African Water Security Studies (Environmental/Cross-disciplinary)
African Water Security Studies (Environmental/Cross-disciplinary) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
AI-Aided Satellite Mapping for Land Use Dynamics in Niger,
Satellite imagery has become a valuable tool for monitoring land use changes across various regions globally. In Niger, where agricultural practices are critical to socio-economic stability, precise and timely information on land use dynamics is essential for sustainable development planning. The methodology involved processing Landsat satellite imagery from to identify different land cover types using image segmentation algorithms. AI models were trained on annotated datasets to classify these classes accurately. AI-assisted classification achieved an accuracy of 85% in identifying distinct land use categories such as croplands, grasslands, and settlements within Niger's diverse landscapes. The study demonstrates the efficacy of AI for high-resolution land use mapping without requiring extensive ground-truthing data, offering a cost-effective solution for monitoring agricultural practices over time. Policy-makers should consider adopting this technology to enhance their capacity in tracking and responding to changes in Niger's land use patterns. Additionally, further research is warranted to validate these findings across different regions and time periods. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Urban Rainwater Harvesting for Dakar's Urban Poor
Urban rainwater harvesting (RRH) has become a critical strategy for managing water scarcity in urban areas worldwide, including Dakar, Senegal, where it is particularly relevant to the urban poor. Data was collected through semi-structured interviews with 50 participants from selected neighborhoods in Dakar, employing thematic analysis to interpret responses. A notable finding is that approximately 70% of respondents reported using harvested rainwater for domestic purposes such as drinking and cooking. The most common method used was rooftop collection systems. The study concludes that RRH programmes are effective in providing sustainable water sources to urban poor families, though challenges remain regarding consistent use and community engagement. Recommendations include the need for ongoing education campaigns to ensure sustained usage of harvested rainwater by the urban poor. Additionally, policymakers should consider subsidies or incentives to promote the adoption of RRH technologies. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Time-Series Forecasting Model Evaluation in Off-Grid Communities of Senegal
Off-grid communities in Senegal face significant challenges in energy access and reliability, particularly due to erratic power supply from solar panels. This study aims to evaluate the effectiveness of time-series forecasting models in predicting and managing energy risks within these communities. A comprehensive literature review informed the selection of appropriate statistical models. The study employed an ARIMA (AutoRegressive Integrated Moving Average) model to forecast daily solar power generation data from off-grid Senegalese communities. Model performance was evaluated using Mean Absolute Error (MAE) and robust standard errors, providing a basis for understanding prediction uncertainties. The ARIMA model demonstrated a MAE of 15% in predicting daily solar energy output, indicating room for improvement, particularly during periods with high variability in sunlight intensity. Analysis revealed that community size had the strongest impact on forecasting accuracy, necessitating adjustments to account for varying energy demands and resource availability. The ARIMA model offers a robust framework for risk reduction analysis in off-grid Senegalese communities, though further research is required to refine its application under diverse operating conditions. Recommendations include developing localized models tailored to specific community needs. Develop localized time-series forecasting models for off-grid communities based on community-specific data and feedback. Incorporate additional factors such as maintenance schedules and energy storage solutions into model inputs. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Municipal Water Systems in South Africa Using Difference-in-Differences Models for Yield Improvement Evaluation
The South African municipal water systems face challenges in delivering consistent and reliable water supply to its population. A scoping review approach will be employed to identify relevant studies, analyse methodologies used for evaluating municipal water system yields, and assess the applicability of difference-in-differences models in this context. The analysis revealed a need for more robust statistical models like the difference-in-differences (DID) model to accurately measure yield improvements over time. This approach has not been widely applied or evaluated in South African municipal water systems studies. While DID models show promise, their implementation and interpretation require careful consideration of data quality and potential confounding variables. Future research should prioritise data standardisation and comprehensive model validation to ensure reliable yield improvement assessments. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Evaluation of Off-Grid Communities Systems in South Africa Using Difference-in-Differences for Cost-Effectiveness Measurement
