Skip to main navigation
Skip to search
Skip to main content
New Jersey Institute of Technology Home
Help & FAQ
Home
Profiles
Research units
Equipment
Projects
Research output
Search by expertise, name or affiliation
Detection and predictive modeling of chaos in finite hydrological time series
S. Khan, A. R. Ganguly,
S. Saigal
Research output
:
Contribution to journal
›
Article
›
peer-review
43
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Detection and predictive modeling of chaos in finite hydrological time series'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Chaos
56%
Decompose
5%
Demonstrate
5%
Modeling
5%
Observation
5%
Predictive Modeling
100%
Rainfall
12%
Rivers
11%
Seasonality
12%
Time series
54%
Earth & Environmental Sciences
chaotic dynamics
67%
decomposition
7%
detection
35%
modeling
25%
rainfall
6%
river
4%
runoff
7%
seasonality
9%
streamflow
9%
test
3%
time series
42%
Chemical Compounds
Chaos (Dynamical)
90%
Decomposition
9%
Strength
9%
Time
21%
Physics & Astronomy
chaos
55%
Colorado River (North America)
23%
decomposition
7%
drainage
12%
thresholds
6%