Controllare lo stato dell'ordine

Entra a far parte di una comunità di amanti dei libri di tutto il mondo e ottieni numerosi vantaggi. Crea un account gratuito

Punto Poste 5.49 € Punto Poste 5.49 € Corriere DHL 6.99 € Corriere GLS 5.99 € Punto GLS 4.49 € Corriere Bartolini 4.49 € Punto Bartolini 3.49 €

Contatto

Come acquistare

Aiuto

Il mio account

▸ Vuoto :-(

Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques

Name: Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques
Brand: Taylor & Francis Ltd
SKU: 17870028
Availability: OutOfStock
Author: Durga Lal Shrestha
ISBN: 9781138424098

Durga Lal Shrestha

Lingua

Inglese

Libro Rigido

Codice Libristo: 17870028

Casa editrice Taylor & Francis Ltd, luglio 2017

This book describes the use of machine learning techniques to build predictive models of uncertainty... Descrizione completa

Codice Libristo: 17870028

I clienti hanno acquistato anche

Kopalnia soli "Wieliczka". Od dziś do miocenu Kajetan d'Obyrn

In brossura

4.19 €

Német felsőfokú írásbeli gyakorlókönyv - Új helyesírással Péntek Bernadett;Hatos Hajnal

In brossura

14.99 €

Anforderungen an die Aufbereitung Digitalen Contents fur Ultra-Mobile Gerate Martin Stenger

In brossura

57.49 €

Pitirre No Quiere Hablar Ingles Frank Alvarado Madrigal

Rigido

25.09 €

Als Michel versuchte, Linas Backenzahn zu ziehen Georg Riedel

CD audio

10.19 €

Orchestrální studie pro klarinet 1. díl Milan Etlík

In brossura

5.79 €

Die Lieder der Gräser Volker Friebel

In brossura

9.49 €

Mission: Impossible, The 6-Movie Collection, 6 Blu-rays Tom Cruise

Blu-ray

62.29 €

This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models, focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological models performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.