78.0%
ValueCountFrequency (%)
38.17 1
1.0%
38.18 1
1.0%
38.21 1
1.0%
38.22 2
2.0%
38.24 1
1.0%
38.25 1
1.0%
38.27 1
1.0%
38.28 1
1.0%
38.3 2
2.0%
38.31 1
1.0%
ValueCountFrequency (%)
670.61 1
1.0%
670.6 1
1.0%
670.59 1
1.0%
670.57 1
1.0%
670.56 1
1.0%
670.54 1
1.0%
670.53 1
1.0%
670.51 1
1.0%
670.48 1
1.0%
670.42 1
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.894037
Minimum0
Maximum37
Zeros74
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:42.023224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.475925
95-th percentile12.94864
Maximum37
Range37
Interquartile range (IQR)0.475925

Descriptive statistics

Standard deviation5.9172637
Coefficient of variation (CV)3.1241542
Kurtosis21.018008
Mean1.894037
Median Absolute Deviation (MAD)0
Skewness4.3799279
Sum189.4037
Variance35.01401
MonotonicityNot monotonic
2023-12-10T23:12:42.234741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 74
74.0%
0.5 3
 
3.0%
1.5 2
 
2.0%
2.5 2
 
2.0%
0.5321 2
 
2.0%
0.4679 1
 
1.0%
17.0 1
 
1.0%
8.0 1
 
1.0%
34.0 1
 
1.0%
1.0 1
 
1.0%
Other values (12) 12
 
12.0%
ValueCountFrequency (%)
0.0 74
74.0%
0.4679 1
 
1.0%
0.5 3
 
3.0%
0.5321 2
 
2.0%
1.0 1
 
1.0%
1.5 2
 
2.0%
2.0 1
 
1.0%
2.5 2
 
2.0%
2.5321 1
 
1.0%
2.9358 1
 
1.0%
ValueCountFrequency (%)
37.0 1
1.0%
34.0 1
1.0%
18.5 1
1.0%
17.0 1
1.0%
13.1926 1
1.0%
12.9358 1
1.0%
12.1284 1
1.0%
8.0 1
1.0%
5.4037 1
1.0%
5.2111 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2179
Minimum0
Maximum1.901
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:42.497805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01875
median0.131
Q30.26225
95-th percentile0.67475
Maximum1.901
Range1.901
Interquartile range (IQR)0.2435

Descriptive statistics

Standard deviation0.29160547
Coefficient of variation (CV)1.3382536
Kurtosis12.926063
Mean0.2179
Median Absolute Deviation (MAD)0.115
Skewness3.1108034
Sum21.79
Variance0.085033747
MonotonicityNot monotonic
2023-12-10T23:12:42.745064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.017 4
 
4.0%
0.015 4
 
4.0%
0.158 3
 
3.0%
0.12 2
 
2.0%
0.129 2
 
2.0%
0.161 2
 
2.0%
0.168 2
 
2.0%
0.346 2
 
2.0%
0.009 2
 
2.0%
Other values (66) 68
68.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.007 1
 
1.0%
0.009 2
 
2.0%
0.01 1
 
1.0%
0.013 1
 
1.0%
0.014 1
 
1.0%
0.015 4
4.0%
0.017 4
4.0%
0.018 2
 
2.0%
0.019 1
 
1.0%
ValueCountFrequency (%)
1.901 1
1.0%
1.268 1
1.0%
1.236 1
1.0%
0.915 1
1.0%
0.727 1
1.0%
0.672 1
1.0%
0.629 1
1.0%
0.524 1
1.0%
0.52 1
1.0%
0.511 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24105
Minimum0.028
Maximum0.408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:43.080205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.028
5-th percentile0.03
Q10.03875
median0.304
Q30.34425
95-th percentile0.3901
Maximum0.408
Range0.38
Interquartile range (IQR)0.3055

