Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making
This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban e...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024017626 |
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| author | Saeed Alqadhi Ahmed Ali Bindajam Javed Mallick Swapan Talukdar Atiqur Rahman |
| author_facet | Saeed Alqadhi Ahmed Ali Bindajam Javed Mallick Swapan Talukdar Atiqur Rahman |
| author_sort | Saeed Alqadhi |
| collection | DOAJ |
| description | This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in ‘built-up’ areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157–250 km2, while the natural zone covers 91–410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques. |
| first_indexed | 2024-03-08T00:01:10Z |
| format | Article |
| id | doaj.art-c45c7246d626486db150323d53b9ed90 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:22:58Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-c45c7246d626486db150323d53b9ed902024-03-09T09:26:04ZengElsevierHeliyon2405-84402024-02-01104e25731Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-makingSaeed Alqadhi0Ahmed Ali Bindajam1Javed Mallick2Swapan Talukdar3Atiqur Rahman4Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi ArabiaDepartment of Architecture and Planning, College of Engineering, King Khalid University, Abha, Kingdom of Saudi ArabiaDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia; Corresponding author.Department of Civil Engineering, College of Engineering, King Khalid University, P.O. Box: 394 Abha 61411, P, Saudi Arabia.Urban Environmental & Remote Sensing Division, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India; Corresponding author.Urban Environmental & Remote Sensing Division, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, IndiaThis study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in ‘built-up’ areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157–250 km2, while the natural zone covers 91–410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques.http://www.sciencedirect.com/science/article/pii/S2405844024017626Urban ecological conditionArtificial neural networkFuzzy logicFAHPSensitivity analysisDeep learning |
| spellingShingle | Saeed Alqadhi Ahmed Ali Bindajam Javed Mallick Swapan Talukdar Atiqur Rahman Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making Heliyon Urban ecological condition Artificial neural network Fuzzy logic FAHP Sensitivity analysis Deep learning |
| title | Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making |
| title_full | Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making |
| title_fullStr | Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making |
| title_full_unstemmed | Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making |
| title_short | Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making |
| title_sort | applying deep learning to manage urban ecosystems in arid abha saudi arabia remote sensing based modelling for ecological condition assessment and decision making |
| topic | Urban ecological condition Artificial neural network Fuzzy logic FAHP Sensitivity analysis Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024017626 |
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