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Detection and predictive modeling of chaos in finite hydrological time series
S. Khan, A. R. Ganguly,
S. Saigal
Research output
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Contribution to journal
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Article
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peer-review
43
Scopus citations
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Dive into the research topics of 'Detection and predictive modeling of chaos in finite hydrological time series'. Together they form a unique fingerprint.
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Mathematics
Predictive Modeling
100%
Chaos
56%
Time series
54%
Seasonality
12%
Rainfall
12%
Rivers
11%
Observation
5%
Modeling
5%
Decompose
5%
Demonstrate
5%
Earth & Environmental Sciences
chaotic dynamics
67%
time series
42%
detection
35%
modeling
25%
seasonality
9%
streamflow
9%
decomposition
7%
runoff
7%
rainfall
6%
river
4%
test
3%
Chemical Compounds
Chaos (Dynamical)
90%
Time
21%
Decomposition
9%
Strength
9%
Physics & Astronomy
chaos
55%
Colorado River (North America)
23%
drainage
12%
decomposition
7%
thresholds
6%