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Publisher: Politechnika LubelskaSubject: climate change

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    Article
    Citation - WoS: 6
    Citation - Scopus: 6
    Model-Driven Engineering and Creative Arts Approach To Designing Climate Change Response System for Rural Africa: a Case Study of Adum-Aiona Community in Nigeria
    (Politechnika Lubelska, 2017) Okewu, Emmanuel; Misra, Sanjay; Okewu, Jonathan; Computer Engineering
    Experts at the just concluded climate summit in Paris (COP21) are unanimous in opinion that except urgent measures are taken by all humans, average global temperature rise would soon reach the deadly 2 degrees C mark. When this happens, socio-economic livelihoods, particularly in developing economies, would be dealt lethal blow in the wake of associated natural causes such as increased disease burden, soil nutrient destruction, desertification, food insecurity, among others. To avert imminent dangers, nations, including those from Africa, signed a legally binding universally accepted climate control protocol to propagate and regulate environmentally-friendly behaviours globally. The climate vulnerability of Africa as established by literature is concerning. Despite contributing relatively less than other continents to aggregate environmental injustice, the continent is projected to bear the most brunt of environmental degradation. This is on account of her inability to put systems and mechanisms in place to stem consequences of climate change. Hence, our resolve to use a combination of scientific and artistic models to design a response system for tackling climate challenges in Africa. Our model formulation encompasses computational model and creative arts model for drawing attention to environmentally friendly behaviours and climate adaptation and mitigation strategies. In this work, we focus on rural Africa to share experience of climate change impact on agriculture mainstay of rural African economy. We examine the carbon footprints of a rural community in Nigeria the Adum-Aiona community as case study and for industrial experience. The authors will provide operational data to substantiate claims of existential threats posed by greenhouse gas (GHG) generation on livelihoods of rural dwellers. The study will also design and test a Climate Change Response System (CCRS) that will enable people to adapt and reduce climate change impact. To achieve the research objective, the researchers will review literature, gather requirements, model the proposed system using Unified Modelling Language (UML), and test CCRS statically. We expect that the implementation of the proposed system will enable people mitigate the effects of, and adapt to, climate change-induced socio-economic realities. This is besides the fact that the empirical data provided by the study will help clear doubts about the real or perceived threats of climate change. Finally, the industrial experience and case study we share from Africa using model-driven engineering approach will scale up the repository of knowledge of both climate change research and model-driven engineering community.
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    Article
    Citation - WoS: 11
    Citation - Scopus: 16
    Deep Neural Networks for Curbing Climate Change-Induced Farmers-Herdsmen Clashes in a Sustainable Social Inclusion Initiative
    (Politechnika Lubelska, 2019) Okewu, Emmanuel; Misra, Sanjay; Fernandez Sanz, Luis; Ayeni, Foluso; Mbarika, Victor; Damasevicius, Robertas; Computer Engineering
    Peaceful coexistence of farmers and pastoralists is becoming increasingly elusive and has adverse impact on agricultural revolution and global food security. The targets of Sustainable Development Goal 16 (SDG 16) include promoting peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. As a soft approach and long term solution to the perennial farmers herdsmen clashes with attendant humanitarian crisis, this study proposes a social inclusion architecture using deep neural network (DNN). This is against the backdrop that formulating policies and implementing programmes based on unbiased information obtained from historical agricultural data using intelligent technology like deep neural network (DNN) can be handy in managing emotions. In this vision paper, a DNN-based Farmers-Herdsmen Expert System (FHES) is proposed based on data obtained from the Nigerian National Bureau of Statistics for tackling the incessant climate change induced farmers-herdsmen clashes, with particular reference to Nigeria. So far, many lives have been lost. FHES is modelled as a deep neural network and trained using farmers-herdsmen historical data. Input variables used include land, water, vegetation, and implements while the output is farmers/herders disposition to peace. Regression analysis and pattern recognition performed by the DNN on the farmers-herdsmen data will enrich the inference engine of FHES with extracted rules (knowledge base). This knowledge base is then relied upon to classify future behaviours of herdsmen/farmers as well as predict their dispositions to violence. Critical stakeholders like governments, service providers and researchers can leverage on such advisory to initiate proactive and socially inclusive conflict prevention measures such as, people-friendly policies, programmes and legislations. This way, conflicts can be averted, national security challenges tackled, and peaceful atmosphere guaranteed for sustainable development.