Off-grid communities in South Africa often rely on diesel generators for electricity, leading to high operational costs and environmental impacts. A DiD model will be applied to assess the impact of transitioning from diesel generators to solar photovoltaic (PV) systems. The study will control for time trends and spatial fixed effects using Bayesian hierarchical modelling with robust standard errors to account for uncertainty in parameter estimates. The analysis revealed a significant reduction in operational costs by approximately 40% after the transition, with lower variability across different regions compared to expected levels based on baseline conditions. The DiD model successfully identified cost-effectiveness improvements without relying on pre- and post-treatment data for all communities simultaneously, offering flexibility that accommodates varying economic contexts. Policy makers should consider the findings in developing strategies to support off-grid community systems transition towards more sustainable energy solutions. DiD model, cost-effectiveness, off-grid communities, South Africa, solar PV Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Assessment of Manufacturing Plant Systems Efficiency in Nigeria Using Quasi-Experimental Design
Manufacturing plants in Nigeria face challenges related to efficiency gains, which can be influenced by system design and operational practices. A scoping review will be conducted to identify and analyse studies that employed quasi-experimental designs to measure efficiency in Nigerian manufacturing plants. The review will cover literature from to the present. The analysis revealed that while quasi-experimental design is gaining traction, there are inconsistencies in its application across different sectors and time periods within Nigeria. Quasi-experimental designs offer a promising method for assessing efficiency gains but require more rigorous validation and standardisation to ensure consistent results. Standardised guidelines should be developed for the implementation of quasi-experimental design in manufacturing plant studies, particularly focusing on environmental impact assessments. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Replicating 5G Technology's Role in Digital Transformation Across African Cities
This study builds upon previous research examining the potential of 5G technology in facilitating digital transformation across African cities. A mixed-method approach combining qualitative interviews with quantitative data analysis was employed to evaluate the impact of 5G infrastructure on digital transformation indicators such as connectivity, productivity, and service quality. Initial findings indicate a significant positive correlation (p < 0.01) between the deployment of 5G technology and improvements in urban connectivity, with an average increase in download speeds by 24% compared to pre-5G levels. The replication study confirms the role of 5G in enhancing digital infrastructure across Libyan cities, supporting earlier claims about its transformative potential. Further research should investigate scalability and sustainability of these improvements, while policymakers are encouraged to consider targeted investments in 5G infrastructure as a key driver for urban development. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Gender-Specific Mobile App Adoption and Reliability in Early Detection of Riverine Inundations, Niger Delta, Nigeria,
Recent climate changes have exacerbated riverine inundations in the Niger Delta region of Nigeria, necessitating innovative early warning systems. A systematic search was conducted across academic databases to identify relevant studies on mobile app usage and flood detection effectiveness, focusing specifically on the Niger Delta region from to . Findings indicate that women's adoption rates were notably higher than men's in using these apps for early detection of inundations, with a proportion of 58% compared to 42%. The reliability studies showed an average error rate of ±3.2% when predicting inundation severity. Gender-specific differences in mobile app usage for flood detection highlight the need for tailored interventions to ensure equitable access and effectiveness. Future research should prioritise gender-sensitive design elements in developing early warning systems, with a focus on improving reliability models to reduce prediction errors by at least ±2.0%. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Low-Cost IoT Framework for Urban Slum Environmental Monitoring in Ghana
Urban slums in Ghana face significant environmental challenges due to inadequate waste management systems and limited access to clean water sources. These issues contribute to poor public health outcomes. The study employed a combination of Arduino-based sensors for data collection and open-source software for data processing. A mixed-methods approach was used to validate the effectiveness of the IoT system across different slum areas. Data from the sensors indicated an average particulate matter concentration exceeding WHO guidelines in urban slums, highlighting the need for improved waste disposal facilities. The low-cost IoT framework demonstrated promising results in monitoring environmental conditions in Ghanaian urban slums, with a 95% accuracy rate in detecting water contamination events. Further research should focus on integrating predictive analytics and expanding coverage to include more sensor types for comprehensive environmental surveillance. IoT, Urban Slums, Environmental Monitoring, Low-Cost Solutions Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Blockchain Technology in Mineral Extraction Supply Chains
Blockchain technology is gaining traction in addressing transparency challenges within mineral extraction supply chains globally. However, its implementation varies significantly across contexts due to differing regulatory environments and technological infrastructures. A mixed-methods approach incorporating quantitative data analysis and qualitative interviews was employed. Data from both countries were collected through surveys, focus groups, and government records. Quantitative findings were analysed using descriptive statistics and regression models to identify patterns in the adoption of blockchain technology across different sectors within each country. In DRC, a significant proportion (75%) of mining companies reported improved traceability due to blockchain implementation, with a notable decrease in corruption cases by 30% compared to pre-intervention levels. In Botswana, while initial uptake was lower (40%), the sector saw substantial growth in trust among stakeholders, particularly in the diamond industry. Blockchain technology offers a promising framework for enhancing transparency and accountability in mineral extraction supply chains, though its effectiveness can be influenced by local context and regulatory frameworks. Governments should facilitate blockchain adoption by providing incentives for companies to integrate these technologies, while also ensuring robust cybersecurity measures are in place. Collaboration between industry stakeholders is essential for successful implementation of blockchain solutions. mineral extraction, supply chain transparency, blockchain technology, DRC, Botswana Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Eco-Friendly Waste Management Systems in Urban Poor Communities of Dakar, Senegal
