Faculty of Civil Engineering, University of Tabriz, Tabriz, East Azerbaijan, Iran.
Department of Water Engineering, Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, East Azerbaijan, Iran.
J Environ Manage. 2020 Feb 1;255:109871. doi: 10.1016/j.jenvman.2019.109871. Epub 2019 Dec 3.
Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for mapping the vulnerability to SWI. This AI-based modeling strategy is a two-level learning process, where vulnerability to SWI at Level 1 can be predicted by such models as Artificial Neural Network (ANN), Sugeno Fuzzy Logic (SFL), and Neuro-Fuzzy (NF); and their outputs serve as the input to the model at Level 2, such as Support Vector Machine (SVM). This model is applied to Urmia aquifer, west coast of Lake Urmia, where both are currently declining. The construction of the above four models both at Levels 1 and 2 provide tools for mapping the SWI vulnerability of the study area. Model performances in the paper are studied using RMSE and R metrics, where the models at Level 1 are found to be fit-for-purpose and the SVM at Level 2 is improved particularly with respect to the reduced scale of scatters in the results. Evaluating the result and groundwater samples by Piper diagram confirms the correspondence of SWI status with vulnerability index.
沿海含水层的非计划性地下水开采导致水位下降，进而引发海水入侵（SWI）。本研究基于 GALDIT 方法制定了一种新的建模策略，利用人工智能（AI）模型来绘制海水入侵脆弱性图。这种基于 AI 的建模策略是一个两级学习过程，其中 SWI 脆弱性可以通过人工神经网络（ANN）、Sugeno 模糊逻辑（SFL）和神经模糊（NF）等模型进行预测；它们的输出作为模型在第二级的输入，例如支持向量机（SVM）。该模型应用于乌尔米亚含水层，即乌尔米亚湖西海岸，目前这两个地区都在下降。上述四个模型的构建都为研究区的 SWI 脆弱性提供了工具。本文通过 RMSE 和 R 指标研究了模型性能，结果表明，第一级的模型是合适的，第二级的 SVM 尤其在结果中散射范围缩小方面得到了改进。通过皮珀图对结果和地下水样本进行评估，确认了海水入侵状况与脆弱性指数的对应关系。
