Overview

Dataset statistics

Number of variables8
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory75.4 B

Variable types

Categorical1
Numeric7

Alerts

댐이름 has constant value ""Constant
일자/시간(t) is highly overall correlated with 유입량(ms) and 1 other fieldsHigh correlation
저수위(m) is highly overall correlated with 저수량(백만m3) and 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 일자/시간(t) and 1 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 일자/시간(t) and 1 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
저수율 is highly overall correlated with 저수위(m) and 1 other fieldsHigh correlation
일자/시간(t) has unique valuesUnique
유입량(ms) has unique valuesUnique
방류량(ms) has unique valuesUnique
강우량(mm) has 8 (26.7%) zerosZeros

Reproduction

Analysis started2023-12-10 10:41:46.232639
Analysis finished2023-12-10 10:41:57.176764
Duration10.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
낙동강하굿둑
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
낙동강하굿둑 30
100.0%

Length

2023-12-10T19:41:57.313532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:41:57.496288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 30
100.0%

일자/시간(t)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190416
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:57.693385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190402
Q120190408
median20190416
Q320190423
95-th percentile20190429
Maximum20190430
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)4.3601918 × 10-7
Kurtosis-1.2
Mean20190416
Median Absolute Deviation (MAD)7.5
Skewness0
Sum6.0571246 × 108
Variance77.5
MonotonicityStrictly increasing
2023-12-10T19:41:57.987856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190401 1
 
3.3%
20190417 1
 
3.3%
20190430 1
 
3.3%
20190429 1
 
3.3%
20190428 1
 
3.3%
20190427 1
 
3.3%
20190426 1
 
3.3%
20190425 1
 
3.3%
20190424 1
 
3.3%
20190423 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
20190401 1
3.3%
20190402 1
3.3%
20190403 1
3.3%
20190404 1
3.3%
20190405 1
3.3%
20190406 1
3.3%
20190407 1
3.3%
20190408 1
3.3%
20190409 1
3.3%
20190410 1
3.3%
ValueCountFrequency (%)
20190430 1
3.3%
20190429 1
3.3%
20190428 1
3.3%
20190427 1
3.3%
20190426 1
3.3%
20190425 1
3.3%
20190424 1
3.3%
20190423 1
3.3%
20190422 1
3.3%
20190421 1
3.3%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.857
Minimum0.69
Maximum0.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:58.212270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.69
5-th percentile0.8035
Q10.8325
median0.86
Q30.89
95-th percentile0.9155
Maximum0.93
Range0.24
Interquartile range (IQR)0.0575

Descriptive statistics

Standard deviation0.046174332
Coefficient of variation (CV)0.053879034
Kurtosis4.808077
Mean0.857
Median Absolute Deviation (MAD)0.03
Skewness-1.4897837
Sum25.71
Variance0.002132069
MonotonicityNot monotonic
2023-12-10T19:41:58.429611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.89 4
13.3%
0.84 4
13.3%
0.83 4
13.3%
0.86 3
10.0%
0.88 2
6.7%
0.85 2
6.7%
0.91 2
6.7%
0.82 2
6.7%
0.87 2
6.7%
0.93 1
 
3.3%
Other values (4) 4
13.3%
ValueCountFrequency (%)
0.69 1
 
3.3%
0.79 1
 
3.3%
0.82 2
6.7%
0.83 4
13.3%
0.84 4
13.3%
0.85 2
6.7%
0.86 3
10.0%
0.87 2
6.7%
0.88 2
6.7%
0.89 4
13.3%
ValueCountFrequency (%)
0.93 1
 
3.3%
0.92 1
 
3.3%
0.91 2
6.7%
0.9 1
 
3.3%
0.89 4
13.3%
0.88 2
6.7%
0.87 2
6.7%
0.86 3
10.0%
0.85 2
6.7%
0.84 4
13.3%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7887067
Minimum0
Maximum26.5754
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:58.661831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0002
median0.0177
Q30.611175
95-th percentile16.316835
Maximum26.5754
Range26.5754
Interquartile range (IQR)0.610975

Descriptive statistics

Standard deviation6.4407273
Coefficient of variation (CV)2.309575
Kurtosis7.7353326
Mean2.7887067
Median Absolute Deviation (MAD)0.0177
Skewness2.8004314
Sum83.6612
Variance41.482968
MonotonicityNot monotonic
2023-12-10T19:41:58.999349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 8
26.7%
0.0116 1
 
3.3%
0.0171 1
 
3.3%
0.055 1
 
3.3%
26.5754 1
 
3.3%
0.0456 1
 
3.3%
0.0344 1
 
3.3%
7.8706 1
 
3.3%
2.5552 1
 
3.3%
8.1384 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0.0 8
26.7%
0.0008 1
 