Descriptive statistics

Standard deviation0.1416111
Coefficient of variation (CV)0.58747606
Kurtosis-1.3359996
Mean0.24105
Median Absolute Deviation (MAD)0.0495
Skewness-0.69286609
Sum24.105
Variance0.020053705
MonotonicityNot monotonic
2023-12-10T23:12:43.427669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.038 7
 
7.0%
0.036 6
 
6.0%
0.298 4
 
4.0%
0.03 3
 
3.0%
0.039 3
 
3.0%
0.304 3
 
3.0%
0.3 3
 
3.0%
0.028 3
 
3.0%
0.385 3
 
3.0%
0.34 2
 
2.0%
Other values (50) 63
63.0%
ValueCountFrequency (%)
0.028 3
3.0%
0.03 3
3.0%
0.031 2
 
2.0%
0.033 2
 
2.0%
0.034 1
 
1.0%
0.035 1
 
1.0%
0.036 6
6.0%
0.038 7
7.0%
0.039 3
3.0%
0.04 2
 
2.0%
ValueCountFrequency (%)
0.408 1
 
1.0%
0.407 1
 
1.0%
0.403 1
 
1.0%
0.402 1
 
1.0%
0.392 1
 
1.0%
0.39 1
 
1.0%
0.389 1
 
1.0%
0.388 1
 
1.0%
0.385 3
3.0%
0.384 1
 
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.60946
Minimum2.04
Maximum7.961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:43.723844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.04
5-th percentile2.05185
Q12.0905
median6.975
Q37.588
95-th percentile7.90285
Maximum7.961
Range5.921
Interquartile range (IQR)5.4975

Descriptive statistics

Standard deviation2.4353567
Coefficient of variation (CV)0.43415172
Kurtosis-1.3824547
Mean5.60946
Median Absolute Deviation (MAD)0.7775
Skewness-0.70638259
Sum560.946
Variance5.9309623
MonotonicityNot monotonic
2023-12-10T23:12:44.061913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.965 3
 
3.0%
2.085 3
 
3.0%
2.049 2
 
2.0%
6.977 2
 
2.0%
7.555 2
 
2.0%
7.58 2
 
2.0%
7.588 2
 
2.0%
7.723 2
 
2.0%
2.063 2
 
2.0%
7.033 2
 
2.0%
Other values (77) 78
78.0%
ValueCountFrequency (%)
2.04 1
1.0%
2.042 1
1.0%
2.047 1
1.0%
2.049 2
2.0%
2.052 1
1.0%
2.054 1
1.0%
2.058 1
1.0%
2.06 1
1.0%
2.063 2
2.0%
2.065 1
1.0%
ValueCountFrequency (%)
7.961 1
1.0%
7.953 1
1.0%
7.944 1
1.0%
7.927 1
1.0%
7.919 1
1.0%
7.902 1
1.0%
7.893 1
1.0%
7.876 1
1.0%
7.85 1
1.0%
7.799 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.876
Minimum57.5
Maximum79.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:44.503716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57.5
5-th percentile57.795
Q160.175
median69.65
Q377.9
95-th percentile79.305
Maximum79.8
Range22.3
Interquartile range (IQR)17.725

Descriptive statistics

Standard deviation7.9137027
Coefficient of variation (CV)0.11489783
Kurtosis-1.3979109
Mean68.876
Median Absolute Deviation (MAD)8.75
Skewness-0.059627451
Sum6887.6
Variance62.626691
MonotonicityNot monotonic
2023-12-10T23:12:44.810256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.7 4
 
4.0%
70.1 3
 
3.0%
79.1 3
 
3.0%
70.2 3
 
3.0%
77.7 3
 
3.0%
78.3 3
 
3.0%
57.7 3
 
3.0%
69.5 3
 
3.0%
70.3 2
 
2.0%
79.2 2
 
2.0%
Other values (58) 71
71.0%
ValueCountFrequency (%)
57.5 2
2.0%
57.7 3
3.0%
57.8 2
2.0%
57.9 1
 
