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Decarbonisation is instrumental in attaining sustainable mobility. To actualize the Ghana Nationally Determined Contribution (NDC) set emission reduction target of 15% relative to Business-As-Usual (BAU) scenario by 2030, sustainable transport actions should be encouraged. Thus, promoting the use of Liquified Petroleum Gas (LPG) and Compressed Natural Gas (CNG) in automobiles is very crucial to ensure efficient and green mobility. Nonetheless, existing policies in Ghana overlook autogas (LPG/CNG) as prospective decarbonizing solution in the transport sector. The study employed survey analysis and analytical modeling approach to elicit the benign effects of autogas in the transport sector using Accra as a case study. The ecological, economic and social dimensions of autogas were expatiated to extrapolate effective measures to facilitate their smooth implementation. Survey was carried out in the central business district of Accra to attain the percentage of autogas and gasoline used by taxis operators for the first time per author’s knowledge. A purposive sample of 500 taxi drivers was selected and data analysis was conducted using R statistical package. From the survey, 14% of taxis were powered with LPG whilst 86% were gasoline, however, the LPG-powered taxis were retrofitted gasoline engine vehicles. The analytical model was based on physics principles involving three resistance forces- aerodynamic, rolling resistance and inertia. CO2 emission savings of 29% and 18.4% were elicited from the use of CNG and LPG relative to gasoline fuel at the end of the simulation using the ambient conditions in Accra. Thus, use of autogas will limit global warming impact and aid the country to fulfill its pledged emission target by 2030. The government is entreated to regulate autogas use in the transport sector and increase its patronage by promoting flexible policies like meager custom duty on imported CNG/LPG vehicles as well as tax credits.

Flooding, exacerbated by the challenges of climate change, poses a growing threat to communities in the Upper West Region (UWR) of Ghana. This persistent issue, particularly during the rainy seasons has subjected the region to several losses of properties and lives over the years. This has spurred the need for a comprehensive delineation of flood risk terrains (FRTs) and analysis of the rainfall patterns in the region. This study, therefore, started by analysing a digital elevation model (SRTM—DEM) using Jenks Natural Breaks Classification (JNBC) algorithm to delineate potential FRTs map within the region. Further, analysis was performed using Analytical Hierarchy Process Multi-Criteria Decision (AHP-MCD) with the incorporation of six spatial factors (Lineament Density, Elevation, Topographic Wetness Index, Drainage Density, Slope, and Aspect) to generate a comprehensive FRTs map. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) from 1992 to 2022 were also visualized in a Jupyter Notebook to assess rainfall patterns in the UWR. Historical flood events data were also analysed to understand the trends of flood events impacts. From the findings, both the JNBC and AHP-MCD algorithms categorized the UWR’s total area into five classes, namely; very high, high, moderate, low, and very low FRTs. The JNBC map had area coverages of 4% (856.278 km2), 7% (1466.685 km2), 12% (2418.642 km2), 35% (7014.96 km2), and 42% (8351.496 km2) from very high risk to very low FRTs, respectively. Notably, the very high risk terrains and high risk terrains were predominantly located along the southeastern and eastern regions, particularly along the Kulpawn River and Sisili River in the UWR. The five classes AHP-MCD map also recorded areas as 0.004% (0.707 km2), 21% (3830.02 km2), 69% (12807.31 km2), 10% (1827.011 km2), and 0.062% (11.535 km2) very high risk to very low FRTs, respectively. These findings further revealed a prevalence of high FRTs along stream and river networks. Interestingly, the validation of the AHP-MCD map over the ground truthing points indicated that the UWR is dominated by moderate FRTs (71.76%), underscoring the region's vulnerability to flooding. The visualization of the spatial rainfall distribution from 1992 to 2022, also highlighted the significance of heavy rainfall years, particularly in 2018, 2019, and 2021, and the month of August as consistent predictors of flood occurrences. A correlation matrix reinforces the strong connection between rainfall and flood-related impacts, such as affected populations, economic costs, and agricultural losses from 2016 to 2021. In light of these findings, UWR residents must prioritize flood-resilient crop cultivation and adhere to flood disaster safety protocols, especially during the critical month of August. These insights hold valuable implications for municipal, district, and community planning policies, offering a foundation for proactive sustainable flood risk mitigation and community resilience efforts in the region.

The increasing popularity of drones has led to their adoption by electric utility companies to monitor intrusive vegetation near powerlines. The study proposes a deep learning-based detection framework compatible with drones for monitoring vegetation encroachment near powerlines which estimates vegetation health and detects powerlines. Aerial image pairs from a drone camera and a commercial-grade multispectral sensor were captured and processed into training and validation datasets which were used to train a Generative Adversarial Network (Pix2Pix model) and a Convolutional Neural Network (YoLov5 model). The Pix2Pix model generated satisfactory synthetic image translations from coloured images to Look-Up Table (LUT) maps whiles the YoLov5 obtained good performance for detecting powerlines in aerial images with precision, recall, mean Average Precision (mAP) @0.5, and mAP0.5:0.95 values are 0.82, 0.76, 0.79 and 0.56 respectively. The proposed vegetation detection framework was able to detect locations of powerlines and generate NDVI estimates represented as LUT maps directly from RGB images captured from aerial images which could serve as a preliminary and affordable alternative to relatively expensive multispectral sensors which are not readily available in developing countries for monitoring and managing the presence and health of trees and dense vegetation within powerline corridors.

