THE ROLE OF PREDICTIVE ANALYTICS FOR DESERTIFICATION CONTROL: A CASE STUDY OF SOKOTO STATE’S SEMI-ARID ZONES
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Background of the Study
Desertification is a pressing environmental challenge affecting semi-arid regions globally. In Sokoto State, desertification has led to land degradation, reduced agricultural productivity, and biodiversity loss. Predictive analytics, an AI-based approach, offers the potential to mitigate these issues by forecasting trends in land degradation and identifying high-risk areas.
With predictive analytics, it is possible to analyze climate data, soil properties, and human activities to generate models that anticipate desertification patterns. These insights enable stakeholders to implement targeted interventions, such as afforestation and sustainable farming practices.
Statement of the Problem
Sokoto State lacks an effective framework for using predictive analytics to combat desertification. Traditional approaches to land restoration have been largely reactive, leading to minimal long-term impact. This study addresses how predictive analytics can transform desertification control strategies.
Aim and Objectives of the Study
- To assess the effectiveness of predictive analytics in desertification control.
- To identify key factors contributing to desertification in Sokoto’s semi-arid zones.
- To propose data-driven strategies for sustainable land management.
Research Questions
- How can predictive analytics improve desertification control measures?
- What are the primary factors driving desertification in Sokoto State?
Research Hypothesis
- Predictive analytics significantly enhances desertification control efforts.
- Human activities are the primary drivers of desertification in Sokoto State.
- Data-driven interventions lead to more sustainable land management outcomes.
Significance of the Study
This research provides innovative solutions for combating desertification in Sokoto State, benefiting environmentalists, policymakers, and local communities reliant on agriculture.
Scope and Limitation of the Study
The study focuses on semi-arid zones in Sokoto State. Limitations include the availability of accurate historical data and challenges in validating predictive models in real-time.
Definition of Terms
- Predictive Analytics: The use of data, statistical algorithms, and AI to forecast future outcomes.
- Desertification: The degradation of land in arid regions due to climatic and human factors.
- Semi-Arid Zones: Regions characterized by low rainfall and limited vegetation.
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