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Summary: Motivation: Communities across the global south use their rich indigenous and local knowledge (ILK) to predict weather events and climate hazards. ILK may assist efforts to address climate change challenges in Africa and make subsequent decisions regarding climate adaptation. Purpose: The article documents evidence of the ILK's potential in reducing vulnerability to climate change and/or improving the resilience of communities. The study also reflects on major barriers that hinder the improved mainstreaming of ILK into adaptation strategies. Methods and approach: The present study uses two main methods: a literature review and a presentation of case studies from a sample of African countries where ILK informs adaptation options, including indigenous land‐tenure practices and weather prediction. The selected case studies highlight the historical legacy of ILK and its effectiveness in reducing vulnerability and the impacts of climate change. Findings The results indicate that, despite being acknowledged as a valuable resource for climate adaptation, current national adaptation policies on the African continent still show serious gaps in effectively integrating ILK systems within the legal frameworks to reduce vulnerability. Policy implications: ILK should be better integrated with modern climate change adaptation strategies to anticipate more effective responses. Both rural communities and relevant government agencies should complement the use of ILK with climate change strategies, so as to maximize its contribution to the effective implementation of climate change policies. [ABSTRACT FROM AUTHOR]

This study develops an Adaboost-GIS model for flood susceptibility mapping and evaluates its relative performance by undertaking a comparative assessment of the machine learning model with Multi-Criteria Decision Making (MCDM) and soft computing models integrated with GIS. An Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Fuzzy-AHP, Fuzzy-ANP and AdaBoost machine learning models were developed and integrated with GIS to classify the susceptibility of the study area. Out of 70 sample validation locations, Adaboost’s performance was the best with a 95.72% similarity match with very high and high susceptibility locations followed by F-ANP, ANP, F-AHP and AHP with 95.65%, 92.75%, 81.42% and 77.14% similarity matches, respectively. It also had the highest AUC (0.864). Thus, the Adaboost machine learning, Fuzzy computing and conventional MCDM models can be adopted by stakeholders for accurately assessing flood susceptibility, thereby fostering safe and resilient cities. [ABSTRACT FROM AUTHOR]

In this study, we conducted a holistic evaluation of current and future trend in coastal sea level at the 21 stations along Malaysia's coastline. For sea level prediction, univariate and 3 scenarios of multivariate Long Short Term Memory (LSTM) neural networks were trained with absolute sea level data and ocean-atmospheric variables. The result from the four scenario predictive models revealed that multivariate LSTM neural network trained with combined ocean-atmospheric variables performed best for modelling sea level variation, giving a mean RMSE and R accuracy of 0.060 and 0.861, respectively. The national sea level rise estimated from the average of sea level trend at all stations is 3.72 mm/yr for relative sea level and 3.68 mm/yr for absolute sea level. The 2050 and 2100 projections indicate that sea level will continue to rise but at a very slow rate with no acceleration. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa, mass rural-urban migration negatively affectthe agriculture sector that accounts for about 23% of the GDP and employs over 60% of the population. Together with a rapidly changing climate, unplanned urbanization poses serious threats to Africa's agriculture sector with the risk of chronic food shortages in the future. To stem this tide, it is imperative to systematically assess the unplanned urbanization trend from a socio-economic perspective and distill the broader implication for sustainable urban farming within the context of climate change in the region. The potentials of digitalization as a tool for transformative adaptation to climate change and enabler of sustainable development in different domains, including agriculture, are beginning to emerge. However, most studies are based on data from Asia, Europe, North America, and Oceania. There is minimal documentation of current applications and prospects of digitalization for sustainable agricultural practices in Africa, particularly in an increasingly urbanized era. Thus, this study addresses this need by evaluating the potentials of digitalization to enable sustainable farming in the face of unprecedented climate change constraints in Africa and minimize the negative impacts of urbanization on agriculture. Through a desk research approach, the present study explores the challenges to digital farming in Africa despite its successful implementation in the global North. Drawing lessons from successful case-studies worldwide, we suggest possible pathways to overcome the challenges and implement localized digitalization approaches to strengthen preventive action against climate risks, enhance disaster preparedness, and aid effective planning and management of agriculture practices. Integrating agriculture into the city via digital urban farming is crucial for long-term food security and creating appealing clean-tech jobs for a large number of new immigrants, thereby supporting African cities' resilience and sustainable development. [ABSTRACT FROM AUTHOR]

