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Journal ArticleDOI

A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation

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TLDR
This algorithm was implemented and applied for predicting, spatially and temporally, the hydraulic head in an area located in Bavaria, Germany and can be characterized as favorable, since the RMSE of the method is in the order of magnitude of 10−2 m.
About
This article is published in Journal of Hydrology.The article was published on 2014-11-27. It has received 74 citations till now. The article focuses on the topics: Variogram & Kriging.

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Citations
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Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh

TL;DR: In this article, the authors investigated the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points using water evaluation indices and a number of statistical approaches.
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A review of the artificial intelligence methods in groundwater level modeling

TL;DR: A review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years and the reviewing and comparison of the obtained results.
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Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach.

TL;DR: The proposed methodology is used to support a hydrological simulation with the HEC-HMS model, applied at the Keramianos basin which is ungauged for SMC.
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Groundwater salinization and associated co-contamination risk increase severe drinking water vulnerabilities in the southwestern coast of Bangladesh.

TL;DR: The positive correlations between EC and trace and toxic elements indicated the potential influence of groundwater salinization on the dissolution of more chemical contaminants in the aquifer, representing possible saltwater intrusion along the coastal aquifer.
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Groundwater level prediction using machine learning models: A comprehensive review

TL;DR: In this article , the authors provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain, as well as recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge.
References
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Book

Fuzzy sets

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book

Neural Networks: A Comprehensive Foundation

Simon Haykin
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations

TL;DR: In this paper, the authors present guidelines for watershed model evaluation based on the review results and project-specific considerations, including single-event simulation, quality and quantity of measured data, model calibration procedure, evaluation time step, and project scope and magnitude.
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Principles of geostatistics

G. Matheron
- 01 Dec 1963 - 
TL;DR: In this article, the authors present a new science leading to such an approach, namely geostatistics, which is a new approach for estimating the estimation of ore grades and reserves.
Journal ArticleDOI

Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation

TL;DR: In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
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