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Groundwater is an important element of the hydrological cycle and has increased in importance due to insufficient surface water supply. Mismanagement and population growth have been identified as the main drivers of water shortage in the continent. This study aimed to derive a groundwater potential zone (GWPZ) map for Nelson Mandela Bay (NMB) District, South Africa using a geographical information system (GIS)-based analytic hierarchical process (AHP) and machine learning (ML) random forest (RF) algorithm. Various hydrological, topographical, remote sensing-based, and lithological factors were employed as groundwater-controlling factors, which included precipitation, land use and land cover, lineament density, topographic wetness index, drainage density, slope, lithology, and soil properties. These factors were weighted and scaled by the AHP technique and their influence on groundwater potential. A total of 1371 borehole samples were divided into 70:30 proportions for model training (960) and model validation (411). Borehole location training data with groundwater factors were incorporated into the RF algorithm to predict GWPM. The model output was validated by the receiver-operating characteristic (ROC) curve, and the models’ reliability was assessed by the area under the curve (AUC) score. The resulting groundwater-potential maps were derived using a weighted overlay for AHP and RF models. GWPM computed using weighted overlay classified groundwater potential zones (GWPZs) as having low (2.64%), moderate (29.88%), high (59.62%) and very high (7.86%) groundwater potential, whereas GWPZs computed using RF classified GWPZs as having low (0.05%), moderate (31.00%), high (62.80%) and very high (6.16%) groundwater potential. The RF model showed superior performance in predicting GWPZs in Nelson Mandela Bay with an AUC score of 0.81 compared to AHP with an AUC score of 0.79. The results reveal that Nelson Mandela Bay has high groundwater potential, but there is a water supply shortage, partially caused by inadequate planning, management, and capacity in identifying potential groundwater zones.

The Upper Indus basin (UIB) is characterized by contrasting hydrometeorological behaviors; therefore, it has become pertinent to understand hydrometeorological trends at the sub-watershed level. Many studies have investigated the snow cover and hydrometeorological modeling at basin level but none have reported the spatial variability of trends and their magnitude at a sub-basin level. This study was conducted to analyze the trends in the contrasting hydrological regimes of the snow and glacier-fed river catchments of the Hunza and Astore sub-basins of the UIB. Mann-Kendall and Sen’s slope methods were used to study the main trends and their magnitude using MODIS snow cover information (2001–2015) and hydrometeorological data. The results showed that in the Hunza basin, the river discharge and temperature were significantly (p ≤ 0.05) decreased with a Sen’s slope value of −2.541 m3·s−1·year−1 and −0.034 °C·year−1, respectively, while precipitation data showed a non-significant (p ≥ 0.05) increasing trend with a Sen’s slope value of 0.023 mm·year−1. In the Astore basin, the river discharge and precipitation are increasing significantly (p ≤ 0.05) with a Sen’s slope value of 1.039 m3·s−1·year−1 and 0.192 mm·year−1, respectively. The snow cover analysis results suggest that the Western Himalayas (the Astore basin) had a stable trend with a Sen’s slope of 0.07% year−1 and the Central Karakoram region (the Hunza River basin) shows a slightly increasing trend with a Sen’s slope of 0.394% year−1. Based on the results of this study it can be concluded that since both sub-basins are influenced by different climatological systems (monsoon and westerly), the results of those studies that treat the Upper Indus basin as one unit in hydrometeorological modeling should be used with caution. Furthermore, it is suggested that similar studies at the sub-basin level of the UIB will help in a better understanding of the Karakoram anomaly.

