Information‐Based Machine Learning for Tracer Signature Prediction in Karstic Environments-Reference-Cited by-同舟云学术

Information‐Based Machine Learning for Tracer Signature Prediction in Karstic Environments

Published:2020-02 Issue:2 Volume:56 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Mewes B.¹,Oppel H.¹²,Marx V.²,Hartmann A.²

Affiliation:

1. Institute of Hydrology, Water Resources and Environmental Engineering Ruhr‐University Bochum Bochum Germany

2. Chair of Hydrological Modeling and Water Resources Albert‐Ludwigs‐University of Freiburg Freiburg Germany

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR024558

Reference67 articles.

1. Daily Mean Streamflow Prediction in Perennial and Non-Perennial Rivers Using Four Data Driven Techniques

2. Recharge processes in karstic systems investigated through the correlation of chemical and isotopic composition of rain and spring-waters

3. Bailly‐Comte V. Ladouche B. Allanic C. Bitri A. Moiroux F. Monod B. Vigouroux P. &Maréchal J. C.(2018).Evaluation des ressources en eaux souterraines du Plateau de Sault ‐ Amélioration des connaisances sur les potentialiés de la ressource et cartographie de la vulnérabilité. Rapport final. BRGM/PR‐67528‐FR.

4. BDLisa. (2019). Retrieved fromhttps://bdlisa.eaufrance.fr

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