Space, movement and heritage planning of the historic cities in Islamic societies: Learning from the Old City of Jeddah, Saudi Arabia

This is the author's accepted manuscript. The original published version is available at http://www.palgrave-journals.com.Traditional historic cities in Islamic societies of Asia and Africa are fast disappearing and/or losing relevance. There is a clear need for heritage planners to plan for what is left and to integrate these historic cities with the bigger cities that surround them. This study on movement dynamics of people and cars in relation to spatial configuration, described using space syntax, was undertaken in the Old City of Jeddah to understand and learn how accessibility and the density of people and activities can be used for retaining the viability and vitality of this and other historic cities. The study reports several findings that are potentially relevant to heritage planning. However, further studies are needed to understand the importance of the findings in relation to different social and symbolic realities of Islamic societies before specific spatial strategies can be identified for heritage planning in this and other historic cities

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria

Impacts of Vegetation and Topography on Land Surface Temperature Variability over the Semi-Arid Mountain Cities of Saudi Arabia

Land surface temperature (LST) can fully reflect the water–heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST–NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas

Economic Diversity by Sustaining Historical Buildings: King Abdul Aziz Palace, as a Case Study

Within the long history, Saudi Arabia has a diverse range of heritage buildings and sites which still exist until today. Palace of the Emirate in Qebah town is one of the most significant historical buildings which were built by order of King Abdul Aziz in 1351 AH in Al-Qassim Region. Saudi Vision 2030 is a plan to reduce Saudi Arabia's dependence on oil, diversify its economy, and develop public service sectors such as infrastructure, recreation, and tourism. This paper aims to discuss the sustainability of historical buildings in Al Qassim Region with the help of the implementation of conservation policies to enhance the diversity of the economy in the country.Keywords: Sustainable; Historic Buildings; Heritage Conservation; Economic DiversityISSN: 2398-4287 © 2019. It is published Published for AMER ABRA CE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and CE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i10.155

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 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

A comparison of four land surface temperature retrieval method using TERRA-ASTER satellite images in the semi-arid region of Saudi Arabia

Land surface temperature is a significant source of energy budget and climate information, contributing to various environmental and biophysical processes. This research includes comparing the LSTs retrieved from the ASTER sensor using the Reference Channel method, the Emissivity Normalization method (NOR), TES method and Retrieving LSE by taking the proportion of vegetation cover coupled with NDVI and integrates it into the TES algorithm. The results of derived LST from the four algorithms compare with MODIS data of 7 control points having thermally homogenous sites. The analysis showed that the four algorithms are suitable for LST retrieval, whereby the proposed emissivity-derived NDVI algorithms exhibited the highest degree of accuracy (RMSE 0.145), and the NOR had the least accuracy (RMSE 0.403). The analysis shows that emissivity derived NDVI TES method more reliant on the upwelling, downwelling and transmittance and will achieve the best results compared to the other three algorithms

Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping

Landslides and other catastrophic environmental disasters pose a significant danger to environmental, infrastructure, and people's lives. This research aimed to construct four optimized ensemble machine learning algorithms for landslide susceptibility (LS) mapping, namely particle swarm optimization (PSO) based artificial neural network (ANN), random forest, M5P, and support vector machine. The logistic regression (LR) model was then applied to the four-ensemble machine learning model and generated a hybrid optimized machine learning model. The receiver operating characteristics (ROC) curve was then used to validate LS map. The best model of four LS models depending on ROC's area under curve (AUC) is PSO-ANN (AUC-0.958) model. Also, LR model-based hybrid ensemble machine learning model achieved better accuracy (AUC: 0.962) than PSO-ANN model. Various resources, viz. grassland, built-up, and scarce foliage, are declared as landslide risk zones. Finally, elevation, soil-texture, slope, rainfall, and road distance are considered the most sensitive parameters for landslide occurrences