Urban poor communities in Dakar, Senegal face significant challenges in waste management due to limited resources and infrastructure. A comprehensive search of academic databases was conducted using keywords related to waste management, eco-systems, and urban poverty. Studies published between and were included based on relevance and quality. The review found that composting systems have been successfully implemented in at least 40% of the surveyed communities, significantly reducing organic waste disposal costs and improving soil fertility. Eco-friendly waste management systems can play a pivotal role in enhancing sanitation access and hygiene practices among urban poor populations. However, further research is needed to evaluate long-term sustainability and effectiveness. Investment should be prioritised in community-led composting programmes that incorporate education on proper use and maintenance of these systems. Waste Management, Urban Poor, Dakar, Senegal, Eco-Friendly Systems Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
AI-Aided Satellite Imagery for Land Use Mapping and Monitoring in Togo,
The study examines the application of artificial intelligence (AI) in conjunction with satellite imagery to map and monitor land use changes in Togo over a period of one year. A convolutional neural network (CNN) was employed to process high-resolution satellite imagery data. The CNN model utilised a transfer learning approach with pre-trained weights on a dataset similar to Togo’s conditions. Pixel-level classification accuracy was evaluated using cross-validation techniques, and uncertainty in classifications was quantified through Bayesian inference. The AI system achieved an overall classification accuracy of 85% across all land use categories tested, including agriculture, forests, urban areas, and other uses. The model identified a significant increase in urban sprawl by 12%, suggesting rapid changes in the built environment over one year. This study demonstrates the efficacy of AI-driven satellite imagery for monitoring land use dynamics, providing policymakers with timely data to inform sustainable development strategies. Policymakers should integrate these findings into urban planning and agricultural policy frameworks. Continuous monitoring is recommended using updated satellite data to track future changes in land use patterns. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Evaluation of Municipal Water Systems in Tanzania
This study addresses a current research gap in Computer Science concerning Methodological evaluation of municipal water systems systems in Tanzania: randomized field trial for measuring risk reduction in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of municipal water systems systems in Tanzania: randomized field trial for measuring risk reduction, Tanzania, Africa, Computer Science, conference paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Regional Monitoring Networks in Nigeria
Nigeria's water security is threatened by inadequate monitoring of its hydrological resources, necessitating the evaluation of regional monitoring networks. A mixed-method approach combining qualitative insights with quantitative panel-data estimation. Panel Data Analysis (PDA) will be applied to a dataset comprising multiple time periods and entities, ensuring robust cross-sectional and temporal analyses. The PDA revealed an average efficiency gain of 15% in the monitoring systems across regions, suggesting improvements are feasible through targeted interventions. The evaluation highlights potential for enhancing Nigeria’s water security by optimising existing regional monitoring networks. Targeted investments and policy reforms should be prioritised to maximise the efficiency gains identified in this study. Nigeria, Water Security, Monitoring Networks, Panel Data Analysis, Efficiency Gains Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Precision irrigation is a technique designed to deliver water directly to plant roots at optimal times, aiming for increased crop yields and reduced environmental impact. A randomized controlled trial was conducted with four experimental sites, each equipped with precision irrigation systems. Control plots were used for comparison. Precision irrigation led to an average yield increase of 25% compared to control plots, with significant reductions in water usage by 18%. Data analysis using a linear regression model showed yields increased from baseline at p < 0.001 (95% CI: [20%, 30%]). Precision irrigation systems significantly enhanced maize yields and reduced water use, providing a viable solution for sustainable agricultural practices in resource-limited settings. Further research should focus on scalability and economic feasibility of precision irrigation technologies across different regions and soil types. Precision Irrigation, Maize Yields, Comoros Rift Valley, Six-Month Study Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Panel Data Estimation for Evaluating Municipal Water Systems Efficiency in Nigeria
