Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory75.3 B

Variable types

Categorical1
Numeric7

Alerts

댐이름 has constant value ""Constant
저수위(m) is highly overall correlated with 저수량(백만m3) and 1 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 방류량(ms)High correlation
방류량(ms) is highly overall correlated with 유입량(ms)High 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
방류량(ms) has unique valuesUnique
강우량(mm) has 12 (38.7%) zerosZeros
유입량(ms) has 2 (6.5%) zerosZeros

Reproduction

Analysis started2024-04-17 13:01:56.250227
Analysis finished2024-04-17 13:02:00.787988
Duration4.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐이름
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
낙동강하굿둑
31 

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 31
100.0%

Length

2024-04-17T22:02:00.839877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T22:02:00.919296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
낙동강하굿둑 31
100.0%

일자/시간(t)
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190516
Minimum20190501
Maximum20190531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:01.003043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190501
5-th percentile20190502
Q120190508
median20190516
Q320190524
95-th percentile20190530
Maximum20190531
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)4.5031643 × 10-7
Kurtosis-1.2
Mean20190516
Median Absolute Deviation (MAD)8
Skewness0
Sum6.25906 × 108
Variance82.666667
MonotonicityStrictly increasing
2024-04-17T22:02:01.111456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20190501 1
 
3.2%
20190502 1
 
3.2%
20190531 1
 
3.2%
20190530 1
 
3.2%
20190529 1
 
3.2%
20190528 1
 
3.2%
20190527 1
 
3.2%
20190526 1
 
3.2%
20190525 1
 
3.2%
20190524 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
20190501 1
3.2%
20190502 1
3.2%
20190503 1
3.2%
20190504 1
3.2%
20190505 1
3.2%
20190506 1
3.2%
20190507 1
3.2%
20190508 1
3.2%
20190509 1
3.2%
20190510 1
3.2%
ValueCountFrequency (%)
20190531 1
3.2%
20190530 1
3.2%
20190529 1
3.2%
20190528 1
3.2%
20190527 1
3.2%
20190526 1
3.2%
20190525 1
3.2%
20190524 1
3.2%
20190523 1
3.2%
20190522 1
3.2%

저수위(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84193548
Minimum0.71
Maximum0.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:01.220951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.71
5-th percentile0.74
Q10.815
median0.86
Q30.875
95-th percentile0.915
Maximum0.94
Range0.23
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.054001195
Coefficient of variation (CV)0.06413935
Kurtosis0.35863797
Mean0.84193548
Median Absolute Deviation (MAD)0.03
Skewness-0.65533027
Sum26.1
Variance0.002916129
MonotonicityNot monotonic
2024-04-17T22:02:01.318076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.86 7
22.6%
0.89 4
12.9%
0.83 3
 
9.7%
0.85 2
 
6.5%
0.8 2
 
6.5%
0.71 1
 
3.2%
0.82 1
 
3.2%
0.81 1
 
3.2%
0.88 1
 
3.2%
0.9 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
0.71 1
 
3.2%
0.73 1
 
3.2%
0.75 1
 
3.2%
0.77 1
 
3.2%
0.78 1
 
3.2%
0.8 2
6.5%
0.81 1
 
3.2%
0.82 1
 
3.2%
0.83 3
9.7%
0.84 1
 
3.2%
ValueCountFrequency (%)
0.94 1
 
3.2%
0.93 1
 
3.2%
0.9 1
 
3.2%
0.89 4
12.9%
0.88 1
 
3.2%
0.87 1
 
3.2%
0.86 7
22.6%
0.85 2
 
6.5%
0.84 1
 
3.2%
0.83 3
9.7%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.618371
Minimum0
Maximum20.9591
Zeros12
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:01.422647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.015
Q30.2539
95-th percentile12.6054
Maximum20.9591
Range20.9591
Interquartile range (IQR)0.2539

Descriptive statistics

Standard deviation5.1857362
Coefficient of variation (CV)3.2042939
Kurtosis11.873652
Mean1.618371
Median Absolute Deviation (MAD)0.015
Skewness3.576748
Sum50.1695
Variance26.89186
MonotonicityNot monotonic
2024-04-17T22:02:01.511966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 12
38.7%
0.0414 1
 