1.0%
58.0 1
 
1.0%
58.1 1
 
1.0%
58.2 2
2.0%
58.4 1
 
1.0%
58.5 1
 
1.0%
58.6 1
 
1.0%
ValueCountFrequency (%)
79.8 1
 
1.0%
79.7 1
 
1.0%
79.6 1
 
1.0%
79.5 1
 
1.0%
79.4 1
 
1.0%
79.3 1
 
1.0%
79.2 2
2.0%
79.1 3
3.0%
79.0 1
 
1.0%
78.9 1
 
1.0%

Interactions

2023-12-10T23:12:39.295366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:31.409387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:32.632530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:34.159423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:35.506860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:36.755460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:37.843814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:39.438096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:31.578138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:32.809988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:34.419736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:35.659230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:36.899997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:38.022182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:39.555380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:31.730327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:33.022210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:34.627778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:35.802999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:37.065282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:38.173265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:39.658325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:31.887363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:33.219685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:34.764496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:35.960549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:37.230613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:38.667287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:39.770064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:32.050894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:33.402805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:34.942159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:36.118897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:37.395281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:38.796432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:39.909355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:32.251094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:33.703598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:35.106146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:36.270697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:37.545157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:38.922722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:40.032573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:32.442931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:33.952100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:35.346638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:36.533049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:37.697982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:39.082811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:12:44.965085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.3340.3860.8541.0001.000
일자/시간(t)0.0001.0000.1390.0000.4450.4960.0000.432
저수위(m)1.0000.1391.0000.3880.3840.7641.0001.000
강우량(mm)0.3340.0000.3881.0000.6650.5850.3340.278
유입량(ms)0.3860.4450.3840.6651.0000.4090.3860.727
방류량(ms)0.8540.4960.7640.5850.4091.0000.8540.846
저수량(백만m3)1.0000.0001.0000.3340.3860.8541.0001.000
저수율1.0000.4321.0000.2780.7270.8461.0001.000
2023-12-10T23:12:45.246368image/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)1.000-0.1240.0260.217-0.301-0.023-0.0210.000
저수위(m)-0.1241.0000.0360.7640.8780.951-0.8210.995
강우량(mm)0.0260.0361.0000.178-0.0180.037-0.1120.183
유입량(ms)0.2170.7640.1781.0000.6320.794-0.7170.269
방류량(ms)-0.3010.878-0.0180.6321.0000.751-0.8450.831
저수량(백만m3)-0.0230.9510.0370.7940.7511.000-0.7721.000
저수율-0.021-0.821-0.112-0.717-0.845-0.7721.0000.984
댐이름0.0000.9950.1830.2690.8311.0000.9841.000

Missing values

2023-12-10T23:12:40.229997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:40.464514image/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
0감포2019030138.520.00.00.042.10479.8
1감포2019030238.510.00.0240.0452.10279.7
2감포2019030338.490.00.00.0382.09879.6
3감포2019030438.480.00.0170.0382.09679.5
4감포2019030538.460.00.00.0382.09379.4
5감포2019030638.450.50.0130.0342.09179.3
6감포2019030738.443.00.0180.0392.08979.2
7감포2019030838.430.00.0150.0362.08779.2
8감포2019030938.420.00.0170.0382.08579.1
9감포2019031038.4218.50.0330.0332.08579.1
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90구천2019032987.620.00.1330.3176.99769.9
91구천2019033087.580.00.120.3046.98169.7
92구천2019033187.540.00.1280.3126.96569.5
93달방20190301109.460.00.1010.3855.83166.6
94달방20190302109.40.00.120.4035.80666.4
95달방20190303109.350.00.150.3855.78666.1
96달방20190304109.280.00.080.4085.75765.8
97달방20190305109.220.00.1220.4025.73365.5
98달방20190306109.160.00.1270.4075.70965.3
99달방20190307109.1517.00.3460.3925.70565.2