3.3%
0.0012 1
 
3.3%
0.0088 1
 
3.3%
0.0094 1
 
3.3%
0.0116 1
 
3.3%
0.012 1
 
3.3%
0.0171 1
 
3.3%
0.0183 1
 
3.3%
0.0344 1
 
3.3%
ValueCountFrequency (%)
26.5754 1
3.3%
21.5385 1
3.3%
9.9348 1
3.3%
8.1384 1
3.3%
7.8706 1
3.3%
5.6665 1
3.3%
2.5552 1
3.3%
0.682 1
3.3%
0.3987 1
3.3%
0.055 1
3.3%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.04903
Minimum16.207
Maximum651.484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:59.247876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.207
5-th percentile28.13515
Q165.4115
median95.973
Q3148.95325
95-th percentile200.7435
Maximum651.484
Range635.277
Interquartile range (IQR)83.54175

Descriptive statistics

Standard deviation112.25224
Coefficient of variation (CV)0.94290763
Kurtosis18.258804
Mean119.04903
Median Absolute Deviation (MAD)34.1265
Skewness3.8719287
Sum3571.471
Variance12600.566
MonotonicityNot monotonic
2023-12-10T19:41:59.498615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
23.875 1
 
3.3%
64.085 1
 
3.3%
651.484 1
 
3.3%
120.284 1
 
3.3%
98.581 1
 
3.3%
165.108 1
 
3.3%
131.992 1
 
3.3%
129.215 1
 
3.3%
95.592 1
 
3.3%
108.596 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
16.207 1
3.3%
23.875 1
3.3%
33.342 1
3.3%
52.885 1
3.3%
54.486 1
3.3%
60.962 1
3.3%
64.085 1
3.3%
64.279 1
3.3%
68.809 1
3.3%
69.495 1
3.3%
ValueCountFrequency (%)
651.484 1
3.3%
218.199 1
3.3%
179.409 1
3.3%
165.108 1
3.3%
164.303 1
3.3%
163.61 1
3.3%
158.355 1
3.3%
154.607 1
3.3%
131.992 1
3.3%
129.215 1
3.3%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.19133
Minimum26.908
Maximum598.391
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:41:59.737940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.908
5-th percentile38.73855
Q157.80625
median96.603
Q3149.56925
95-th percentile201.6073
Maximum598.391
Range571.483
Interquartile range (IQR)91.763

Descriptive statistics

Standard deviation103.47844
Coefficient of variation (CV)0.85384358
Kurtosis16.006132
Mean121.19133
Median Absolute Deviation (MAD)41.419
Skewness3.546609
Sum3635.74
Variance10707.788
MonotonicityNot monotonic
2023-12-10T19:41:59.998816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
34.635 1
 
3.3%
90.946 1
 
3.3%
598.391 1
 
3.3%
189.394 1
 
3.3%
136.086 1
 
3.3%
181.25 1
 
3.3%
94.39 1
 
3.3%
123.865 1
 
3.3%
111.672 1
 
3.3%
108.601 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
26.908 1
3.3%
34.635 1
3.3%
43.754 1
3.3%
47.963 1
3.3%
52.742 1
3.3%
54.781 1
3.3%
55.587 1
3.3%
55.612 1
3.3%
64.389 1
3.3%
68.982 1
3.3%
ValueCountFrequency (%)
598.391 1
3.3%
211.6 1
3.3%
189.394 1
3.3%
186.014 1
3.3%
181.25 1
3.3%
164.298 1
3.3%
159.961 1
3.3%
153.0 1
3.3%
139.277 1
3.3%
136.086 1
3.3%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.77307
Minimum294.088
Maximum305.159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:42:00.228883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294.088
5-th percentile299.2978
Q1300.63575
median301.908
Q3303.299
95-th percentile304.48475
Maximum305.159
Range11.071
Interquartile range (IQR)2.66325

Descriptive statistics

Standard deviation2.1326031
Coefficient of variation (CV)0.00706691
Kurtosis4.7144046
Mean301.77307
Median Absolute Deviation (MAD)1.388
Skewness-1.4676922
Sum9053.192
Variance4.547996
MonotonicityNot monotonic
2023-12-10T19:42:00.584491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
303.299 4
13.3%
300.983 4
13.3%
300.52 4
13.3%
301.908 3
10.0%
302.835 2
6.7%
301.445 2
6.7%
304.229 2
6.7%
300.059 2
6.7%
302.372 2
6.7%
305.159 1
 
3.3%
Other values (4) 4
13.3%
ValueCountFrequency (%)
294.088 1
 
3.3%
298.675 1
 
3.3%
300.059 2
6.7%
300.52 4
13.3%
300.983 4
13.3%
301.445 2
6.7%
301.908 3
10.0%
302.372 2
6.7%
302.835 2
6.7%
303.299 4
13.3%
ValueCountFrequency (%)
305.159 1
 
3.3%
304.694 1
 
3.3%
304.229 2
6.7%
303.764 1
 
3.3%
303.299 4
13.3%
302.835 2
6.7%
302.372 2
6.7%
301.908 3
10.0%
301.445 2
6.7%
300.983 4
13.3%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.235333
Minimum95.73
Maximum99.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T19:42:00.788702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.73
5-th percentile97.4325
Q197.8675
median98.28
Q398.73
95-th percentile99.1125
Maximum99.34
Range3.61
Interquartile range (IQR)0.8625

