Enhancing Streamflow Forecasting in Major West African Rivers by Utilizing Meta-Heuristic Algorithms and Climate Data Time Lag Analysis

Accurate streamflow prediction is essential for irrigation planning, water allocation, and flood risk management, particularly in water-scarce regions like the Niger River Basin. However, the complexity of hydrological processes and data limitations make reliable predictions challenging. This study optimizes Support Vector Machine (SVM) hyperparameters for daily streamflow prediction using time-lagged climate data and four metaheuristic algorithms—Binary Slime Mould Algorithm (BSMA), African Vulture Optimisation Algorithm (AVOA), Archery Algorithm (AA), and Intelligent Ice Fishing Algorithm (IIFA). Model performance was assessed using eight evaluation metrics, with results showing that AA and IIFA consistently outperform the others, achieving Nash-Sutcliffe Efficiency (NSE) values between 0.986–0.999 and 0.893–0.999, respectively. AVOA and BSMA show less consistent performance, with NSE ranges of 0.524–0.999 and 0.863–0.965, respectively. The study highlights the novel integration of multiple metaheuristic algorithms for optimizing machine learning models, offering insights into their effectiveness for hydrological prediction. By demonstrating the superior performance of AA and IIFA, this research provides a robust framework for enhancing long-term streamflow forecasting. These findings support improved water resource management in West Africa, helping policymakers address climate variability, water scarcity, and hydrological uncertainty.

Evaluating a Motion-Based Region Proposal Approach with Background Subtraction Methods for Small Drone Detection

The detection of drones in complex and dynamic environments poses significant challenges due to their small size and background clutter. This study aims to address these challenges by developing a motion-based pipeline that integrates background subtraction and deep learning-based classification to detect drones in video sequences. Two background subtraction methods, Mixture of Gaussians 2 (MOG2) and Visual Background Extractor (ViBe), are assessed to isolate potential drone regions in highly complex and dynamic backgrounds. These regions are then classified using the ResNet18 architecture. The Drone-vs-Bird dataset is utilized to test the algorithm, focusing on distinguishing drones from other dynamic objects such as birds, trees, and clouds. By leveraging motion-based information, the method enhances the drone detection process by reducing computational demands. Results show that ViBe achieves a recall of 0.956 and a precision of 0.078, while MOG2 achieves a recall of 0.857 and a precision of 0.034, highlighting the comparative advantages of ViBe in detecting small drones in challenging scenarios. These findings demonstrate the robustness of the proposed pipeline and its potential contribution to enhancing surveillance and security measures.

How Do Gender-Based Employment, Agricultural Machinery, and Fertilizers Influence Regional Agricultural Productivity? Panel Analyses for South and Southeast Asian Countries

The analysis delves into key strategies for enhancing agricultural productivity in Southeast Asia and South Asia. It underscores the vital role of mechanization, sustainable input practices, and gender-inclusive policies. Customized interventions in these realms hold promise for significantly amplifying agricultural performance in the region. Fertilizer and machinery productivity are pivotal factors that correlate strongly with overall agricultural productivity, as revealed by regression analyses. Notably, male employment in agriculture and agricultural machinery exhibits positive and substantial impacts on agricultural productivity, while female employment and fertilizer consumption indicators show significant yet negative associations. The study highlights systemic issues such as unequal resource access and differing gender roles in agriculture that may impede the immediate productivity gains from increased female labor force participation. Mechanization and efficient fertilizer utilization emerge as critical drivers of enhanced agricultural output, with consistent coefficients across models. Male employment consistently demonstrates a positive influence on productivity, emphasizing the significance of labor force engagement in agriculture. Moreover, the study underscores the imperative of judicious fertilizer management to avert environmental degradation and diminishing returns. The findings affirm the efficacy of the random effects model, supported by the Hausman test, which indicates congruence in results between fixed and random effects models. This methodological choice ensures robust and reliable conclusions regarding the relationships between male and female employment, machinery, fertilizer consumption, and agricultural productivity in South and Southeast Asia.

