Self-optimization of falaj irrigation using case-based reasoning algorithms

This paper presents a novel application of case-based reasoning (CBR) for modernizing traditional falaj irrigation systems in arid regions, using a multi-level hierarchical framework that addresses challenges at provider, tenant, and user levels. The research employs a comprehensive methodology that integrates traditional water management practices with modern technologies while preserving cultural heritage. Through the implementation of CBR at Falaj Al Sarrani, the study demonstrates significant improvements in water conservation (58.3% reduction in water use), crop productivity (27.3% average yield increase), and economic returns (23.7% internal rate of return). The research evaluates five similarity functions across hierarchical levels, identifying optimal functions for each level: Manhattan distance for the provider level, Squared Chord for the tenant level, and Canberra for the user level. This level-specific optimization reduced the overall system error rate by 18% compared to using any single function across all levels. The findings provide valuable insights for water resource managers, agricultural agencies, and policymakers facing water scarcity challenges in arid and semi-arid regions.