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 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 3 other fieldsHigh correlation
댐이름 is highly overall correlated with 저수위(m) and 2 other fieldsHigh correlation
강우량(mm) has 70 (70.0%) zerosZeros
유입량(ms) has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 14:12:15.666798
Analysis finished2023-12-10 14:12:24.667498
Duration9 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
30 
광동
30 
구천
30 
달방
10 

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 30
30.0%
광동 30
30.0%
구천 30
30.0%
달방 10
 
10.0%

Length

2023-12-10T23:12:24.769438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:12:25.025377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감포 30
30.0%
광동 30
30.0%
구천 30
30.0%
달방 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
2023-12-10T23:12:25.200111image/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
2023-12-10T23:12:25.431665image/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 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.8931
Minimum37.87
Maximum672.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:25.685722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.87
5-th percentile37.8995
Q138.0875
median87.405
Q3670.62
95-th percentile672.1625
Maximum672.34
Range634.47
Interquartile range (IQR)632.5325

Descriptive statistics

Standard deviation278.35971
Coefficient of variation (CV)1.1139151
Kurtosis-1.2467113
Mean249.8931
Median Absolute Deviation (MAD)49.365
Skewness0.86257435
Sum24989.31
Variance77484.128
MonotonicityNot monotonic
2023-12-10T23:12:25.929126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
670.62 5
 
5.0%
87.46 3
 
3.0%
670.61 3
 
3.0%
37.88 3
 
3.0%
672.14 3
 
3.0%
87.5 2
 
2.0%
37.9 2
 
2.0%
87.22 2
 
2.0%
87.17 2
 
2.0%
87.39 2
 
2.0%
Other values (66) 73
73.0%
ValueCountFrequency (%)
37.87 1
 
1.0%
37.88 3
3.0%
37.89 1
 
1.0%
37.9 2
2.0%
37.91 1
 
1.0%
37.92 1
 
1.0%
37.93 1
 
1.0%
37.95 1
 
1.0%
37.96 1
 
1.0%
37.97 1
 
1.0%
ValueCountFrequency (%)
672.34 1
 
1.0%
672.33 1
 
1.0%
672.32 1
 
1.0%
672.27 1
 
1.0%
672.21 1
 
1.0%
672.16 1
 
1.0%
672.15 1
 
1.0%
672.14 3
3.0%
672.13 2
2.0%
672.11 1
 
1.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.664679
Minimum0
Maximum43
Zeros70
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:26.167261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile20.350925
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation8.3633641
Coefficient of variation (CV)2.2821546
Kurtosis6.8211037
Mean3.664679
Median Absolute Deviation (MAD)0
Skewness2.6249952
Sum366.4679
Variance69.945859
MonotonicityNot monotonic
2023-12-10T23:12:26.357896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 70
70.0%
1.0 3
 
3.0%
0.4679 2
 
2.0%
5.0 1
 
1.0%
30.0 1
 
1.0%
19.0 1
 
1.0%
32.0 1
 
1.0%
13.0 1
 
1.0%
11.0 1
 
1.0%
8.0 1
 
1.0%
Other values (18) 18
 
18.0%
ValueCountFrequency (%)
0.0 70
70.0%
0.4679 2
 
2.0%
0.5 1
 
1.0%
1.0 3
 
3.0%
1.4037 1
 
1.0%
1.5 1
 
1.0%
1.5321 1
 
1.0%
2.0 1
 
1.0%
3.5 1
 
1.0%
5.0 1
 
1.0%
ValueCountFrequency (%)
43.0 1
1.0%
32.0 1
1.0%
30.0 1
1.0%
28.0 1
1.0%
27.0185 1
1.0%
20.0 1
1.0%
19.9815 1
1.0%
19.0 1
1.0%
18.5 1
1.0%
17.0642 1
1.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57686
Minimum0
Maximum4.283
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:26.595627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02575
median0.1755
Q30.52175
95-th percentile3.1369
Maximum4.283
Range4.283
Interquartile range (IQR)0.496

Descriptive statistics

Standard deviation0.96558239
Coefficient of variation (CV)1.6738592
Kurtosis5.440179
Mean0.57686
Median Absolute Deviation (MAD)0.1585
Skewness2.426204
Sum57.686
Variance0.93234935
MonotonicityNot monotonic
2023-12-10T23:12:26.848637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
0.016 6
 
