Geology Reference
In-Depth Information
2.7 Conclusions
This chapter summarizes some of the data modeling issues where one can
nd
major over-simpli
ed assumptions and unsolved issues. It covers the relatively
simple and neglected topics of training data length, data redundancy, and
assumptions in neuron selection in ANN modeling.
References
1. Abbott MB (1991) Hydroinformatics: information technology and the aquatic environment.
Ashgate, Aldershot
2. Abbott MB, Vojinovic Z (2013) Towards a hydroinformatics praxis in the service of social
justice. J Hydroinform (in press) doi: 10.2166/hydro.2013
3. Abbott MB (1996) The sociotechnical dimensions of hydroinformatics. In: Proceedings of the
second international conference on hydroinformatics, Balkema, Rotterdam, pp 3 - 18
4. Abrahart RJ, See L, Kneale PE (1999) Using pruning algorithms and genetic algorithms to
optimise network architectures and forecasting inputs in a neural network rainfall-runoff
model. J Hydroinform 1(2):103 - 114
5. Abrahart RJ, See L, Kneale PE et al (2001) Investigating the role of saliency analysis with a
neural network rainfall-runoff model. Comput Geosci 27:921 - 928
6. Abrahart R, See L, Dawson C (2008) Neural network hydroinformatics: maintaining scientific
rigour. In: Abrahart R, See L, Solomatine D (eds) Practical hydroinformatics. Computational
intelligence and technological develop-ments in water applications. Springer-Verlag, Berlin,
Heidelberg, Germany, pp 33
47
7. Akaike H (1970) Statistical predictor identification. Ann Inst Statist Math 22:203
217
8. Anellopoulos I, Wilkinson G (1997) Strategies and best practice for neural networkimage
classification. Int J Remote Sens 18:711
725
9. ASCE Task Committee on Application of Arti cial Neural Networks in Hydrology (2000)
Arti cial neural networks in hydrology-I: preliminary concepts. J Hydraul Eng ASCE 5(2):
115
-
123
10. Avci E (2007) Forecasting daily and sessional returns of the ISE-100 index with neural
network models. Do
-
142
11. Barnes CJ (1995) Commentary: the art of catchment modelling: what is a good model?
Environ Int 21(5):747 - 751
12. Beck M (1987) Water quality modelling: a review of the analysis of uncertainty. Water Resour
Res 23:1393 - 1442
13. Berry MJ, Linoff G (1997) Data mining techniques: for marketing, sales, and customer
support. Wiley, New York
14. Beven K, Binley A (1992) The future of distributed models
ğ
u
ş Ü
niversitesi Dergisi 8(2):128
-
model calibration and
298
15. Beven KJ (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv
Water Resour 16:41
uncertainty prediction. Hydrol Process 6(3):279
-
51
16. Beven KJ (2001) How far can we go in distributed hydrological modelling? Hydrol Earth Syst
Sci 5(1):1
12
17. Blum A (1992) Neural networks in C ++. Wiley, NY, p 60
18. Boger Z, Guterman H (1997) Knowledge extraction from arti cial neural network models. In:
IEEE Systems, Man, and Cybernetics Conference, Orlando, FL
 
Search WWH ::




Custom Search