An Ecological Expert System Optimization for Assessing Environmental Water Requirements of Hypersaline Lakes

The present study proposes an applicable methodology to optimize environmental water requirement of hypersaline lakes with a focus on Urmia lake as the case study in which remote sensing analysis, machine learning model and fuzzy expert system are linked. A machine learning model was developed to simulate effective abiotic parameters in which bands of operational land imager (Landsat 8) were inputs and depth and total dissolved solids were the outputs of the model. Moreover, an ecological expert system using Mamadani fuzzy inference system was developed to generate the habitat suitability map for the selected target species. Then, a multivariate linear model was developed to assess unit habitat suitability in which water level and total inflow of the lake were the variables of the model. An optimization model was developed to assess environmental water requirement in which habitat suitability between natural and regulated flows and water supply loss was minimized. The multivariate linear model was applied to assess habitat suitability in the optimization model. Based on the results in the case study, the proposed combined model is able to balance the ecological requirements and water demand by allocating 60% and 40% of total inflow to environmental water requirement and water demand respectively. Average habitat loss proposed by the optimal environmental water requirement was less than 20% which implies the robustness of the model. Generating habitat suitability maps of the lake by a reliable method which is used in the environmental flow optimization might be the significance of the proposed method.