Predictive models for forecasting hourly urban water demand

By Manuel Herrera, Luís Torgo, Joaquín Izquierdo, Rafael Pérez-García. 2010

426 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b6e42df7dc8bf15d178606de0112ecdf9cfe16a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Manuel Sanchez-Castillo
2. Mario Chica-Olmo
3. Victor Francisco Rodriguez-Galiano
4. Mario Chica-Rivas
5. Quanlong Feng
6. Muhammad Waseem Ahmad
7. Abdulhakim M. Abdi
8. Jiantao Liu

Citations in the corpus

• Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, background): “Algorithms based on DT are easy to apply, as fewer number of parameters need be estimated; hence, these have high degrees of automation (Bater and Coops, 2009; Herrera et al., 2010).”