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

댐이름 has constant value ""Constant
일자/시간(t) is highly overall correlated with 강우량(mm)High correlation
저수위(m) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
강우량(mm) is highly overall correlated with 일자/시간(t)High 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
일자/시간(t) has unique valuesUnique
강우량(mm) has 90 (90.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:23:27.348803
Analysis finished2023-12-10 10:23:36.387461
Duration9.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군남
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군남 100
100.0%

Length

2023-12-10T19:23:36.547992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:23:36.704164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 100
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0220726 × 109
Minimum2.0220717 × 109
Maximum2.0220731 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:36.866399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0220717 × 109
5-th percentile2.0220718 × 109
Q12.0220718 × 109
median2.0220729 × 109
Q32.022073 × 109
95-th percentile2.0220731 × 109
Maximum2.0220731 × 109
Range1401
Interquartile range (IQR)1199.5

Descriptive statistics

Standard deviation570.37027
Coefficient of variation (CV)2.820721 × 10-7
Kurtosis-1.5720468
Mean2.0220726 × 109
Median Absolute Deviation (MAD)189
Skewness-0.60211909
Sum2.0220726 × 1011
Variance325322.24
MonotonicityNot monotonic
2023-12-10T19:23:37.145665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022073023 1
 
1.0%
2022071908 1
 
1.0%
2022071813 1
 
1.0%
2022071816 1
 
1.0%
2022071817 1
 
1.0%
2022071819 1
 
1.0%
2022071820 1
 
1.0%
2022071824 1
 
1.0%
2022071901 1
 
1.0%
2022071903 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2022071723 1
1.0%
2022071724 1
1.0%
2022071801 1
1.0%
2022071802 1
1.0%
2022071803 1
1.0%
2022071804 1
1.0%
2022071805 1
1.0%
2022071806 1
1.0%
2022071807 1
1.0%
2022071808 1
1.0%
ValueCountFrequency (%)
2022073124 1
1.0%
2022073123 1
1.0%
2022073122 1
1.0%
2022073121 1
1.0%
2022073120 1
1.0%
2022073119 1
1.0%
2022073118 1
1.0%
2022073117 1
1.0%
2022073116 1
1.0%
2022073115 1
1.0%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.04277
Minimum23.67
Maximum25.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:37.427721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.67
5-th percentile23.6895
Q123.7975
median23.84
Q324.0945
95-th percentile25.13805
Maximum25.27
Range1.6
Interquartile range (IQR)0.297

Descriptive statistics

Standard deviation0.42550837
Coefficient of variation (CV)0.017697976
Kurtosis2.182722
Mean24.04277
Median Absolute Deviation (MAD)0.12
Skewness1.7757931
Sum2404.277
Variance0.18105737
MonotonicityNot monotonic
2023-12-10T19:23:37.682122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.82 10
 
10.0%
23.8 9
 
9.0%
23.84 5
 
5.0%
24.02 3
 
3.0%
23.79 3
 
3.0%
23.67 3
 
3.0%
23.72 2
 
2.0%
23.68 2
 
2.0%
23.73 2
 
2.0%
23.7 2
 
2.0%
Other values (54) 59
59.0%
ValueCountFrequency (%)
23.67 3
3.0%
23.68 2
2.0%
23.69 1
 
1.0%
23.7 2
2.0%
23.71 1
 
1.0%
23.72 2
2.0%
23.73 2
2.0%
23.74 1
 
1.0%
23.744 1
 
1.0%
23.75 1
 
1.0%
ValueCountFrequency (%)
25.27 2
2.0%
25.26 1
1.0%
25.25 1
1.0%
25.234 1
1.0%
25.133 1
1.0%
25.039 1
1.0%
24.993 1
1.0%
24.938 1
1.0%
24.84 1
1.0%
24.777 1
1.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56
Minimum0
Maximum17
Zeros90
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:37.886140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4342672
Coefficient of variation (CV)4.3469056
Kurtosis29.772095
Mean0.56
Median Absolute Deviation (MAD)0
Skewness5.3554859
Sum56
Variance5.9256566
MonotonicityNot monotonic
2023-12-10T19:23:38.443670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 90
90.0%
1 3
 
3.0%
12 2
 
2.0%
3 2
 
2.0%
4 1
 
1.0%
17 1
 
1.0%
2 1
 
1.0%
ValueCountFrequency (%)
0 90
90.0%
1 3
 
3.0%
2 1
 
1.0%
3 2
 
2.0%
4 1
 
1.0%
12 2
 
2.0%
17 1
 
1.0%
ValueCountFrequency (%)
17 1
 
1.0%
12 2
 
2.0%
4 1
 
1.0%
3 2
 
2.0%
2 1
 
1.0%
1 3
 
3.0%
0 90
90.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.45863
Minimum0
Maximum459.608
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:38.666023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.9142
Q192.65575
median100.8
Q3148.035
95-th percentile402.01735
Maximum459.608
Range459.608
Interquartile range (IQR)55.37925