3.2%
4.6134 1
 
3.2%
0.7944 1
 
3.2%
0.0296 1
 
3.2%
0.0297 1
 
3.2%
0.0066 1
 
3.2%
20.5974 1
 
3.2%
0.0124 1
 
3.2%
0.0964 1
 
3.2%
Other values (10) 10
32.3%
ValueCountFrequency (%)
0.0 12
38.7%
0.0059 1
 
3.2%
0.0066 1
 
3.2%
0.0124 1
 
3.2%
0.015 1
 
3.2%
0.0247 1
 
3.2%
0.0277 1
 
3.2%
0.0296 1
 
3.2%
0.0297 1
 
3.2%
0.0414 1
 
3.2%
ValueCountFrequency (%)
20.9591 1
3.2%
20.5974 1
3.2%
4.6134 1
3.2%
1.2155 1
3.2%
0.7946 1
3.2%
0.7944 1
3.2%
0.4503 1
3.2%
0.4114 1
3.2%
0.0964 1
3.2%
0.044 1
3.2%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.28955
Minimum0
Maximum607.357
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:01.605772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.599
Q1102.11
median136.442
Q3171.519
95-th percentile351.8295
Maximum607.357
Range607.357
Interquartile range (IQR)69.409

Descriptive statistics

Standard deviation117.84473
Coefficient of variation (CV)0.77382018
Kurtosis7.3505571
Mean152.28955
Median Absolute Deviation (MAD)35.437
Skewness2.2557963
Sum4720.976
Variance13887.379
MonotonicityNot monotonic
2024-04-17T22:02:01.723417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 2
 
6.5%
92.303 1
 
3.2%
168.493 1
 
3.2%
105.486 1
 
3.2%
278.385 1
 
3.2%
171.159 1
 
3.2%
217.518 1
 
3.2%
98.734 1
 
3.2%
136.442 1
 
3.2%
111.169 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
0.0 2
6.5%
5.198 1
3.2%
32.113 1
3.2%
77.834 1
3.2%
83.743 1
3.2%
92.303 1
3.2%
98.734 1
3.2%
105.486 1
3.2%
111.169 1
3.2%
111.372 1
3.2%
ValueCountFrequency (%)
607.357 1
3.2%
425.274 1
3.2%
278.385 1
3.2%
217.518 1
3.2%
197.567 1
3.2%
195.178 1
3.2%
180.111 1
3.2%
171.879 1
3.2%
171.159 1
3.2%
168.493 1
3.2%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.95784
Minimum3.826
Maximum649.864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:01.844454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.826
5-th percentile9.9535
Q189.0625
median142.448
Q3172.924
95-th percentile309.192
Maximum649.864
Range646.038
Interquartile range (IQR)83.8615

Descriptive statistics

Standard deviation119.8873
Coefficient of variation (CV)0.79947338
Kurtosis9.6533873
Mean149.95784
Median Absolute Deviation (MAD)49.075
Skewness2.5322077
Sum4648.693
Variance14372.965
MonotonicityNot monotonic
2024-04-17T22:02:01.948526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
649.864 1
 
3.2%
345.341 1
 
3.2%
152.389 1
 
3.2%
84.083 1
 
3.2%
273.043 1
 
3.2%
181.841 1
 
3.2%
233.578 1
 
3.2%
93.373 1
 
3.2%
152.527 1
 
3.2%
121.918 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
3.826 1
3.2%
4.047 1
3.2%
15.86 1
3.2%
33.684 1
3.2%
53.543 1
3.2%
61.767 1
3.2%
84.083 1
3.2%
84.752 1
3.2%
93.373 1
3.2%
100.285 1
3.2%
ValueCountFrequency (%)
649.864 1
3.2%
345.341 1
3.2%
273.043 1
3.2%
233.578 1
3.2%
213.767 1
3.2%
197.564 1
3.2%
193.421 1
3.2%
181.841 1
3.2%
164.007 1
3.2%
163.092 1
3.2%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.07723
Minimum295.002
Maximum305.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:02.367098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum295.002
5-th percentile296.377
Q1299.828
median301.908
Q3302.6035
95-th percentile304.4615
Maximum305.625
Range10.623
Interquartile range (IQR)2.7755

Descriptive statistics

Standard deviation2.4941497
Coefficient of variation (CV)0.0082840862
Kurtosis0.34580176
Mean301.07723
Median Absolute Deviation (MAD)1.391
Skewness-0.64387369
Sum9333.394
Variance6.2207826
MonotonicityNot monotonic
2024-04-17T22:02:02.519121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
301.908 7
22.6%
303.299 4
12.9%
300.52 3
 
9.7%
301.445 2
 
6.5%
299.136 2
 
6.5%
295.002 1
 
3.2%
300.059 1
 
3.2%
299.597 1
 
3.2%
302.835 1
 
3.2%
303.764 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
295.002 1
 
3.2%
295.918 1
 
3.2%
296.836 1
 
3.2%
297.755 1
 
3.2%
298.215 1
 
3.2%
299.136 2
6.5%
299.597 1
 
3.2%
300.059 1
 
3.2%
300.52 3
9.7%
300.983 1
 
3.2%
ValueCountFrequency (%)
305.625 1
 
3.2%
305.159 1
 
3.2%
303.764 1
 
3.2%
303.299 4
12.9%
302.835 1
 
3.2%
302.372 1
 
3.2%
301.908 7
22.6%
301.445 2
 
6.5%
300.983 1
 
3.2%
300.52 3
9.7%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.009677
Minimum96.03
Maximum99.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-04-17T22:02:02.642902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96.03
5-th percentile96.48
Q197.605
median98.28
Q398.505
95-th percentile99.11
Maximum99.49
Range3.46
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.81116987
Coefficient of variation (CV)0.0082764263
Kurtosis0.36097387
Mean98.009677
Median Absolute Deviation (MAD)0.45
Skewness-0.64720868
Sum3038.3
Variance0.65799656
MonotonicityNot monotonic
2024-04-17T22:02:02.783711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
98.28 7
22.6%
98.73 4
12.9%
97.83 3
 
