Geology Reference
In-Depth Information
29. Han D, Chan L, Zhu N et al (2007) Flood forecasting using support vector machines.
J Hydroinf 9(4):267
-
276
30. Han H, Felker P (1997) Estimation of daily soil water evaporation using an arti
cial neural
network. J Arid Environ 37:251
-
260
31. Han D, Kwong T, Li S et al (2007) Uncertainties in real-time flood forecasting with neural
networks. Hydrol Process 21:223
228
32. Han D, Cluckie ID, Karbassioun D, Lawry J, Krauskopf B et al. (2002) River flow modelling
using fuzzy decision trees, Water Resour Manage 16(6):431
-
445
33. Hecht-Nielsen R (1990) Neurocomputing. Addison-Wesley, Reading, MA
34. Hochreiter S, Schmidhuber
-
J
(1997) Long short-term memory. Neural Comput 9
1780
35. Hop
eld JJ (1982) Neural networks and physical systems with emergent collective
computational abilities. Proc Natl Acad Sci USA 79:2554
(8):1735
-
2558
36. Horikawa S, Furuhashi T, Uchikawa Y et al (1992) On fuzzy modeling using fuzzy networks
with back-propagation algorithm. IEEE Trans Neural Networks 3(5):801
-
806
37. Hossein T, Ozgur K, Azadeh E, Hosseinzadeh PTA et al (2012) SVM, ANFIS, regression
and climate based models for reference evapotranspiration modelling using limited climatic
data in a semi-arid highland environment. J Hydrol 444
-
445:78
-
89
38. Jain A, Sudheer KP, Srinivasulu S (2004) Identi
cation of physical processes inherent in
arti
cial neural network rainfall runoff models. Hydrol Process 18:571
-
581
39. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Sys
Manage Cybernetics 23(3):665
-
685
40. Jian YL, Chun TC, Ch Kwok W et al (2006) Using support vector machines for long term
discharge prediction. Hydrol Sci J 51(4):599
-
612. doi:
10.1623/hysj.51.4.599
41. Jones AJ (2004) New tools in non-linear modelling and prediction. Comput Manage Sci
1:109
-
149. doi:
10.1007/s10287-003-0006-1
42. Jonsdottir H, Aa Nielsen H, Madsen H, Eliasson J, Palsson OP, Nielsen MK et al. (2007)
Conditional parametric models for storm sewer runoff. Water Res Res 43:1
-
9
43. Jordan MI (1986) Serial order: A parallel distributed processing approach. Institute for
Cognitive Science Report 8604. UC, San Diego
44. Karamouz M, Razavi S, Araghinejad S et al (2008) Long-lead seasonal rainfall forecasting
using time-delay recurrent neural networks: a case study. Hydrol Process 22:229
-
241
45. Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan
evaporation estimation in western Turkey. Hydrol Sci J 49(6):1001
-
1010
46. Keskin ME, TerziO (2006) Arti
cial neural network models of daily pan evaporation.
47. Kishor N, Singh SP (2007) Nonlinear predictive control for a NNARX hydro plant model.
Neural Comput Appli 16(2):101
-
108
48. Kisi O (2006) Evapotranspiration estimation using feed-forward neural networks. Nord
Hydrol 37(3):247
-
260
49. Kisi O (2008) Stream flow forecasting using neuro-wavelet
-
technique. Hydrol Process
4152
50. Kosko B (1992) Fuzzy systems as universal approximators. In: Proceedings of the IEEE
international conference on fuzzy systems, pp 1153
22:4142
-
1162
-
51. Koutsoyiannis D (2007) Discussion of
“
Generalized regression neural networks for
835
52. Kumar M, Raghuwanshi NS, Singh R, Wallender W, Pruitt WO et al (2002) Estimating
evapotranspiration using arti
cial neural network. J Irrig Drain Eng 128(4):224
evapotranspiration modelling
”
by O. Kişi
i (2006). Hydrol Sci J 52(4):832
ş
-
233
53. Labat D (2005) Recent advances in wavelet analyses: part 1 a review of concepts. J Hydrol
314:275
-
288
54. Lafreni`ere M, Sharp M (2003) Wavelet analysis of inter-annual variability in the runoff
regimes of glacial and nival stream catchments, Bow Lake, Alberta. Hydrol Process
17:1093
-
1118
-
