Veröffentlichungen

Schmitz, G.H., Puhlmann, H. and Dröge, W. (2005): Artificial neural networks for estimating soil hydraulic parameters from dynamic flow experiments. European Journal of Soil Science 56 (1), 19-30.
Schütze, N., Droege, W., Petersohn, U., & Schmitz, G.H. (2005): A representation of the Richards equation and its inverse solution by artificial neural networks: a comparison of three different network architectures. accepted by Water Resources Research.

Puhlmann, H., Dröge,W., Schmitz, G. H., Morgenstern, Y. (2004): Artifical neural networks for estimating soil-hydraulic parameters from evaporation experiments, Eurosoil 2004, Freiburg Germany 2004.
Puhlmann, H., Dröge, W., Schmitz, G. H. (2003): Artifical Neural Networks for identifying soil-hydraulic parameters from dynamic flow experiments, Thurnau 2003.
Puhlmann, H., Dröge, W., Schmitz, G. H., Lennartz, F. (2003): Determination of soil-hydraulic properties from multi-step outflow experiments: a comparison between the inverse approach and an artificial neural network Artificial neural networks for estimating soil-hydraulic parameters from evaporation experiments, 28th International Hydrology and Water Resources Symposium, Wollongong 2003.
Schmitz, G.H., Puhlmann, H. and Dröge, W. (2002): Evaluation of soil-hydraulic properties: Artificial Neural Networks as an efficient alternative to inverse modeling techniques, in Water Resources and Environment Research, Volume I: Modeling water resources phenomena / Water resources management. Proceedings of Third International Conference on Water Resources and Environment Research. Dresden, Germany, 202-208.

Schmitz, G. H., Puhlmann, H., Dröge,W., Lennartz, F. (2002): Estimating soil-hydraulic parameters from dynamic flow experiments with artifical neural networks Hannover 2002.

Zu dieser Seite