The purpose of this study is to model the migration of hydrocarbon using Geographic Information System (GIS). Understanding hydrocarbon migration is important since it can mean the difference between success and failure in oil and gas exploration project. The hydrocarbon migration modeling using geophysical method is still not accurate due to the limitations of available data. In recent years, GIS has emerged as a powerful tool for subsurface mapping and analysis. Recent studies have been carried out about the abilities of GIS to model hydrocarbon migration. Recent advances in GIS support the establishment and monitoring of prediction hydrocarbon migration. The concept, model, and calculation are based on the current geological situation. The spatial data of hydrocarbon reservoirs is determined by its geometry of lithology and geophysical attributes. Top of Group E horizon of north-east Malay basin was selected as the study area due to the occurrence of hydrocarbon migration. Spatial data and attributes data such as seismic data, wells log data and lithology were acquired and processed. Digital Elevation Model (DEM) was constructed from the selected horizon as a result of seismic interpretation using the Petrel software. Furthermore, DEM was processed in ArcGIS as a base map to shown hydrocarbon migration in north-east Malay Basin. Finally, all the data layers were overlaid to produce a map of hydrocarbon migration. A good data was imported to verify the model is correct.

Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model's ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM. [ABSTRACT FROM AUTHOR]

Small Island Developing States (SIDS) are heavily dependent on the use of imported fossil fuels to address their energy needs. This has a negative impact on the environment, SIDS budgets, and energy security. Therefore, this study aimed to investigate the role of renewable energy (RE) as a tool for energy security in SIDS. In this regard, using VOSviewer, a widely known software tool, two bibliometric analyses were performed with a focus on the literature that explores two intertwined issues: (i) the links between RE and energy security; and (ii) the implications of RE and energy security in SIDS. The results from the study show that RE can help SIDS enhance their energy security and assure long-term energy sustainability. In addition, the results show that with the reduction in the cost of batteries in the future, they will eventually replace diesel generators. Moreover, the study showed that renewable energy may assist SIDS in their long-term efforts to achieve food security. The analysis discusses the major obstacles and the potential solutions for the integration of RES into the energy generation of SIDS. The paper concludes with useful recommendations to help island nations reduce their carbon footprint. [ABSTRACT FROM AUTHOR]

Global Land Cover (GLC) maps have been extensively used as background data for numerous applications such as land-use planning, climate change monitoring, and urban planning. While countries in Europe and North America have greatly developed land cover maps, several developing countries in Asia and Africa lack such vital data sources. GlobeLand30's 30 m resolution dataset has the potential to meet the mapping needs of these developing regions. However, differences in accuracy of the dataset between countries can be significant making it unsuitable to adopt regional accuracy directly for assessing individual country's accuracy level. Therefore, it is critical to develop reliable localized assessments. This research assesses the accuracy of GlobeLand30 for Malaysia using multiple evaluation approaches. Seven classes of land cover data were evaluated using 197 samples in the country. An overall accuracy of 63.45% and 65.70% was obtained from the Error Matrix using sample counts and the new unbiased area estimator respectively. We conclude that the overall accuracy of GlobeLand30 in tropical developing countries like Malaysia is generally lower than the accuracy in developed countries and arid developing countries thereby requiring significant improvement for optimal use. However, despite the errors due largely to misclassification, the GlobeLand 30 data has the potential to complement other LULC data to estimate and monitor vital land-use changes in Malaysia and the ASEAN region in general for sustainable development. [ABSTRACT FROM AUTHOR]

