Topological data analysis via unsupervised machine learning for recognizing atmospheric river patterns on flood detection

Ohanuba, Felix O.; Ismail, Mohd Tahir; Ali, Majid Khan Majahar

doi:10.1016/j.sciaf.2021.e00968

Cited by 5 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The theorem is obtained from Eq. ( 13) if (13) The categorical product of N points (balls) in the dataset is expressed as × This theorem is equivalent to , and expressed in Eq. ( 14) as (14) Further derivation produces its barcode , and obtained as Eq.…”

Section: Computation Of Persistence Homology (Ph) In the Tda-ml Approachmentioning

confidence: 99%

“…The significance of TDA is that the classifier performs well in all kinds of data sets from other areas of study. The algorithm and software are beneficial to a high number of supervised machine learning (ML) techniques; they are flexible and threshold-free [13]. The PH provided a concise representation of standard features throughout their life span.…”

Section: Graphical Abstract 1 Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of topological data analysis to flood disaster management in Nigeria

Ohanuba

Ismail

Ali

2022

Environmental Engineering Research

View full text Add to dashboard Cite

The importance of this research to the literature lies in the ability to develop a hybrid method of Topological Data Analysis and Unsupervised Machine Learning (TDA-uML) for flood detection. The working method in TDA-uML entails collection and loading of datasets, feature representation, data preprocessing (i.e., training, testing, computation, and validation), and data classification. Three properties make TDA distinct from traditional methods: coordinate-invariance, deformative-invariance, and compressed-representative. Formerly utilized hydrologic, hydraulic, and statistical models for flood control were frequently erroneous in their forecasts, lacked the use of hybrid models, and were not validated. The main research objective is to develop a hybrid method for predicting floods. The motivation is to fill research gaps by using TDA-uML methods for persistent homology (PH) and synthetic k-means clustering. The results will be used to compare and categorize the features that are produced. Seven states were selected based on Nigeria's flood history and affected population. The 7 states are located within the 8 hydrological areas of Nigeria. The efficiency of the resultant validity was 91%. The findings contributed to the development of a model for flood prediction and management; topological features were extracted from the data to predict and categorize the risk.

show abstract

Section: Computation Of Persistence Homology (Ph) In the Tda-ml Approachmentioning

confidence: 99%

Section: Graphical Abstract 1 Introductionmentioning

confidence: 99%

Application of topological data analysis to flood disaster management in Nigeria

Ohanuba

Ismail

Ali

2022

Environmental Engineering Research

View full text Add to dashboard Cite

show abstract

Topological clustering in investigating spatial patterns of particulate matter between air quality monitoring stations in malaysia

Zulkepli,

Madukpe,

Noorani

et al. 2024

Air Qual Atmos Health

View full text Add to dashboard Cite

Comparative assessment of the developed algorithm with the soft computing algorithm for the laser machined depth

Anjum

Shaikh

Tiwari³

2023

Infrared Physics & Technology

View full text Add to dashboard Cite

Topological data analysis via unsupervised machine learning for recognizing atmospheric river patterns on flood detection

Cited by 5 publications

References 27 publications

Application of topological data analysis to flood disaster management in Nigeria

Application of topological data analysis to flood disaster management in Nigeria

Topological clustering in investigating spatial patterns of particulate matter between air quality monitoring stations in malaysia

Comparative assessment of the developed algorithm with the soft computing algorithm for the laser machined depth

Contact Info

Product

Resources

About