Assessing climate and human activity effects on lake characteristics using spatio-temporal satellite data and an emotional neural network

Different sensing methods provide valuable information for comprehensive monitoring strategies, which are crucial the ecological management of lakes and watersheds. Subsequently, resulting spatio-temporal can be considered fundamental knowledge water resources Lake Urmia is deemed one most important aquatic habitats in Iran. It has been experiencing significant changes during recent years due to climate change, anthropogenic activities, a lack coherent approaches. Hence, awareness hydro-ecological factors last few decades critical identifying problems. In this research, impacts key parameters such as precipitation, evapotranspiration, surface temperatures, suspended sediment concentration, saline features, vegetation explored using satellite imagery. The primary purpose study evaluate crisis concerning human-involved agriculture sector construction causeway. regard, limbic-based Emotional Artificial Neural Network (EANN) developed non-linear universal mapping implemented first time demonstrate interactions between sensitivity two indicators lake health. Providing analysis another objective detect onset deterioration parameters. values efficiency criteria were measured EANN models related inputs. results model scenario 4 with runoff input variables led higher performance best criteria, including DC = 0.868 RMSE 0.096. quantitative confirm that combination both factors, agricultural sector's overdraft, leads efficient and, consequently, leading cause crisis.