On February 05 2016, a significant and unfortunate series of mine disasters occurred at the Lily gold mine in South Africa, where 79 mineworkers were trapped under a huge rock and soil mass. In this paper, two separate geographical information system based models are proposed; one for surface and one for subsurface mine disasters. The multi-temporal (2004–2017) high-resolution pre and post-disaster satellite images were analyzed to assess the magnitude and spatial extent of the surface disaster. The explicit modeling technique was applied for mine subsidence using the Mohr–Coulomb failure criterion under gravitational acceleration. For surface disaster, the weighted overlay model was applied by assigning weights to hydrological (rainfall, flow direction, accumulation, and density); geological (geology and lineaments) and geomorphological (slope, aspect, and curvature) causative factors. The mine subsidence modeling results showed a subsidence zone of 55 × 24 × 71 m due to a failure of a crown pillar, whereas, in reality, it was 60 × 30 × 80 m. The deformation results showed that the lamp room wherein the miners were trapped could have been displaced somewhere at or below level 5 but above level 6 towards the southwest direction. The output of surface disaster modeling was also satisfactory and reliable as the actual two slope failures, i.e. the western and southern landslides of the Lily gold mine, located in extreme risk zones as predicted by the model. The results of this study can be useful for future mine planning and the environmental improvements at the Lily gold mine.

Accurate mapping of selective logging (SL) serves as the foundation for additional research on forest restoration and regeneration, species diversification and distribution, and ecosystem dynamics, among other applications. This study aimed to model canopy gaps created by illegal logging of Ocotea usambarensis in Mt. Kenya Forest Reserve (MKFR). A texture-spectral analysis approach was applied to exploit the potential of WorldView-3 (WV-3) multispectral imagery. First, texture properties were explored in the sub-band images using fused grey-level co-occurrence matrix (GLCM)- and local binary pattern (LBP)-based texture feature extraction. Second, the texture features were fused with colour using the multivariate local binary pattern (MLBP) model. The G-statistic and Euclidean distance similarity measures were applied to increase accuracy. The random forest (RF) and support vector machine (SVM) were used to identify and classify distinctive features in the texture and spectral domains of the WV-3 dataset. The variable importance measurement in RF ranked the relative influence of sets of variables in the classification models. Overall accuracy (OA) scores for the respective MLBP models were in the range of 80–95.1%. The respective user’s accuracy (UA) and producer’s accuracy (PA) for the univariate LBP and MLBP models were in the range of 67–75% and 77–100%, respectively.

The issue of air pollution has recently come to light due to rapid urbanization and population growth globally. Due to its impact on human health, such as causing lung and heart diseases, air quality monitoring is one of the main concerns. Improved air pollution forecasting techniques and systems are needed to minimize the human health impact. Systems that fall under the Internet of Things (IoT) topology have been developed to assess and track numerous air quality metrics. This paper presents a review of IoT systems for air quality measurements, where the emphasis is placed on systems with LTE/4G and LoRa communication capabilities. Firstly, an overview of the IoT monitoring system is provided with recent technologies in the market. A critical review is provided of IoT systems regarding air quality using LTE/4G and LoRa communications systems. Lastly, this paper presents a market analysis of commercial IoT devices in terms of the costs, availability of the device, particulate matter each device can measure, etc. A comparative study of these devices is also presented on LTE/4G and possibly LoRa communications systems.

The underground mining industry faces significant challenges in maintaining reliable communication due to multipath fading and physical obstructions, leading to weak signals and dead spots. This study addresses these issues by proposing a smart antenna system with circular polarization and beam steering capabilities. The system utilizes a four-element square patch array and a Butler matrix for beamforming, enabling directional signal transmission. The antenna was designed and optimized using CST simulations. The experimental results demonstrate the antenna’s ability to steer beams in four directions, significantly reducing signal interference and improving coverage. The antenna achieved a bandwidth of 400 MHz (5.52–5.99 GHz) and a gain of up to 9.69 dBi, effectively mitigating polarization mismatches. The novelty of this study lies in the integration of circular polarization and beam steering into a compact, cost-effective system, specifically designed to enhance communication in underground mining environments. This solution improves both safety and operational efficiency by providing reliable communication in harsh conditions.