Background: "The impacts of the Coronavirus Disease 2019 (COVID-19) pandemic and the shutdown it triggered at universities across the world, led to a great degree of social isolation among university staff and students. The aim of this study was to identify the perceived consequences of this on staff and their work and on students and their studies at universities.Method: The study used a variety of methods, which involved an on-line survey on the influences of social isolation using a non-probability sampling. More specifically, two techniques were used, namely a convenience sampling (i.e. involving members of the academic community, which are easy to reach by the study team), supported by a snow ball sampling (recruiting respondents among acquaintances of the participants). A total of 711 questionnaires from 41 countries were received. Descriptive statistics were deployed to analyse trends and to identify socio-demographic differences. Inferential statistics were used to assess significant differences among the geographical regions, work areas and other socio-demographic factors related to impacts of social isolation of university staff and students.Results: The study reveals that 90% of the respondents have been affected by the shutdown and unable to perform normal work or studies at their institution for between 1 week to 2 months. While 70% of the respondents perceive negative impacts of COVID 19 on their work or studies, more than 60% of them value the additional time that they have had indoors with families and others. .Conclusions: While the majority of the respondents agree that they suffered from the lack of social interaction and communication during the social distancing/isolation, there were significant differences in the reactions to the lockdowns between academic staff and students. There are also differences in the degree of influence of some of the problems, when compared across geographical regions. In addition to policy actions that may be deployed, further research on innovative methods of teaching and communication with students is needed in order to allow staff and students to better cope with social isolation in cases of new or recurring pandemics. [ABSTRACT FROM AUTHOR]

This article explores using satellite images to monitor spatiotemporal variations in temperature related to urban form. Land surface temperatures (LST) were estimated from Landsat images (1986–2016) and the land cover and urban form LST were extracted by using samples representing different urban forms/cover types. A transect of 20 km was taken across the city to derive the LST across the different land cover types. Urban heat island index and statistical analysis were carried out to understand the influence of urban form and cover on changes in surface temperature. The results are compared with temperature regimes of an industrial city (Yanbu) to depict differences in the two cities. The analysis of variance (ANOVA) shows variations, at 0.01 level of significance, in the LST values of the city centre, high-rise, low-density, vegetation, desert and industrial land-use types. The outcome of the study is valuable for decision-makers in achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

Selection of pipeline routes is a geospatial multi-criteria evaluation problem since it involves the assessment of technical, environmental and safety-related factors that influence pipeline system operations. This study determines the optimal pipeline route for transmission of oil and gas products using the Dangote Refinery Project in Nigeria as a case study. Pertinent route selection factors were identified and structured into the expert feedback model of the Analytical Network Process (ANP) for accurate prioritization. LANDSAT 8 imageries of the study area were processed and classified into various land use and land cover types, which were further modelled in ArcMap 10.2 GIS software for routing analysis. Findings reveal that the most highly prioritized factors for proper route selection were the environmental and geological factors, while the least prioritized was the economic factor. In the end, alternative Route 2 was identified as the optimal route among the four evaluated alternative routes because of its minimum route length, minimum passage length through sensitive settlement areas, and less number of river, stream and existing platforms' crossings. [ABSTRACT FROM AUTHOR]