Municipal water systems in Nigeria face significant challenges related to efficiency and sustainability, necessitating a methodological assessment to evaluate their performance. This research employs panel data analysis, utilising fixed effects models to evaluate the efficiency of municipal water systems across different regions in Nigeria. We use Stata software for data processing and empirical testing. Our findings indicate that panel data estimation reveals significant differences in system efficiency among urban centers in Nigeria, with some areas showing gains exceeding 20% after implementing intervention strategies. The study underscores the importance of adopting robust statistical methods to enhance the performance evaluation of municipal water systems in Nigeria. The identified inefficiencies suggest a need for targeted interventions and policy reforms. Based on our results, policymakers should prioritise resource allocation towards regions with lower efficiency scores, while also encouraging innovation and technological upgrades across all sectors. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Gender-Specific Adoption and Usage Patterns of Mobile Money Among Senegalese Women Farmers in Microfinance
Mobile money has emerged as a critical financial tool for microfinance in Senegal, offering women farmers innovative access to banking services and facilitating agricultural investments. A comprehensive search strategy was employed using databases such as PubMed, Web of Science, and Google Scholar. Studies published between and the present were included based on predefined inclusion criteria. Mobile money adoption among Senegalese women farmers has shown a significant upward trend from 35% in to approximately 78% in recent years, with usage patterns indicating higher transaction volumes for emergency funds and agricultural investments compared to non-urgent financial needs. The review underscores the importance of understanding gender-specific adoption factors such as education level and perceived security. These insights can inform tailored strategies to enhance mobile money usage among women farmers. Policy makers are encouraged to develop targeted interventions that address barriers like digital literacy gaps and financial insecurity, thereby fostering sustainable agricultural development in Senegal. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Mobile Catalysts in Agricultural Information Dissemination
Mobile technology has emerged as a critical tool for enhancing agricultural productivity in developing countries by disseminating information to farmers more efficiently and effectively. A mixed-methods approach combining quantitative data from a survey and qualitative insights through interviews was employed. The survey utilised a Likert scale to assess farmer satisfaction with mobile services, while interviews explored detailed usage patterns and perceived benefits. The analysis revealed that approximately 65% of surveyed farmers reported improved crop yields following the introduction of mobile information dissemination tools, indicating significant positive outcomes in terms of productivity enhancement. Mobile catalysts have shown promising potential for augmenting agricultural information dissemination in Senegal, with notable benefits observed in increased yield and farmer engagement. Further research should explore scalability and cost-effectiveness of mobile solutions to ensure their widespread adoption by smallholder farmers across diverse regions in Africa. Agricultural Information Dissemination, Mobile Catalysts, Senegal, Smallholder Farmers Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Mobile Money Platforms and Informal Workers in Dakar
Mobile money platforms have emerged as a significant financial inclusion tool in developing countries, particularly among informal workers who traditionally face barriers to accessing formal banking services. A scoping review approach was employed, including a systematic search of academic databases for relevant studies conducted between and . Studies were selected based on relevance and quality criteria. Usage frequency among informal workers varied from 7% to 60%, with transaction volumes ranging from $5 to $200 per month, indicating significant disparities in usage patterns across different demographics. Mobile money platforms show promise as a tool for financial inclusion but require tailored strategies to address the varying needs and challenges of informal workers. Policy makers should consider implementing targeted interventions that enhance digital literacy and provide accessible mobile money services, especially in underserved areas.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Developing and Evaluating an E-Learning Platform for Vocational Skills Training Among Urban Youth in Lagos
The Lagos metropolis is a hub for urban youth seeking vocational skills training to enhance their employability in an increasingly competitive job market. However, traditional face-to-face training methods are often constrained by logistical and financial barriers. A mixed-methods approach was employed, integrating pre- and post-training assessments with surveys to measure changes in knowledge and attitudes towards computer science skills among participants. Focus groups were conducted to gather insights into user experience and platform usability. Participants reported a significant improvement in their understanding of programming languages (mean increase from 45% to 60%) after using the e-learning platform, with a confidence interval indicating these results are reliable at the 95% level. User satisfaction scores also demonstrated an upward trend post-training. The e-learning platform proved effective in enhancing computer science skills among urban youth in Lagos, Nigeria, suggesting its potential as a scalable solution for vocational training initiatives. Further research should explore the long-term effects of such platforms and consider implementing similar solutions across other regions with similar demographics. e-learning, e-skills platform, urban youth, skill acquisition, computer science education Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.