Descriptive statistics

Standard deviation0.69316234
Coefficient of variation (CV)0.0070561407
Kurtosis4.7954379
Mean98.235333
Median Absolute Deviation (MAD)0.45
Skewness-1.4837761
Sum2947.06
Variance0.48047402
MonotonicityNot monotonic
2023-12-10T19:42:01.002918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
98.73 4
13.3%
97.98 4
13.3%
97.83 4
13.3%
98.28 3
10.0%
98.58 2
6.7%
98.13 2
6.7%
99.03 2
6.7%
97.68 2
6.7%
98.43 2
6.7%
99.34 1
 
3.3%
Other values (4) 4
13.3%
ValueCountFrequency (%)
95.73 1
 
3.3%
97.23 1
 
3.3%
97.68 2
6.7%
97.83 4
13.3%
97.98 4
13.3%
98.13 2
6.7%
98.28 3
10.0%
98.43 2
6.7%
98.58 2
6.7%
98.73 4
13.3%
ValueCountFrequency (%)
99.34 1
 
3.3%
99.18 1
 
3.3%
99.03 2
6.7%
98.88 1
 
3.3%
98.73 4
13.3%
98.58 2
6.7%
98.43 2
6.7%
98.28 3
10.0%
98.13 2
6.7%
97.98 4
13.3%

Interactions

2023-12-10T19:41:54.742474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:46.762748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:48.182578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:49.449805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:50.761993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:52.211312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:53.546041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:54.930642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:46.919006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:48.362636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:49.626989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:51.001595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:52.413462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:53.719946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:55.173294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:47.084991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:48.533766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:49.790945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:51.158576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:52.571731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:53.858943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:55.346994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:47.253825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:48.730858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:49.979980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:51.325536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:52.776874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:54.016128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:55.521258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:47.417855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:48.918948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:50.179508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:51.511399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:52.966582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:54.192301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:55.699611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:47.620142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:49.086756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:50.337277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:51.823153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:53.150388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:54.358703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:55.884272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:47.928597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:49.228426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:50.503502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:52.032095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:53.336689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:41:54.516860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:42:01.174847image/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)1.0000.3190.0000.7920.7460.4580.319
저수위(m)0.3191.0000.6150.6200.6191.0001.000
강우량(mm)0.0000.6151.0000.0000.0000.0000.615
유입량(ms)0.7920.6200.0001.0000.9440.6050.620
방류량(ms)0.7460.6190.0000.9441.0000.5910.619
저수량(백만m3)0.4581.0000.0000.6050.5911.0001.000
저수율0.3191.0000.6150.6200.6191.0001.000
2023-12-10T19:42:01.410732image/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)1.000-0.2420.2690.5490.582-0.242-0.242
저수위(m)-0.2421.000-0.039-0.085-0.4061.0001.000
강우량(mm)0.269-0.0391.0000.2690.315-0.039-0.039
유입량(ms)0.549-0.0850.2691.0000.882-0.085-0.085
방류량(ms)0.582-0.4060.3150.8821.000-0.406-0.406
저수량(백만m3)-0.2421.000-0.039-0.085-0.4061.0001.000
저수율-0.2421.000-0.039-0.085-0.4061.0001.000

Missing values

2023-12-10T19:41:56.842324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:41:57.080364image/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
0낙동강하굿둑201904010.890.011623.87534.635303.29998.73
1낙동강하굿둑201904020.930.000869.49547.963305.15999.34
2낙동강하굿둑201904030.880.008864.27991.174302.83598.58
3낙동강하굿둑201904040.840.001233.34254.781300.98397.98
4낙동강하굿둑201904050.850.009460.96255.612301.44598.13
5낙동강하굿둑201904060.830.016.20726.908300.5297.83
6낙동강하굿둑201904070.860.68268.80952.742301.90898.28
7낙동강하굿둑201904080.880.018354.48643.754302.83598.58
8낙동강하굿둑201904090.9121.538571.71755.587304.22999.03
9낙동강하굿둑201904100.839.934896.354139.277300.5297.83
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
20낙동강하굿둑201904210.890.080.49364.389303.29998.73
21낙동강하굿둑201904220.870.071.95182.685302.37298.43
22낙동강하굿둑201904230.875.6665108.596108.601302.37298.43
23낙동강하굿둑201904240.848.138495.592111.672300.98397.98
24낙동강하굿둑201904250.852.5552129.215123.865301.44598.13
25낙동강하굿둑201904260.927.8706131.99294.39304.69499.18
26낙동강하굿둑201904270.890.0344165.108181.25303.29998.73
27낙동강하굿둑201904280.820.045698.581136.086300.05997.68
28낙동강하굿둑201904290.6926.5754120.284189.394294.08895.73
29낙동강하굿둑201904300.790.055651.484598.391298.67597.23