MLBlock: Harnessing Machine Learning, Blockchain, and XAI for Smarter Land Valuation
Alauddin Sabari, Md Jakir Hossain, Imran Hasan, Abdullah All Ahhad, Md Shafiqul Islam, Feroza Naznin, Md Zahidul Islam · 2024
Accurate and transparent land valuation is critical for sustainable urban planning, yet traditional methods often lack flexibility and reliability. This study presents ML-Block, a framework integrating blockchain, machine learning (ML), and Explainable AI (XAI) to address these challenges. The methodology begins with secure data validation through blockchain, ensuring tamper-proof and verifiable user data. Advanced ML techniques, including Reinforcement Learning (RL) and polynomial regression, enhance predictive accuracy by 20% over conventional approaches, achieving RMSE values between 200-300 USD and R2above 0.8. RL adds a crucial layer of adaptability, enabling the model to learn from dynamic market trends and adjust predictions in real time. XAI provides clear, interpretable insights, fostering trust among stakeholders such as governments, real estate firms, and community planners [22]. Interactive visualization tools, powered by Google Maps API, identify undervalued regions and economic trends, supporting equitable development. This framework demonstrates how secure data handling, adaptable models, and transparent predictions can bridge the gap between technological innovation and real-world applications, promoting informed decision-making across diverse stakeholders, governments, and real estate investors.