Adaptive Network-based Fuzzy Inference System-Genetic Algorithm Models for Prediction Groundwater Quality Indices: a GIS-based Analysis
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The results show that the ANFis-GA method can present a more parsimonious model with a less number of employed rules compared to ANFIS model and improve the fitness criteria and so model efficiency at the same time.Abstract:
The prediction of groundwater quality is very important for the management of water resources and environmental activities. The present study has integrated a number of methods such as Geographic Information Systems (GIS) and Artificial Intelligence (AI) methodologies to predict groundwater quality in Kerman plain (including HCO3-, concentrations and Electrical Conductivity (EC) of groundwater). This research has investigated the abilities of Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of ANFIS with Genetic Algorithm (GA), and Artificial Neural Network (ANN) techniques as well to predict the groundwater quality. Various combinations of monthly variability, namely rainfall and groundwater levels in the wells were used by two different neuro-fuzzy models (standard ANFIS and ANFIS-GA) and ANN. The results show that the ANFIS-GA method can present a more parsimonious model with a less number of employed rules (about 300% reduction in number of rules) compared to ANFIS model and improve the fitness criteria and so model efficiency at the same time (38.4% in R2 and 44% in MAPE). The study also reveals that groundwater level fluctuations and rainfall contribute as two important factors in predicting indices of groundwater quality.read more
Citations
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References
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Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
An adaptive neuro-fuzzy inference system for bridge risk assessment
Ying-Ming Wang,Taha M. S. Elhag +1 more
TL;DR: An adaptive neuro-fuzzy system (ANFIS) is developed using 506 bridge maintenance projects for bridge risk assessment, which can help Highways Agency to determine the maintenance priority ranking of bridge structures more systematically, more efficiently and more economically.
Journal ArticleDOI
Neural network prediction of nitrate in groundwater of Harran Plain, Turkey
TL;DR: In this paper, an artificial neural network (ANN) model was used to predict the concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain.
Journal ArticleDOI
Application of several artificial intelligence models and ARIMAX model for forecasting drought using the Standardized Precipitation Index
TL;DR: Results indicated that in a 9-months period (as the timescale), the ARIMAX model gives SPI values and forecast drought with more precision than the SVM, ANFIS, and MLP models.