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Deep Learning and Adaptive Clusterin...

Sharafkhani, Fahimeh,

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Deep Learning and Adaptive Clustering Approaches for Flood Prediction and Efficient Sensor Placement in Missouri /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning and Adaptive Clustering Approaches for Flood Prediction and Efficient Sensor Placement in Missouri // Fahimeh Sharafkhani.
Author:	Sharafkhani, Fahimeh,
Description:	1 electronic resource (164 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31932354
ISBN:	9798286424436

Deep Learning and Adaptive Clustering Approaches for Flood Prediction and Efficient Sensor Placement in Missouri /
Sharafkhani, Fahimeh,

Deep Learning and Adaptive Clustering Approaches for Flood Prediction and Efficient Sensor Placement in Missouri /Fahimeh Sharafkhani. - 1 electronic resource (164 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Floods represent formidable natural calamities, posing a significant threat to communities and infrastructure due to their unpredictable and often devastating consequences. The occurrence of floods is influenced by a convergence of meteorological, hydrological, and geographical factors, resulting in changes to the patterns of rising water levels. Machine learning models have emerged as favored tools in recent times for modeling water levels and enhancing the precision of flood predictions. This research employs both supervised and unsupervised machine learning models, with the main objective of improving the accuracy of flood predictions and sensor placement. Four distinct deep learning models are used to forecast water levels for a designated set of gage stations in Missouri. The comparative analysis assesses the effectiveness of four deep learning models, considering various prediction intervals and parameter tuning to bolster result robustness. Subsequently, the study incorporates an unsupervised learning algorithm to identify optimal locations for placement of new water level sensors. Unlike conventional sensor placement algorithms, this strategy focuses on dynamic water level patterns to extract important features that are indicative of informative gage locations. The methodology is evaluated using a deep learning model to ascertain its efficacy. The research concludes by pinpointing locations for water level sensors in Missouri. The result of these studies enables a thorough examination of accuracy-cost trade-offs before deciding to new sensors and providing efficient ways to analyze the Missouri river networks with less computational intensity.

English

ISBN: 9798286424436Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Clustering

Deep Learning and Adaptive Clustering Approaches for Flood Prediction and Efficient Sensor Placement in Missouri /
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300 $a 1 electronic resource (164 pages)
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500 $a Advisors: Corns, Steven Committee members: Dagli, Cihan; Nicolosi, Gabriel; Holmes, Robert; Wunsch, Donald.
502 $b Ph.D. $c Missouri University of Science and Technology $d 2025.
520 $a Floods represent formidable natural calamities, posing a significant threat to communities and infrastructure due to their unpredictable and often devastating consequences. The occurrence of floods is influenced by a convergence of meteorological, hydrological, and geographical factors, resulting in changes to the patterns of rising water levels. Machine learning models have emerged as favored tools in recent times for modeling water levels and enhancing the precision of flood predictions. This research employs both supervised and unsupervised machine learning models, with the main objective of improving the accuracy of flood predictions and sensor placement. Four distinct deep learning models are used to forecast water levels for a designated set of gage stations in Missouri. The comparative analysis assesses the effectiveness of four deep learning models, considering various prediction intervals and parameter tuning to bolster result robustness. Subsequently, the study incorporates an unsupervised learning algorithm to identify optimal locations for placement of new water level sensors. Unlike conventional sensor placement algorithms, this strategy focuses on dynamic water level patterns to extract important features that are indicative of informative gage locations. The methodology is evaluated using a deep learning model to ascertain its efficacy. The research concludes by pinpointing locations for water level sensors in Missouri. The result of these studies enables a thorough examination of accuracy-cost trade-offs before deciding to new sensors and providing efficient ways to analyze the Missouri river networks with less computational intensity.
546 $a English
590 $a School code: 0587
650 4 $a Computer science. $3 523869
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Systems science. $3 3168411
650 4 $a Engineering. $3 586835
653 $a Clustering
653 $a Deep learning
653 $a Flood predictions
653 $a Machine learning
653 $a Sensor placement
653 $a Water level prediction
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690 $a 0388
690 $a 0790
690 $a 0800
690 $a 0537
710 2 $a Missouri University of Science and Technology. $b Systems Engineering. $e degree granting institution. $3 3823582
720 1 $a Corns, Steven $e degree supervisor.
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791 $a Ph.D.
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