Irrigation networks in arid and semi-arid regions face water deficits and distribution losses, making reliable operation a challenge. This study investigates the Dez distributary E1R1 (Iran) with three pool-controlled reaches (Ch1-Ch3) and six turnouts (TO1-TO6) using operator records and Irrigation Conveyance System Simulation (ICSS) scenarios. Objectives are to infer an operator-consistent reward using Maximum Entropy Inverse Reinforcement Learning (MEIRL), derive gate-control policies from the recovered reward, and evaluate performance using indicators. The framework couples MEIRL with an ICSS Saint-Venant simulator, learning from expert state-action demonstrations and testing the policy across scenarios. Unlike canal-control reinforcement learning that prescribes or tunes a reward (e.g., fuzzy SARSA), this approach infers a canal-specific reward from expert behavior, yielding an auditable preference model for policy derivation and validation. Water-level regulation is assessed via maximum and integral absolute error (MAE, IAE), while delivery service is assessed via Molden-Gates indicators: efficiency (MPF), equity (MPE), and dependability (MPD). After learning, MAE falls below 5% (vs. > 20% during learning). Post-learning, Ch1 remains stable (MAE 1.9-4.6%), Ch2 shows residual volatility (peak MAE 14.3%), and Ch3 is precise (MAE <= 1.3% in most scenarios; one at 12.9%); mean IAE values are 1.65% (Ch1), 4.34% (Ch2), and 1.64% (Ch3). MPF remains within 0.85-1.0 (minimum 0.951 at TO3), with improved dispersion (MPE 0.001-0.03; MPD 0.001-0.023). Results provide an auditable pathway for encoding operational preferences into deployable canal policies, motivating persistence-aware rewards, scaling to larger networks, and indicator-based auditing for data-scarce commands.