𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Model trees as an alternative to neural networks in rainfall—runoff modelling

✍ Scribed by SOLOMATINE, DIMITRI P.; DULAL, KHADA N.

Book ID
111873183
Publisher
Taylor and Francis Group
Year
2003
Tongue
English
Weight
286 KB
Volume
48
Category
Article
ISSN
0262-6667

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Identification of physical processes inh
Identification of physical processes inherent in artificial neural network rainfall runoff models
✍ Ashu Jain; K. P. Sudheer; Sanaga Srinivasulu 📂 Article 📅 2004 🏛 John Wiley and Sons 🌐 English ⚖ 286 KB

## Abstract The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes

A systematic approach to the input deter
A systematic approach to the input determination for neural network rainfall–runoff models
✍ Gwo-Fong Lin; Guo-Rong Chen 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 289 KB

## Abstract Input determination has a great influence on the performance of artificial neural network (ANN) rainfall–runoff models. To improve the performance of ANN models, a systematic approach to the input determination for ANN models is proposed. In the proposed approach, the irrelevant inputs