Water scarcity is influencing environmental and socio-economic development on a global scale. Pakistan is ranked third among the countries facing water scarcity. This situation is currently generating intra-provincial water disputes and could lead to transboundary water conflicts. This study assessed the future water resources of Astore basin under representative concentration pathways (RCP) 4.5 and 8.5 scenarios using fully distributed TOPographic Kinematic APproximation and Integration (TOPKAPI) model. TOPKAPI model was calibrated and validated over five years from 1999–2003 with a Nash coefficient ranging from 0.93–0.97. Towards the end of the 21st century, the air temperature of Astore will increase by 3°C and 9.6 °C under the RCP4.5 and 8.5 scenarios, respectively. The rise in air temperature can decrease the snow cover with Mann Kendall trend of –0.12%/yr and –0.39%/yr (p ≥ 0.05) while annual discharge projected to be increased 11% (p ≤ 0.05) and 37% (p ≥ 0.05) under RCP4.5 and RCP8.5, respectively. Moreover, the Astore basin showed a different pattern of seasonal shifts, as surface runoff in summer monsoon season declined further due to a reduction in precipitation. In the spring season, the earlier onset of snow and glacier melting increased the runoff due to high temperature, regardless of the decreasing trend of precipitation. This increased surface runoff from snow/glacier melt of Upper Indus Basin (UIB) can potentially be utilized to develop water policy and planning new water harvesting and storage structures, to reduce the risk of flooding.

Background: Remote sensing have been intensively used across many disciplines, however, such information was limited in spatial epidemiology. Methods: Two years (2009 & 2010) Landsat TM satellite data was used to develop vegetation, water bodies, air temperature and humidity criterion maps to model malaria risk and its spatiotemporal seasonal variation. The criterion maps were used in weighted overlay analysis to generate final categorized malaria risk map. Results: Overall, 25%, 68%, 18% and 16% of the total area of Rawalpindi region was categorized as danger zone for Jun 2009, Oct 2009, Jan 2010 and Jun 2010, respectively. The malaria risk reached at its peak during the monsoon season whereas air temperature and relative humidity were the main contributing factors in seasonal variation. Conclusion: Malaria risk maps could be used for prioritizing areas for malaria control measures.

Snow cover area (SCA) is an important component of the solid water reservoir in the catchment. The study of snow trends is essential for managing water resources and for understanding regional climate change. Changes in the snow budget have socioeconomic and environmental implications for agriculture, water-based industries, environment, land management, water supplies; and many other areas related with snow melt water resources. To date, however, only a few scientific studies are available to analyze the Upper Indus Basin (UIB). The basic objective of this study was to map the change assessment of SCA of UIB in 2003 and 2013. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were retrieved for the period of 2003 and 2013. Three different digital image processing techniques, including normalized difference snow index (NDSI), satellite image classification and band threshold values were applied to assess the SCA. The results show that snow accumulation typically starts from the beginning of October and continues up to mid of March. From end March, the snow starts melting until it is reduced to a minimum in September. A comparison of snow cover of 2003 and 2013 clearly indicates that the snow accumulation period has shifted and anomaly was observed in the start of November. In 2013, snow cover decreases by almost 49% area during the period 30 Sep to 15 Oct, whereas it increases by 133% area in the first sixteen days of November i.e. 1-16 Nov, as compared to the year 2003. Overall, the correlation between the year 2003 and 2013 SCA is found to be 0.87, which is highly positive correlation.

The novel COVID-19 (coronavirus disease of 2019) pandemic has caused global havoc and impacted almost every aspect of human life and the global economy. The mining industry is not immune to such impacts. The pandemic has accelerated the need for digital transformation in the mining industry and in the era of the fourth Industrial Revolution (4IR), there is further application of digital technologies in the early detection and prescreening of emerging infectious and viral diseases to keep mining areas and communities safer and less vulnerable. This paper aims to explore the application of smart digital technologies that could be applied for detection, prescreening and prevention of COVID-19 in the mining industry. The study will contribute, firstly, to demonstrate the utility and applications of digital technologies in the mining industry and, secondly, the development of a body of knowledge that can be consulted to prevent the spread of the disease in the mining industry.