1.1 Background of the Study
Desertification is a major environmental issue in Sokoto State and other regions in Northern Nigeria, resulting in reduced agricultural productivity, loss of biodiversity, and increased socio-economic vulnerabilities. Traditional methods of forecasting desertification rely heavily on historical data and manual observations, which are often insufficient for accurately predicting trends and implementing effective mitigation strategies.
Artificial Intelligence (AI) offers advanced capabilities in forecasting desertification trends by leveraging machine learning algorithms, geospatial data analysis, and predictive modeling. AI-driven tools can analyze environmental factors such as soil degradation, vegetation loss, and climate variability to forecast desertification risks with high accuracy (Mustapha & Abubakar, 2025). This study evaluates the role of AI in forecasting desertification trends in Sokoto State, highlighting its potential for sustainable land management and climate adaptation strategies.
1.2 Statement of the Problem
Desertification poses a severe threat to Sokoto State, yet traditional forecasting methods are limited in their ability to provide timely and accurate predictions. AI technologies offer promising solutions for improving the accuracy and efficiency of desertification forecasting, but their application in Nigeria remains underexplored. This study addresses the gap by examining the effectiveness of AI-driven tools in predicting desertification trends in Sokoto State.
1.3 Objectives of the Study
1.4 Research Questions
1.5 Research Hypothesis
1.6 Significance of the Study
This study highlights the importance of AI in addressing desertification challenges in Sokoto State. Its findings are valuable for policymakers, environmental managers, and researchers seeking sustainable solutions to land degradation in arid regions.
1.7 Scope and Limitations of the Study
The study focuses on the application of AI-driven tools in forecasting desertification trends in Sokoto State. It does not cover other regions or non-AI-based forecasting methods. Limitations include data availability and the early stage of AI adoption in environmental management in Nigeria.
1.8 Operational Definition of Terms
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Chapter One: Introduction
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