6.0%
0.019 5
 
5.0%
0.014 2
 
2.0%
0.219 2
 
2.0%
0.13 2
 
2.0%
0.018 2
 
2.0%
0.434 1
 
1.0%
0.131 1
 
1.0%
0.162 1
 
1.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.012 1
 
1.0%
0.013 1
 
1.0%
0.014 2
 
2.0%
0.015 1
 
1.0%
0.016 6
6.0%
0.017 1
 
1.0%
0.018 2
 
2.0%
0.019 5
5.0%
0.028 1
 
1.0%
ValueCountFrequency (%)
4.283 1
1.0%
4.17 1
1.0%
3.837 1
1.0%
3.527 1
1.0%
3.439 1
1.0%
3.121 1
1.0%
2.371 1
1.0%
2.313 1
1.0%
2.197 1
1.0%
2.006 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42066
Minimum0.02
Maximum3.729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:27.131779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.0319
Q10.04
median0.298
Q30.33275
95-th percentile1.36145
Maximum3.729
Range3.709
Interquartile range (IQR)0.29275

Descriptive statistics

Standard deviation0.66980219
Coefficient of variation (CV)1.592265
Kurtosis13.193305
Mean0.42066
Median Absolute Deviation (MAD)0.1175
Skewness3.5023723
Sum42.066
Variance0.44863497
MonotonicityNot monotonic
2023-12-10T23:12:27.479439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 6
 
6.0%
0.037 6
 
6.0%
0.033 2
 
2.0%
0.331 2
 
2.0%
0.305 2
 
2.0%
0.312 2
 
2.0%
0.297 2
 
2.0%
0.303 2
 
2.0%
0.291 2
 
2.0%
0.314 2
 
2.0%
Other values (64) 72
72.0%
ValueCountFrequency (%)
0.02 1
1.0%
0.026 1
1.0%
0.028 1
1.0%
0.029 1
1.0%
0.03 1
1.0%
0.032 1
1.0%
0.033 2
2.0%
0.034 2
2.0%
0.035 2
2.0%
0.036 2
2.0%
ValueCountFrequency (%)
3.729 1
1.0%
3.655 1
1.0%
2.876 1
1.0%
2.853 1
1.0%
1.446 1
1.0%
1.357 1
1.0%
1.34 1
1.0%
1.268 1
1.0%
1.174 1
1.0%
1.149 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.82905
Minimum1.986
Maximum9.508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:28.154167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.986
5-th percentile1.9919
Q12.02475
median6.849
Q37.97
95-th percentile9.34235
Maximum9.508
Range7.522
Interquartile range (IQR)5.94525

Descriptive statistics

Standard deviation2.7228363
Coefficient of variation (CV)0.46711493
Kurtosis-1.3095126
Mean5.82905
Median Absolute Deviation (MAD)1.358
Skewness-0.41422641
Sum582.905
Variance7.4138375
MonotonicityNot monotonic
2023-12-10T23:12:28.379611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.97 5
 
5.0%
6.933 3
 
3.0%
7.961 3
 
3.0%
1.988 3
 
3.0%
9.322 3
 
3.0%
6.949 2
 
2.0%
1.992 2
 
2.0%
6.839 2
 
2.0%
6.819 2
 
2.0%
6.906 2
 
2.0%
Other values (66) 73
73.0%
ValueCountFrequency (%)
1.986 1
 
1.0%
1.988 3
3.0%
1.99 1
 
1.0%
1.992 2
2.0%
1.993 1
 
1.0%
1.995 1
 
1.0%
1.997 1
 
1.0%
2.0 1
 
1.0%
2.002 1
 
1.0%
2.004 1
 
1.0%
ValueCountFrequency (%)
9.508 1
 
1.0%
9.498 1
 
1.0%
9.489 1
 
1.0%
9.442 1
 
1.0%
9.387 1
 
1.0%
9.34 1
 
1.0%
9.331 1
 
1.0%
9.322 3
3.0%
9.312 2
2.0%
9.294 1
 
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.655
Minimum60.6
Maximum77.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T23:12:28.634013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60.6
5-th percentile60.7
Q167.475
median69.2
Q375.525
95-th percentile76.8
Maximum77.3
Range16.7
Interquartile range (IQR)8.05

Descriptive statistics

Standard deviation5.3726275
Coefficient of variation (CV)0.077131972
Kurtosis-1.0845805
Mean69.655
Median Absolute Deviation (MAD)6.2
Skewness-0.22012032
Sum6965.5
Variance28.865126
MonotonicityNot monotonic
2023-12-10T23:12:28.904162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.7 5
 
