Deep Neural Networks for Curbing Climate Change-Induced Farmers-Herdsmen Clashes in a Sustainable Social Inclusion Initiative
Emmanuel Okewu
University of Lagos, Lagos, Nigeria (Nigeria)
Sanjay Misra
Covenant University, Canaanland, Nigeria; Atilim University, Ankara, Turkey (Nigeria)
Luis Fernandez Sanz
University of Alcala, Alcala De Henares, Madrid, Spain (Spain)
Foluso Ayeni
Southern University, Baton Rouge, USA (United States)
Victor Mbarika
Southern University, Baton Rouge, USA (United States)
Robertas Damaševičius
Kaunas University of Technology, Kaunas, Lithuania (Lithuania)
Abstract
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.
Keywords:
climate change, deep neural network, farmers-herdsmen clashes, policies and programmes, social inclusionReferences
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Authors
Emmanuel OkewuUniversity of Lagos, Lagos, Nigeria Nigeria
Authors
Sanjay MisraCovenant University, Canaanland, Nigeria; Atilim University, Ankara, Turkey Nigeria
Authors
Luis Fernandez SanzUniversity of Alcala, Alcala De Henares, Madrid, Spain Spain
Authors
Foluso AyeniSouthern University, Baton Rouge, USA United States
Authors
Victor MbarikaSouthern University, Baton Rouge, USA United States
Authors
Robertas DamaševičiusKaunas University of Technology, Kaunas, Lithuania Lithuania
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