東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

An Advanced Approach to Assessing Fl...

Gong, Siming.

FindBook

Google Book

Amazon

博客來

An Advanced Approach to Assessing Flood Risk in Urban Environments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	An Advanced Approach to Assessing Flood Risk in Urban Environments./
作者:	Gong, Siming.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	280 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Geographic information systems. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31355341
ISBN:	9798382765525

An Advanced Approach to Assessing Flood Risk in Urban Environments.
Gong, Siming.

An Advanced Approach to Assessing Flood Risk in Urban Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 280 p.

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--University of Technology Sydney (Australia), 2024.

Urban areas face heightened flood vulnerability due to expanding impervious surfaces, which require consistent research to enhance flood forecasting and management systems. Catchment modelling is increasingly popular in flood risk management. However, modelling urban catchments is challenging due to heterogeneous land use and land cover (LULC) patterns, especially struggle with the extensive interpretation and parameterisation of spatial datasets using conventional data processing methods, leading to inconsistent simulations. Deep Neural Networks (DNNs) have shown promising results in processing catchment data, learning complex spectral and textural relationships for LULC category identification and segmentation. Therefore, implementing DNNs to interpret intricate spatial data and estimate parameters holds significant research value.The project has primarily focused on devising a methodology to effectively apply DNNs in analysing LULC data to estimate parameters in urban catchment models. The assessments of approaches' adaptability, stability and consistency were advanced together as the sub-projects. An end-to-end data assimilation and processing strategy was proposed by integrating DNNs and the clustering algorithm to generate a pixel-based LULC map on high-resolution satellite imagery. A reproducible framework was developed to estimate parameters for the hydrological component of the catchment model at the subcatchment scale by grouping the generated LULC features. The cumulative likelihood and Kolmogorov-Smirnov (KS) methods were adopted to describe the distribution of the results and assess goodness-of-fitting. Then, the expanded LULC samples were quantified using information entropy (IE) to evaluate prediction stability, where administrative data defined the sampling scope and sample impervious fraction was translated into polynomial functions to analyse occurrence likelihood distribution of samples' impervious fraction.The integrated DNNs approach achieved excellent classification performance, where the MeanShift+UNet has the highest accuracy on the test set. The suitability assessment illustrates that all three methods are more suitable for semi-distributed modelling systems. Parameter estimation results suggest that LULC has a limited effect on subcatchment flow length, and the proposed approach can estimate parameters for initialising catchment modelling systems. The probability-fitting study of impervious samples reflects the distribution differential of impervious fractions under the attributes of administrative data. The IE stability test result shows a robust model that clarifies the different confident ranges of imperviousness estimation based on land zoning information. A model of the Alexandra Canal Catchment (1379.8 ha) in Sydney, Australia, was built and configured by generated parameters, continuous rainfall and tidal data to validate the approaches' performance in the ungauged catchment. The modelling result shows great consistency compared to historical models.

ISBN: 9798382765525Subjects--Topical Terms:

542758
Geographic information systems.

An Advanced Approach to Assessing Flood Risk in Urban Environments.
LDR:04114nmm a2200361 4500 001 2400174
005 20240924101923.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382765525
035 $a (MiAaPQ)AAI31355341
035 $a (MiAaPQ)TechSydney178219
035 $a AAI31355341
040 $a MiAaPQ $c MiAaPQ
100 1 $a Gong, Siming. $3 3770142
245 1 3 $a An Advanced Approach to Assessing Flood Risk in Urban Environments.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 280 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisor: Ball, James.
502 $a Thesis (Ph.D.)--University of Technology Sydney (Australia), 2024.
520 $a Urban areas face heightened flood vulnerability due to expanding impervious surfaces, which require consistent research to enhance flood forecasting and management systems. Catchment modelling is increasingly popular in flood risk management. However, modelling urban catchments is challenging due to heterogeneous land use and land cover (LULC) patterns, especially struggle with the extensive interpretation and parameterisation of spatial datasets using conventional data processing methods, leading to inconsistent simulations. Deep Neural Networks (DNNs) have shown promising results in processing catchment data, learning complex spectral and textural relationships for LULC category identification and segmentation. Therefore, implementing DNNs to interpret intricate spatial data and estimate parameters holds significant research value.The project has primarily focused on devising a methodology to effectively apply DNNs in analysing LULC data to estimate parameters in urban catchment models. The assessments of approaches' adaptability, stability and consistency were advanced together as the sub-projects. An end-to-end data assimilation and processing strategy was proposed by integrating DNNs and the clustering algorithm to generate a pixel-based LULC map on high-resolution satellite imagery. A reproducible framework was developed to estimate parameters for the hydrological component of the catchment model at the subcatchment scale by grouping the generated LULC features. The cumulative likelihood and Kolmogorov-Smirnov (KS) methods were adopted to describe the distribution of the results and assess goodness-of-fitting. Then, the expanded LULC samples were quantified using information entropy (IE) to evaluate prediction stability, where administrative data defined the sampling scope and sample impervious fraction was translated into polynomial functions to analyse occurrence likelihood distribution of samples' impervious fraction.The integrated DNNs approach achieved excellent classification performance, where the MeanShift+UNet has the highest accuracy on the test set. The suitability assessment illustrates that all three methods are more suitable for semi-distributed modelling systems. Parameter estimation results suggest that LULC has a limited effect on subcatchment flow length, and the proposed approach can estimate parameters for initialising catchment modelling systems. The probability-fitting study of impervious samples reflects the distribution differential of impervious fractions under the attributes of administrative data. The IE stability test result shows a robust model that clarifies the different confident ranges of imperviousness estimation based on land zoning information. A model of the Alexandra Canal Catchment (1379.8 ha) in Sydney, Australia, was built and configured by generated parameters, continuous rainfall and tidal data to validate the approaches' performance in the ungauged catchment. The modelling result shows great consistency compared to historical models.
590 $a School code: 1295.
650 4 $a Geographic information systems. $3 542758
650 4 $a Stormwater management. $3 3685325
650 4 $a Floods. $3 549458
650 4 $a Neural networks. $3 677449
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Environmental engineering. $3 548583
650 4 $a Water resources management. $3 794747
650 4 $a Geographic information science. $3 3432445
690 $a 0800
690 $a 0370
690 $a 0595
690 $a 0775
690 $a 0388
710 2 $a University of Technology Sydney (Australia). $3 3701578
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 1295
791 $a Ph.D.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31355341