Over the past few decades, groundwater has become a vital resource due to its increasing demand owing to rapid population growth and industrialization. The sustainable development and management of groundwater resources need intensive research using current innovative techniques. Therefore, in this study, an integrated approach involving GIS and remote sensing techniques was applied to thematic layers such as geology, geomorphology, drainage density, lineament density, slope, soil, and land use land cover to generate groundwater potential zone map. The thematic layers and their features were assigned weights based on their relative influence on groundwater occurrence. The weighted thematic layers were overlaid using a weighted sum in ArcMap 10.3 to create the groundwater potential zones map. The final output map shows different zones of groundwater potential ranging from very good, good, moderate, fair to poor. The very good to moderate groundwater potential zones are mainly around the southern, eastern, and western fringes of the district, whereas the fair to poor groundwater potential zones cover the northern and central fringes and some parts of the southern domain. The groundwater potential zones map was finally validated using borehole yield from data of 22 boreholes, which showed a good correlation and is found to be helpful in developing better strategies for groundwater exploitation.

Groundwater is the preferred choice of drinking water for the most dispersed and rural communities in Ghana’s Central Region. Groundwater potential zone delineation has been identified as a vital step to help accelerate the achievement of Sustainable Development Goal Six (SDG 6) and contribute to the realization of sustainable water and land management. The central region is generally characterised by low success rate of drilling and water quality challenges particular along the coast. To characterize the groundwater potential zones of the region for drinking purposes, hydrogeological and hydrochemical data, coupled with Remote Sensing (RS) and Geographic Information Systems (GIS), were used. The weight of each theme (soil type/geology, rainfall, slope, land use/land cover, lineament density, etc.) was determined through the Fuzzy Analytic Hierarchy Process (FAHP) and overlaid in the ArcGIS 10.3 programme to generate the groundwater potential map of the region. The output map was classified into the following five groundwater potential zones: viz. very poor 0.03% (2.6 km2), poor 10.58% (1039.3 km2), moderate 11.78% (1157.4 km2), high 77.48% (7613.5 km2) and very high 0.13% (13.2 km2). The map provides a spatial distribution of the groundwater potential of the region to guide stakeholders involved in groundwater development and sustainable management of the resource in the provision of potable water for the coastal communities.

Groundwater is the main water resource which supplies more than half of the residents of Afram Plains. Recent studies show that the aquifer is subject to deteriorating quality from several anthropogenic activities. The aquifer vulnerability to contamination was assessed using the widely known DRASTIC model. This paper aimed to determine the depth-to-water table, identification of the various soil types, topography, recharge rate, and major lithological units in the area. The degree of vulnerability was identified by an index which is computed by superimposition of all the parameters considered for the model. Based on the vulnerability assessment, it was found that, 20.72% (1044.28 km2), 49.78% (2508.91 km2) and 29.50% (1486.80 km2) of the total area was classified as low, moderate and high, respectively. The final vulnerability index was largely influenced by the depth-to-water table, impact of the vadose zone, net recharge and aquifer media parameters. The model was validated with on-ground nitrate concentrations in groundwater in the study area. The DRASTIC model exhibits highly correlated areas in mapping vulnerable regions. The maps produced in this study could be used as a general guide to vulnerability by planners, groundwater managers and decision-makers on the implementation of measures aimed at the protection of this vulnerable resource.

Current trends in hydrological modeling depend on spatial datasets, complex computational tasks and representation. As the need for the development of hydrological modeling capabilities have evolved, its integration with geographic information system (GIS) has provided a significant contribution to the efforts of hydrologic models. It serves the role of providing support in data capturing and improving hydrological modeling efforts by giving tools for effective analysis. This integration of GIS technology and hydrological modeling has resulted in great value and presents potential benefits to modelers and engineers. This paper reviewed critically, current trends of GIS technology in hydrological modeling. It also discussed the existing drawbacks as well as reviewed the future trends and prospects of this integration.

Floods in Ghana have become a perennial challenge in the major cities and communities located in low-lying areas. Therefore, cities and communities located in these areas have been classified as potential or natural flood-prone zones. In this study, the Digital Elevation Model (DEM) of the Accra Metropolis was used to assess the drainage density and elevation patterns of the area. The annual population estimation data and flood damages were assessed to understand the damages and population trend. This research focused primarily on the elevation patterns, slope patterns, and drainage density of the Accra Metropolis. Very high drainage density values, which range between 149 and 1117 m/m2, showed very high runoff converging areas. High drainage density was also found to be in the range of 1117–1702 m/m2, which defined the area as a high runoff converging point. The medium and low converging points of runoff were also found to be ranging between 1702–2563 m/m2and 2563–4070 m/m2, respectively. About 32% of the study area is covered by natural flood-prone zones, whereas flood-prone zones also covered 33% and frequent flood zones represent 25%. Areas in the Accra Metropolis that fall in the Accraian and Togo series rock types experience high floods. However, the lineament networks (geological structures) that dominate the Dahomeyan series imply that the geological structures in the Dahomeyan series also channel the runoffs into the low-lying areas, thereby contributing to the perennial flooding in the Accra Metropolis.