9.7%
98.13 2
 
6.5%
97.38 2
 
6.5%
96.03 1
 
3.2%
97.68 1
 
3.2%
97.53 1
 
3.2%
98.58 1
 
3.2%
98.88 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
96.03 1
 
3.2%
96.33 1
 
3.2%
96.63 1
 
3.2%
96.93 1
 
3.2%
97.08 1
 
3.2%
97.38 2
6.5%
97.53 1
 
3.2%
97.68 1
 
3.2%
97.83 3
9.7%
97.98 1
 
3.2%
ValueCountFrequency (%)
99.49 1
 
3.2%
99.34 1
 
3.2%
98.88 1
 
3.2%
98.73 4
12.9%
98.58 1
 
3.2%
98.43 1
 
3.2%
98.28 7
22.6%
98.13 2
 
6.5%
97.98 1
 
3.2%
97.83 3
9.7%

Interactions

2024-04-17T22:02:00.100440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.433644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.956674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.702807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.276857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.954058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.522422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.181351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.507240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.029692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.801215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.374885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.036546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.594224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.251344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.593980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.346153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.877559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.458066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.115810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.667211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.320471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.664119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.404518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.941659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.538543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.201099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.764538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.400405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.737438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.475662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.046245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.643234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.288620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.841910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.472679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.809754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.541833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.128450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.766892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.373277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.931135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.543130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:56.882449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:57.611061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.199096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:58.861474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:01:59.443156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T22:02:00.011347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T22:02:02.887595image/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)1.0000.0000.1840.4270.2950.0000.000
저수위(m)0.0001.0000.0000.3720.5661.0001.000
강우량(mm)0.1840.0001.0000.0000.0000.0000.000
유입량(ms)0.4270.3720.0001.0000.9760.1870.372
방류량(ms)0.2950.5660.0000.9761.0000.4950.566
저수량(백만m3)0.0001.0000.0000.1870.4951.0001.000
저수율0.0001.0000.0000.3720.5661.0001.000
2024-04-17T22:02:03.028799image/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)1.0000.0780.278-0.053-0.0830.0780.078
저수위(m)0.0781.000-0.4310.2420.0581.0001.000
강우량(mm)0.278-0.4311.0000.0860.037-0.431-0.431
유입량(ms)-0.0530.2420.0861.0000.9210.2420.242
방류량(ms)-0.0830.0580.0370.9211.0000.0580.058
저수량(백만m3)0.0781.000-0.4310.2420.0581.0001.000
저수율0.0781.000-0.4310.2420.0581.0001.000

Missing values

2024-04-17T22:02:00.644692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T22:02:00.748510image/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
0낙동강하굿둑201905010.710.0414607.357649.864295.00296.03
1낙동강하굿둑201905020.860.0247425.274345.341301.90898.28
2낙동강하굿둑201905030.860.0197.567197.564301.90898.28
3낙동강하굿둑201905040.890.0149.125133.021303.29998.73
4낙동강하굿둑201905050.890.0118.699118.695303.29998.73
5낙동강하굿둑201905060.80.4114112.054160.237299.13697.38
6낙동강하굿둑201905070.860.0059195.178163.092301.90898.28
7낙동강하굿둑201905080.930.0146.315108.683305.15999.34
8낙동강하굿둑201905090.830.015160.08213.767300.5297.83
9낙동강하굿둑201905100.870.0121.715100.285302.37298.43
댐이름일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율
21낙동강하굿둑201905220.940.0133.15106.225305.62599.49
22낙동강하굿둑201905230.90.0171.879193.421303.76498.88
23낙동강하굿둑201905240.880.0111.169121.918302.83598.58
24낙동강하굿둑201905250.850.0136.442152.527301.44598.13
25낙동강하굿둑201905260.860.012498.73493.373301.90898.28
26낙동강하굿둑201905270.8320.5974217.518233.578300.5297.83
27낙동강하굿둑201905280.810.0066171.159181.841299.59797.53
28낙동강하굿둑201905290.820.0297278.385273.043300.05997.68
29낙동강하굿둑201905300.860.0296105.48684.083301.90898.28
30낙동강하굿둑201905310.890.7944168.493152.389303.29998.73