Many cities across the world are facing many problems climate change poses to their populations, communities and infrastructure. These vary from increased exposures to floods, to discomfort due to urban heat, depending on their geographical locations and settings. However, even though some cities have a greater ability to cope with climate change challenges, many struggle to do so, particularly in cities in developing countries. In addition, there is a shortage of international studies which examine the links between climate change adaptation and cities, and which at the same time draw some successful examples of good practice, which may assist future efforts. This paper is an attempt to address this information need. The aim of this paper is to analyse the extent to which cities in a sample of developing countries are attempting to pursue climate change adaptation and the problems which hinder this process. Its goal is to showcase examples of initiatives and good practice in transformative adaptation, which may be replicable elsewhere. To this purpose, the paper describes some trends related to climate change in a set of cities in developing countries across different continents, including one of the smallest capital cities (Georgetown, Guyana) and Shanghai, one the world's most populous cities. In particular, it analyses their degree of vulnerability, how they manage to cope with climate change impacts, and the policies being implemented to aid adaptation. It also suggests the use of transformative approaches which may be adopted, in order to assist them in their efforts towards investments in low-carbon and climate-resilient infrastructure, thereby maximizing investments in urban areas and trying to address their related poverty issues. This paper addresses a gap in the international literature on the problems many cities in developing countries face, in trying to adapt to a changing climate. Unlabelled Image • Studies on adaptation challenges in developing cities are scarce. • Ageing and failing infrastructure limit cities' resilience. • There is a positive relationship between cities' income level and adaptation capacity. • Large population is not a barrier per se to a city's adaptation capacity. • Most cities lack local-level climate policies and programmes. [ABSTRACT FROM AUTHOR]

Proper pipeline route selection is an integral component of a typical oil exploration and transportation project. Improperly selected routes could have severe consequences including pipe failures, oil spillage, and environmental disasters. Consequently, technologies like geographic information systems (GIS) are increasingly being used to facilitate the oil pipeline route selection procedure—especially for onshore routing projects. Surprisingly, not much has been documented on the application of GIS to offshore pipeline routing. With recent discoveries on the merits of offshore oil exploration, it is necessary to extend the analytical capabilities of GIS to the unique offshore domain. However, concerns have been raised regarding the limitations of GIS in accurately prioritizing diverse selection criteria in typical multi-criteria decision-making (MCDM) problems like route selection. Consequently, this paper addresses the offshore/subsea pipeline routing constraint using a hybrid decision support system (DSS), which integrates a GIS and fuzzy logic-based approximate reasoning (AR) models for optimal performance. The resultant spatial decision support system (SDSS) was successfully applied to a case study in Malaysia. The AR algorithm calculated the significance level of the multiple criteria using various fuzzy linguistic variables and membership functions. The aggregated priority ranking from different pipeline routing experts showed that the overall influence of the environmental criteria (61.4%) significantly exceeded that of other equally important criteria in the study area. These rankings were inputted into the SDSS to simulate various probable routes. Final results accurately highlighted an optimal route, which places a premium on the protection of environmental features in the subsea study area—in alignment with the preferences of majority of the experts. [ABSTRACT FROM AUTHOR]

Selecting suitable pipeline routes is essential to the success of any oil exploration and transportation project, and proper prioritization of the diverse routing criteria is an integral component of the route selection endeavor. However, recent researches reveal that accurate prioritization of routing criteria, particularly in an offshore/subsea environment, has not been sufficiently addressed. With global attention rapidly expanding from terrestrial exploration to offshore and subsea oil pipeline operations, this study addresses the offshore pipeline routing ranking concerns. A hybrid multi-criteria decision support system was developed by integrating fuzzy logic-based approximate reasoning (AR) and Analytic Hierarchy Process (AHP) mathematical models. Twelve diverse criteria and sub-criteria in an oil-rich basin offshore east Malaysia were examined to determine their impact in the selection of an optimal pipeline route in the area. These were subsequently classified into three broad categories: economic, engineering, and environmental routing criteria. The integrated system (AR-AHP) was used to determine the relative importance of the criteria using different fuzzy membership functions and linguistic variables. Final results showed that the environmental criteria in the study area have the highest aggregated priority and influence (61.4 %) followed by the engineering criteria (27.6 %) and the economic criteria (11 %) datasets, respectively. This study thus establishes the fact that preservation of environmental factors is superior to the other strategic criteria in the marine study area. [ABSTRACT FROM AUTHOR]

Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (GEE)-enabled Python toolkit, this study conducted shoreline dynamic analysis using high-frequency data sampling to analyze the impact of sea-level rise on the Malaysian coastline between 1993 and 2019. Instantaneous shorelines were extracted from a test site on Teluk Nipah Island and 21 tide gauge sites from the combined Landsat 5–8 and Sentinel 2 images using an automated shoreline-detection method, which was based on supervised image classification and sub-pixel border segmentation. The results indicated that rising sea level is contributing to shoreline erosion in the study area, but is not the only driver of shoreline displacement. The impacts of high population density, anthropogenic activities, and longshore sediment transportation on shoreline displacement were observed in some of the beaches. The conclusions of this study highlight that the synergistic use of multi-sensor remote-sensing data improves temporal resolution of shoreline detection, removes short-term variability, and reduces uncertainties in satellite-derived shoreline analysis compared to the low-frequency sampling approach. [ABSTRACT FROM AUTHOR]

The development of electro-mobility is one of the centerpieces of European country attempts to reduce carbon emissions and increase the quality of life in cities. The goals of reducing emissions from the transport sector and phasing out fossil-fueled vehicles in (urban) transport by 2050 present unrivaled opportunities to foster electro-mobility. This paper provides a comprehensive review of the literature and provides a detailed analysis of the current development of electro-mobility in Europe, assessing social, economic, and environmental aspects under a circular economy (CE) context. It also examines the existing challenges and suggests ways of addressing them towards improving the environmental performance of electro-mobility and the urban quality of life. The paper argues that a narrow technology-only agenda in electro-mobility will be less successful without the imperative of the CE, including not just materials and resources but also energy, to unlock the medium-term co-benefits of de-carbonization of both the transport as well as the building and energy sectors. The paper critically reviews some of the anticipated future developments that may guide the growth of this rapidly growing field into a CE. [ABSTRACT FROM AUTHOR]

Significant developments have been made in the observation systems and techniques of estimating sea level towards meeting the standard accuracy requirement of Global Climate Observation Systems (GCOS). This study undertakes a systematic review of the current advances in estimating sea level change in the context of the 4th industrial revolution. Trends in the use of main observation systems such as tide gauges, satellite altimetry, and ancillary systems such as GNSS and Autonomous Surface Vehicles were explored. Crucially, we examined the contribution of dedicated waveform retracking strategies, advanced corrections and radar technology such as Ka-band altimetry of SARAL/Altika and SAR mode innovations to the progress in coastal altimetry. Further, we show the role of emerging spatial data science concepts and processing workflows in sea level study. Findings suggest that in-situ sea level observation through tide gauges remains the best approach for long-term coastal sea level study despite its limitations while satellite altimetry is suitable for contemporary global and regional scales. Detailed understating of global, regional and local mean sea level change will require an augmentation of tide gauge, satellite altimetry and other ancillary remote sensing and in situ systems. Densification of tide gauges and co-located GNSS networks at sparsely covered regions and improvement in precision of satellite altimetry data for coastal use are also essential for a fully integrated sea level observation system. From the analysis of over 30 trend models that span exploratory, parametric, non-parametric, stochastic and advanced classes in the literature, we conclude that the best model is the one with good statistical foundation and similar assumption with the sea level pattern. [Display omitted] • In-situ sea level observation through tide gauges remains the best approach for long-term coastal sea level study. • Satellite altimetry is suitable for contemporary sea level observations at global and regional scales. • Integration of multiple systems is essential for detailed understating of global, regional and local mean sea level change. • Densification of tide gauges and co-located GNSS networks and improvement in precision of satellite altimetry is required. • Spatial data science techniques such as data engineering and trend estimation are pertinent to sea level studies. [ABSTRACT FROM AUTHOR]

This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m3 (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill. Image 1 • Oil spill coastal sensitivity maps were developed considering climatic variations. • GNOME model was used for the prediction of the oil spill trajectory. • Geographical Information System Kernel density spatial analysis tool is significant for the development of vulnerability map. • The novel coastal environmental vulnerability maps are particularly useful for decision making in a changing climate. [ABSTRACT FROM AUTHOR]

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models' strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives. [ABSTRACT FROM AUTHOR]