Energy Consumption and Economic Growth: Evidence from Electricity and Petroleum in Eastern Africa Region

This study investigates the distinct impacts of electricity and petroleum consumption on economic growth in Eastern Africa. Using a Panel Autoregressive Distributed Lag Model and data for a period spanning 2000 to 2021, the study examines both the short-run and long-run effects of these energy sources on Gross Domestic Product. The findings reveal that petroleum consumption has a statistically significant and positive impact on GDP in both the short run and long run. In contrast, while electricity consumption shows a positive but statistically insignificant effect on GDP in the short run, it exhibits a negative and statistically significant impact in the long run. These results suggest that policymakers in Eastern Africa should prioritize sustainable petroleum management to maximize its economic benefits while mitigating potential environmental risks. While the negative coefficient of electricity implies a corrective response of the variables to long-run equilibrium in the face of short-term shocks. As a result, it is recommended that economic shocks caused by energy consumption be considered in terms of their relationship to economic growth, whether positive or negative in the long or short term, as decision makers need to address their impact and limit such shocks on economic growth.

Icing Models and Mitigation Methods for Offshore Wind in Cold Climate Regions: A Review

Offshore wind turbines (OWTs) in cold climate regions have become increasingly significant due to the abundant wind resources with the development of renewable energy. These areas offer considerable potential for the development of OWTs. Generating energy for communities in cold climate regions involves overcoming significant challenges posed by the remote and harsh environmental conditions. This review presents the state-of-the-art research regarding prediction models for ice accretion on wind turbine components. Furthermore, this review summarizes advanced mitigation solutions, such as cold-weather packages and ice protection systems, designed to address icing issues. The present study identifies critical knowledge gaps in OWT deployment in cold climate regions and proposes future research directions.

Advancing Green Infrastructure Solutions in Rural Regions: Economic Impacts and Capacity Challenges in Southwest Ontario, Canada

Green infrastructure (GI) is a growing topic in urban planning, asset management, and climate change adaptation. However, rural regions have been under-represented in the discourse. This paper explores the benefits and challenges associated with the implementation and management of GI through a regional study of rural communities in southwestern Ontario. Our focus concerns the inter-relationships between GI, economic resilience, and the development of rural places. Findings show rural communities benefit from GI initiatives like natural stormwater management, park naturalization, and natural heritage restoration, which provide low-cost municipal services, conserve agricultural soils, and contribute to the amenity appeal of rural places. Challenges surrounding awareness, organizational capacity, and environmental regulation have slowed the uptake of GI and led to inconsistencies across jurisdictions. A mix of supportive policies, funding of demonstration projects with economic monitoring, and training to build professional capacity will advance the use and efficacy of GI across rural regions.

Geographical Discrepancies in Higher Education in Sweden

There is a growing awareness of the importance of higher education in Sweden to reduce social differences in society. There are also various mechanisms that individuals relate to that favour either the status quo or change based on an ideal of higher education. Individuals live in a geographical context with a number of ‘key actors’ who influence the perception of higher education with varying degrees of intensity. Paradoxically, despite several reforms to broaden recruitment, it can be seen that relative inequalities persist in terms of residents with higher education in Sweden, not least from a regional perspective. The purpose of this article is to shed light on geographical differences in the higher education level of the population over time from a Swedish perspective. The study shows that higher education has a geographical centre-periphery perspective, but not exclusively. There are thus additional influencing factors that in various ways relate to the social context in which the individual is located. We can conclude from our empirical data that the reforms implemented to broaden recruitment have not had the desired effect, especially for the group of men. We find it likely that what differentiates women and men is who their individual ‘key players’ are and how they interact. From an academic education perspective and as an intermediary of higher education, there is therefore a challenge to be able to identify who these “key players” are in order to be able to be an important actor in contributing to the desired broader recruitment that the government is striving to achieve.

Welcome to the Inaugural Issue of Regional and Rural Development

Current Challenges to the Sustainable Development of Rural Communities in Russia's Central Chernozem Region

The rural community system in the Central Chernozem Economic Region in Russia is undergoing a radical transformation under the interrelated influence of fundamental factors that have rendered the development of many communities unsustainable. This paper analyses the role of urbanisation processes in population changes and transformation of rural community systems in the region; determines the level of horizontal mobility among the rural population, as well as its impact on settlement evolution; assesses the share of small and extremely small communities in settlement composition; and outlines these communities’ future development prospects. The authors believe that the socio-demographic “desertification” of peripheral municipalities can pose challenges to rural development: a shortage of labour resources, changes in population quality, and problems of innovation diffusion. The study recommends improving the comfort of the living environment and accelerating the technical re-equipment and automation of agricultural production.