Ecological-environmental quality was evaluated for Tabriz and Rasht cities (in Iran) with different climate conditions using artificial intelligence (AI) and remote sensing (RS) techniques. Sampling sites were surveyed and ecological experts assigned eco-environment background values (EBVs) of sites. Then, eco-environmental attributes were extracted as RS derived, and meteorological attributes were observed. Three AI-based models, artificial neural network (ANN), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) were then applied to learn the relationship between a target set of known EBVs and eco-environmental attributes as inputs. According to the results of the single models, none of the models could evaluate EBV appropriately for all regions and classes. Thereafter, three combining techniques were applied to the outputs of single models to enhance spatial evaluation of EBV. It was observed that the modeling for Tabriz led to more accurate results. It seems that the better network performance for Tabriz may be due to a more heterogeneous dataset in this kind of climate. Furthermore, results indicated that SVR led to better performance than both ANN and ANFIS models, but the models' combining techniques were shown to be superior. Combining techniques enhanced performance of single AI modeling up to 26% in the verification step.

使用人工智能（AI）和遥感（RS）技术对大不里士和拉什特（伊朗）不同气候条件的城市的生态环境质量进行了评估。对抽样地点进行了调查，生态专家分配了地点的生态环境本底值（EBV）。然后，从RS提取生态环境属性，并观察气象属性。然后应用了三个基于AI的模型，人工神经网络（ANN），支持向量回归（SVR）和自适应神经模糊推理系统（ANFIS），以了解已知EBV的目标集与生态环境属性之间的关系，例如输入。根据单个模型的结果，没有一个模型可以针对所有区域和类别正确评估EBV。之后，将三种组合技术应用于单个模型的输出，以增强EBV的空间评估。据观察，大不里士的建模导致更准确的结果。大不里士（Tabriz）更好的网络性能似乎可能是由于这种气候下数据集的异构性更高。此外，结果表明，SVR比ANN和ANFIS模型都具有更好的性能，但是模型的组合技术被证明是更好的。在验证步骤中，组合技术将单个AI建模的性能提高了26％。结果表明，SVR比ANN和ANFIS模型都具有更好的性能，但是模型的组合技术被证明是更好的。在验证步骤中，组合技术将单个AI建模的性能提高了26％。结果表明，SVR比ANN和ANFIS模型都具有更好的性能，但是模型的组合技术被证明是更好的。在验证步骤中，组合技术将单个AI建模的性能提高了26％。