Groundwater DRASTIC vulnerability mapping by unsupervised and supervised techniques using a modelling strategy in two levels

Abstract

Production of defensible modelling tools for aquifer vulnerability mapping under the conditions of sparse data remains topical using the DRASTIC framework. DRASTIC is the acronym for seven data layers using a prescribed scoring system in terms of rates to account for local variations and weights to account for the relative importance of data layers. Also, the term framework signifies the consensual nature of choosing the data layers without any theoretical or empirical basis. Artificial Intelligence (AI) or similar techniques are used to reduce inherent subjectivities through a strategy of unsupervised learning from data by formulating Multiple Frameworks (MF) and supervised learning from Multiple Models (MMs) without searching for any ‘superior’ model. Notably, a framework lacks the procedure for iterative training and testing, as usual in modelling procedures. In this strategy, models are organised in two hierarchical levels: Level 1 comprises the construction of MFs (two frameworks) and/or MMs (two models); Level 2 comprises a model or an algorithm to reuse the outputs from Level 1 through formulating four strategies, including a mixture of MFs/ MMs, in which Support Vector Machine (SVM) is chosen by the paper to run models at Level 2. The supervision process uses target values as a function of observed nitrate-N values. The strategy is applied to the aquifer in the Malekan region, where the past extensive agricultural practices have been transformed into intensive use of fertilisers without protecting water quality in the aquifers. Results show that AIMM/MF lead to significant improvements in delineating high vulnerability areas.