Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art-Reference-Cited by-同舟云学术

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Artificial intelligence and geo-statistical models for stream-flow forecasting in ungauged stations: state of the art

Published:2017-01-09 Issue:3 Volume:86 Page:1377-1392
ISSN:0921-030X
Container-title:Natural Hazards
language:en
Short-container-title:Nat Hazards

Author:

Valizadeh Nariman,Mirzaei Majid,Allawi Mohammed Falah,Afan Haitham Abdulmohsin,Mohd Nuruol Syuhadaa,Hussain Aini,El-Shafie Ahmed

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology

Link

http://link.springer.com/content/pdf/10.1007/s11069-017-2740-7.pdf

Reference133 articles.

1. Adhikary PP, Chandrasekharan H, Chakraborty D, Kamble K (2010) Assessment of groundwater pollution in West Delhi, India using geostatistical approach. Environ Monit Assess 167:599–615

2. Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Wan Mohtar WHM, Hussain A (2014) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29:1231–1245. doi: 10.1007/s11269-014-0870-1

3. Aghakouchak A, Habib E (2010) Application of a conceptual hydrologic model in teaching hydrologic processes. Int J Eng Educ 26:963

4. Ahmadi SH, Sedghamiz A (2007) Geostatistical analysis of spatial and temporal variations of groundwater level. Environ Monit Assess 129:277–294

5. Ahmadi SH, Sedghamiz A (2008) Application and evaluation of kriging and cokriging methods on groundwater depth mapping. Environ Monit Assess 138:357–368

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