Groundwater quality modeling: On the analogy between integrative PSO and MRFO mathematical and machine learning models

[1]  Mohammad Zounemat-Kermani,et al.  Hybrid meta-heuristics artificial intelligence models in simulating discharge passing the piano key weirs , 2019, Journal of Hydrology.

[2]  Lutz Breuer,et al.  Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning. , 2019, The Science of the total environment.

[3]  Michael N. Fienen,et al.  A statistical learning framework for groundwater nitrate models of the Central Valley, California, USA , 2015 .

[4]  Jan Adamowski,et al.  Comparison of machine learning models for predicting fluoride contamination in groundwater , 2017, Stochastic Environmental Research and Risk Assessment.

[5]  K. Madani,et al.  Quantifying Anthropogenic Stress on Groundwater Resources , 2017, Scientific Reports.

[6]  C. Mallows.  Some Comments on Cp , 2000, Technometrics.

[7]  O. Batelaan,et al.  Regional groundwater discharge: phreatophyte mapping, groundwater modelling and impact analysis of land-use change , 2003 .

[8]  Abhijit Mukherjee,et al.  Groundwater quality and depletion in the Indo-Gangetic Basin mapped from in situ observations , 2016 .

[9]  B. Pradhan,et al.  A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination. , 2018, The Science of the total environment.

[10]  G. Ferguson,et al.  Global Groundwater Sustainability, Resources, and Systems in the Anthropocene , 2020, Annual Review of Earth and Planetary Sciences.

[11]  Reza Kerachian,et al.  Characterizing an unknown pollution source in groundwater resources systems using PSVM and PNN , 2010, Expert Syst. Appl..

[12]  Paraskevas Tsangaratos,et al.  Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models , 2020, Applied Sciences.

[13]  Y. Hamed,et al.  Climate impact on surface and groundwater in North Africa: a global synthesis of findings and recommendations , 2018, Euro-Mediterranean Journal for Environmental Integration.

[14]  Frederic Coulon,et al.  Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods. , 2019, The Science of the total environment.

[15]  Mohammad Zounemat-Kermani,et al.  Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms , 2020 .

[16]  Colin L. Mallows,et al.  Some Comments on Cp , 2000, Technometrics.

[17]  Ozgur Kisi,et al.  Assessment of Artificial Intelligence–Based Models and Metaheuristic Algorithms in Modeling Evaporation , 2019, Journal of Hydrologic Engineering.

[18]  Amir Jalalkamali,et al.  Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters , 2015, Earth Science Informatics.

[19]  C. Moeck,et al.  A review of threats to groundwater quality in the anthropocene. , 2019, The Science of the total environment.

[20]  L. A. Desimone,et al.  Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA , 2020 .

[21]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[22]  K. Cho,et al.  Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries , 2016 .

[23]  Fereydoun Radmanesh,et al.  Predicting sea wave height using Symbiotic Organisms Search (SOS) algorithm , 2018, Ocean Engineering.

[24]  Jui-Sheng Chou,et al.  Machine learning in concrete strength simulations: Multi-nation data analytics , 2014 .

[25]  Dietmar Stephan,et al.  Ensemble data mining modeling in corrosion of concrete sewer: A comparative study of network-based (MLPNN & RBFNN) and tree-based (RF, CHAID, & CART) models , 2020, Adv. Eng. Informatics.

[26]  M. Bierkens,et al.  Global depletion of groundwater resources , 2010 .

[27]  Rahim Barzegar,et al.  Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms. , 2018, The Science of the total environment.

[28]  Madan K. Jha,et al.  Efficacy of neural network and genetic algorithm techniques in simulating spatio‐temporal fluctuations of groundwater , 2015 .

[29]  J. Famiglietti.  The global groundwater crisis , 2014 .

[30]  Dong Liu,et al.  ELM evaluation model of regional groundwater quality based on the crow search algorithm , 2017 .

[31]  Liying Wang,et al.  Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications , 2020, Eng. Appl. Artif. Intell..

[32]  Ismail Yusoff,et al.  Application of the Artificial Neural Network and Neuro‐fuzzy System for Assessment of Groundwater Quality , 2015 .

[33]  C. Willmott.  ON THE VALIDATION OF MODELS , 1981 .