Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios.
Naser ShiriJalal ShiriZaher Mundher YaseenSungwon KimIl-Moon ChungVahid NouraniMohammad Zounemat-KermaniPublished in: PloS one (2021)
Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.
Keyphrases
- artificial intelligence
- deep learning
- machine learning
- water quality
- climate change
- big data
- heavy metals
- human health
- drinking water
- health risk
- health risk assessment
- risk assessment
- quality improvement
- healthcare
- systematic review
- public health
- molecular dynamics
- high resolution
- organic matter
- wastewater treatment
- clinical evaluation