Descriptive statistics

Standard deviation98.853031
Coefficient of variation (CV)0.68429994
Kurtosis3.4342305
Mean144.45863
Median Absolute Deviation (MAD)20.4635
Skewness2.0406539
Sum14445.863
Variance9771.9218
MonotonicityNot monotonic
2023-12-10T19:23:39.020653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.3 9
 
9.0%
93.8 8
 
8.0%
100.8 4
 
4.0%
116.4 2
 
2.0%
92.4 2
 
2.0%
70.7 2
 
2.0%
123.9 2
 
2.0%
76.57 1
 
1.0%
74.295 1
 
1.0%
123.961 1
 
1.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0.0 1
1.0%
70.679 1
1.0%
70.7 2
2.0%
72.234 1
1.0%
72.95 1
1.0%
74.295 1
1.0%
76.57 1
1.0%
77.482 1
1.0%
78.332 1
1.0%
80.269 1
1.0%
ValueCountFrequency (%)
459.608 1
1.0%
457.8 1
1.0%
456.26 1
1.0%
448.036 1
1.0%
442.114 1
1.0%
399.907 1
1.0%
360.45 1
1.0%
351.507 1
1.0%
342.387 1
1.0%
312.002 1
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.01301
Minimum70.7
Maximum457.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:39.352107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70.7
5-th percentile75.24035
Q192.75025
median100.8
Q3150.3685
95-th percentile433.5412
Maximum457.8
Range387.1
Interquartile range (IQR)57.61825

Descriptive statistics

Standard deviation101.01359
Coefficient of variation (CV)0.68246428
Kurtosis3.2049868
Mean148.01301
Median Absolute Deviation (MAD)20.5335
Skewness2.0349334
Sum14801.301
Variance10203.746
MonotonicityNot monotonic
2023-12-10T19:23:39.591760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97.3 9
 
9.0%
93.8 8
 
8.0%
100.8 4
 
4.0%
116.4 2
 
2.0%
92.4 2
 
2.0%
70.7 2
 
2.0%
131.518 1
 
1.0%
161.677 1
 
1.0%
153.265 1
 
1.0%
149.403 1
 
1.0%
Other values (69) 69
69.0%
ValueCountFrequency (%)
70.7 2
2.0%
71.762 1
1.0%
71.867 1
1.0%
73.29 1
1.0%
75.343 1
1.0%
75.378 1
1.0%
77.653 1
1.0%
78.237 1
1.0%
79.415 1
1.0%
81.118 1
1.0%
ValueCountFrequency (%)
457.8 1
1.0%
456.33 1
1.0%
454.592 1
1.0%
453.892 1
1.0%
452.982 1
1.0%
432.518 1
1.0%
395.535 1
1.0%
353.255 1
1.0%
345.45 1
1.0%
319.165 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.033741
Minimum0.7043
Maximum3.0507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:39.839248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.7043
5-th percentile0.71181
Q10.754225
median0.7713
Q30.87755
95-th percentile2.89677
Maximum3.0507
Range2.3464
Interquartile range (IQR)0.123325

Descriptive statistics

Standard deviation0.63760605
Coefficient of variation (CV)0.61679478
Kurtosis4.4049461
Mean1.033741
Median Absolute Deviation (MAD)0.0492
Skewness2.4196928
Sum103.3741
Variance0.40654147
MonotonicityNot monotonic
2023-12-10T19:23:40.084314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7632 10
 
10.0%
0.7552 9
 
9.0%
0.7713 5
 
5.0%
0.8457 3
 
3.0%
0.7513 3
 
3.0%
0.7043 3
 
3.0%
0.7237 2
 
2.0%
0.7082 2
 
2.0%
0.7276 2
 
2.0%
0.7159 2
 
2.0%
Other values (54) 59
59.0%
ValueCountFrequency (%)
0.7043 3
3.0%
0.7082 2
2.0%
0.712 1
 