5.0%
75.4 4
 
4.0%
63.0 3
 
3.0%
69.2 3
 
3.0%
60.6 3
 
3.0%
75.6 3
 
3.0%
71.0 3
 
3.0%
69.4 3
 
3.0%
67.9 2
 
2.0%
69.1 2
 
2.0%
Other values (49) 69
69.0%
ValueCountFrequency (%)
60.6 3
3.0%
60.7 5
5.0%
60.8 1
 
1.0%
61.1 1
 
1.0%
61.2 1
 
1.0%
61.4 1
 
1.0%
62.2 1
 
1.0%
62.6 1
 
1.0%
62.7 1
 
1.0%
62.8 2
 
2.0%
ValueCountFrequency (%)
77.3 1
1.0%
77.2 1
1.0%
77.0 2
2.0%
76.8 2
2.0%
76.7 1
1.0%
76.6 2
2.0%
76.5 2
2.0%
76.4 2
2.0%
76.3 1
1.0%
76.2 2
2.0%

Interactions

2023-12-10T23:12:22.779979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:16.119935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:17.378780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:18.455078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.781639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.709136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.663743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:22.980781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:16.280221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:17.543596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:18.600136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.949502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.853488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.810667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:23.203017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:16.422262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:17.693583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.102544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.084168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.988709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.953001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:23.370562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:16.594437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:17.853593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.216673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.197201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.100229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:22.135415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:23.570734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:16.845835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:18.004350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.334197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.321732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.219432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:22.289809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:23.858253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:17.067015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:18.151267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.484452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.438882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.336162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:22.464988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:24.080235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:17.219343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:18.296195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:19.621124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:20.580368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:21.460675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:12:22.621508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:12:29.072168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
댐이름1.0000.0001.0000.0000.5240.5351.0000.998
일자/시간(t)0.0001.0000.0000.5000.4600.4920.3920.665
저수위(m)1.0000.0001.0000.0000.7670.8031.0000.968
강우량(mm)0.0000.5000.0001.0000.0000.3720.0000.000
유입량(ms)0.5240.4600.7670.0001.0000.8570.6940.773
방류량(ms)0.5350.4920.8030.3720.8571.0000.8440.804
저수량(백만m3)1.0000.3921.0000.0000.6940.8441.0000.954
저수율0.9980.6650.9680.0000.7730.8040.9541.000
2023-12-10T23:12:29.300965image/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)1.000-0.1180.2360.1800.1120.0020.2410.000
저수위(m)-0.1181.000-0.0710.8660.8780.928-0.5330.995
강우량(mm)0.236-0.0711.0000.104-0.089-0.096-0.1040.000
유입량(ms)0.1800.8660.1041.0000.8580.883-0.5200.352
방류량(ms)0.1120.878-0.0890.8581.0000.950-0.5070.369
저수량(백만m3)0.0020.928-0.0960.8830.9501.000-0.4520.990
저수율0.241-0.533-0.104-0.520-0.507-0.4521.0000.946
댐이름0.0000.9950.0000.3520.3690.9900.9461.000

Missing values

2023-12-10T23:12:24.344454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:12:24.592417image/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
0감포2019040138.150.00.00.0332.03677.3
1감포2019040238.140.00.0160.0372.03477.2
2감포2019040338.120.00.00.0262.03177.0
3감포2019040438.110.00.0190.042.02977.0
4감포2019040538.090.00.00.0322.02576.8
5감포2019040638.080.00.0190.042.02476.8
6감포2019040738.070.00.0190.042.02276.7
7감포2019040838.050.00.00.0412.01876.6
8감포2019040938.049.50.0170.0382.01776.5
9감포2019041038.0518.50.0560.0352.01876.6
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90달방20190401108.670.00.2010.2015.51563.0
91달방20190402108.670.00.1790.1795.51563.0
92달방20190403108.660.00.1370.1825.51163.0
93달방20190404108.630.00.0360.1725.49962.9
94달방20190405108.630.00.1870.1875.49962.9
95달방20190406108.611.00.1220.2125.49162.8
96달방20190407108.611.00.1980.1985.49162.8
97달방20190408108.590.00.1050.1955.48362.7
98달방20190409108.5719.00.130.225.47662.6
99달방20190410108.7430.00.9450.1765.54263.4