Study of water resources parameters using artificial intelligence techniques and learning algorithms: a survey

نویسندگان

چکیده

Abstract Qualitative analysis of water resources is one the most widely used topics in research today. Researchers use various methods parameters to achieve desired goals this field. This uses artificial intelligence (AI), learning machine (LM), data mining, and mathematical techniques simulate behavior estimate its parametric changes. The proposed model study was a Self-adaptive Extreme (SAELM) hydrogeological Meghan wetland located Markazi province Iran. In addition, SAELM simulation results were compared Least square support vector (LSSVM), Multiple linear regression (MLR), Adaptive Neuro-fuzzy inference system (ANFIS) models. simulated Electrical Conductivity (EC), Total Dissolved Solids (TDS), Groundwater Level (GWL), salinity. information related sampling for 175 months area. Finally, after operation, four models introduced as superior Mentioned exceptional GWL modeling, modeling EC, MLR salinity simulation, LSSVM TDS parameters. Moreover, by five approaches, models' performance evaluated. Suggested strategies evaluation statistical indicators, Wilson score method uncertainty (WSMUA), response & correlation plots, discrepancy ratio charts, distribution error diagrams. Based on accurate with RMSE, MAPE, R 2 indices equal 0.1496, 0.0043, 0.9933, respectively. ANFIS had worst simulation.

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ژورنال

عنوان ژورنال: Applied Water Science

سال: 2022

ISSN: ['2190-5495', '2190-5487']

DOI: https://doi.org/10.1007/s13201-022-01675-7