Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM). (1st February 2020)

• Record Type: Journal Article
• Authors: Moazamnia, Marjan; Hassanzadeh, Yousef; Nadiri, Ata Allah; Sadeghfam, Sina
• Is Part Of: Journal of environmental management. Volume 255(2020)
• Publication Date: 2020-02-01
• Subjects: Saltwater intrusion -- Vulnerability mapping -- Artificial intelligence models -- Two-level learning -- Lake urmia; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2019.109871
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: 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 2 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. Graphical abstract: Image 10259 Highlights: GALDIT maps vulnerability to saltwater intrusion in coastal aquifers. The paper formulates Artificial Intelligence Multiple Model (AIMM) based on GALDIT. AIMM is a two-level learning process and reduces inherent subjectivity in GALDIT. At Levels 1 and 2, vulnerability are mapped by ANN/SFL/NF and SFL respectively. Results gains an insight into manage hotspots in the study area with sparse data.