The management improvement of irrigation networks is inevitable because of limited water resources and competition in various sectors of industry, agriculture, environment, and urban water consumption. Application of appropriate water delivery scheduling is one of the methods to improve irrigation management. Several delivery methods are practiced in irrigation networks according to water availability, type of network control system, and exiting capacities. One promising delivery method is the on-request system, which could be applied manually in irrigation networks. High performance of delivery methods arise from the determination of appropriate operation instructions. In previous studies, the Fuzzy SARSA learning (FSL) method was applied to determine operation instruction for on-request delivery in a canal of the Aghili irrigation network located in southwest Iran (SARSA is the definition of state, action, reward, state, action). In this work, the conventional manual operation on the same canal ...

Recently, a continuous reinforcement learning model called fuzzy SARSA (state, action, reward, state, action) learning (FSL) was proposed for irrigation canals. The main problem related to FSL is its convergence and generalization in environments with many variables such as large irrigation canals and situations beyond training. Furthermore, due to its architecture, FSL may require high computation demands during its learning process. To deal with these issues, this work proposes a computationally lighter generalizing learned Q-function (GLQ) model, which benefits from the FSL-learned Q-function, to provide operators with a faster and simpler mechanism to obtain operational instructions. The proposed approach is tested for different water requests in the East Aghili Canal, located in the southwest of Iran. Several performance indicators are used for evaluating the GLQ model results, showing convergence in all the investigated cases and the ability to estimate operational instructions (actions) in situations beyond training, delivering water with high accuracy regarding several performance indicators. Hence, the use of the GLQ model is recommended for determining the operational patterns in irrigation canals.

Although various optimization algorithms have been developed for operational purposes, the development of new optimization algorithms is still an open problem due to the complex system of irrigation canals that must be addressed. Recently, a new algorithm named gray wolf optimization (GWO) has been introduced and applied in different contexts. It mimics the social hierarchy and hunting behavior of gray wolves in nature. In this research, GWO was formulated, developed, and linked to irrigation canal system simulation (ICSS) to schedule water delivery. A fitness (optimization) function was defined according to the standard water delivery performance indicators. Normal and water shortage operational scenarios in the E1R1 Dez canal in Iran were tested and evaluated. The results revealed that GWO is a powerful optimization method and avoids local optimal points when normal conditions exist. However, it has relatively poor performance in water shortage conditions in which there is not enough water. Water depth variations remain inside acceptable margins. Its results were comparable to fuzzy state, action, reward, state, action (SARSA) learning (FSL) in the same canal, showing a value of maximum absolute error (MAE) and integral absolute error (IAE) of 10.7% and 9.2%, respectively, and it can distribute water between turnouts adequately, efficiently, equitably, and dependably in normal conditions.