1.0%
0.7159 2
2.0%
0.7198 1
 
1.0%
0.7237 2
2.0%
0.7276 2
2.0%
0.7315 1
 
1.0%
0.7331 1
 
1.0%
0.7354 1
 
1.0%
ValueCountFrequency (%)
3.0507 2
2.0%
3.0389 1
1.0%
3.0271 1
1.0%
3.0083 1
1.0%
2.8909 1
1.0%
2.7324 1
1.0%
2.5647 1
1.0%
2.4079 1
1.0%
2.2775 1
1.0%
2.1874 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.458
Minimum1
Maximum4.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:23:40.279075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.1
median1.1
Q31.2
95-th percentile4.01
Maximum4.3
Range3.3
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.88844206
Coefficient of variation (CV)0.60935669
Kurtosis4.3312477
Mean1.458
Median Absolute Deviation (MAD)0.1
Skewness2.4043418
Sum145.8
Variance0.78932929
MonotonicityNot monotonic
2023-12-10T19:23:40.496903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.1 36
36.0%
1.0 22
22.0%
1.2 19
19.0%
1.3 6
 
6.0%
4.2 3
 
3.0%
4.3 2
 
2.0%
1.5 1
 
1.0%
1.7 1
 
1.0%
1.8 1
 
1.0%
4.0 1
 
1.0%
Other values (8) 8
 
8.0%
ValueCountFrequency (%)
1.0 22
22.0%
1.1 36
36.0%
1.2 19
19.0%
1.3 6
 
6.0%
1.4 1
 
1.0%
1.5 1
 
1.0%
1.7 1
 
1.0%
1.8 1
 
1.0%
1.9 1
 
1.0%
3.0 1
 
1.0%
ValueCountFrequency (%)
4.3 2
2.0%
4.2 3
3.0%
4.0 1
 
1.0%
3.8 1
 
1.0%
3.6 1
 
1.0%
3.4 1
 
1.0%
3.2 1
 
1.0%
3.1 1
 
1.0%
3.0 1
 
1.0%
1.9 1
 
1.0%

Interactions

2023-12-10T19:23:34.961649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:27.651280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:29.206429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.487327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:31.601528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:32.699180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:33.863943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:35.135143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:27.797020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:29.397015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.641591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:31.772969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:32.897946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:34.093568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:35.311639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:27.974051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:29.632851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.826085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:31.937543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:33.101561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:34.250713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:35.467417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:28.124859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:29.820568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.981006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:32.126624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:33.262195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:34.392461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:35.607540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:28.291387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.003732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:31.131581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:32.281883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:33.396889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:34.513653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:35.761455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:28.450823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.179650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:31.268945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:32.428905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:33.561673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:34.679341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:35.889730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:28.601489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:30.335800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:31.405060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:32.551397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:33.706191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:23:34.825985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:23:40.643346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.0000.8080.0000.8670.8520.3270.323
저수위(m)0.8081.0000.7510.9650.9670.9020.895
강우량(mm)0.0000.7511.0000.6210.6570.3760.389
유입량(ms)0.8670.9650.6211.0000.9590.8790.898
방류량(ms)0.8520.9670.6570.9591.0000.9330.935
저수량(백만m3)0.3270.9020.3760.8790.9331.0000.984
저수율0.3230.8950.3890.8980.9350.9841.000
2023-12-10T19:23:40.836347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
일자/시간(t)1.000-0.3850.504-0.342-0.403-0.404-0.459
저수위(m)-0.3851.0000.2120.9560.9970.9990.967
강우량(mm)0.5040.2121.0000.2220.2040.1920.101
유입량(ms)-0.3420.9560.2221.0000.9550.9550.923
방류량(ms)-0.4030.9970.2040.9551.0000.9970.966
저수량(백만m3)-0.4040.9990.1920.9550.9971.0000.968
저수율-0.4590.9670.1010.9230.9660.9681.000

Missing values

2023-12-10T19:23:36.087814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:23:36.291782image/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
0군남202207302323.82097.397.30.76321.1
1군남202207291223.74084.01485.0970.73151.0
2군남202207180524.9930351.507395.5352.73243.8
3군남202207302423.82097.397.30.76321.1
4군남202207300423.768087.80186.940.74251.0
5군남202207291323.73082.18283.2650.72761.0
6군남202207182124.060144.721145.9150.86271.2
7군남202207180624.840306.672353.2552.56473.6
8군남202207311623.874107.372105.2330.78341.1
9군남202207310123.82097.397.30.76321.1
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
90군남202207310823.83099.499.40.76731.1
91군남202207310723.83099.47798.3380.76731.1
92군남202207310523.82097.397.30.76321.1
93군남202207310423.82097.397.30.76321.1
94군남202207302223.82097.397.30.76321.1
95군남202207302123.82098.13197.020.76321.1
96군남202207301923.8093.893.80.75521.1
97군남202207301823.8093.893.80.75521.1
98군남202207291023.763087.14489.0050.74061.0
99군남202207291123.75085.21686.660.73541.0