Random Forest and Multilayer Perceptron hybrid models integrated with the genetic algorithm for predicting pan evaporation of target site using a limited set of neighboring reference station data-Reference-Cited by-同舟云学术

Random Forest and Multilayer Perceptron hybrid models integrated with the genetic algorithm for predicting pan evaporation of target site using a limited set of neighboring reference station data

Published:2024-01-30 Issue:2 Volume:17 Page:1261-1280
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Shadkani Sadra,Hashemi Sajjad,Pak Amirreza,Lahijan Alireza Barzgari

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12145-024-01237-2.pdf

Reference64 articles.

1. Abghari H, Ahmadi H, Besharat S, Rezaverdinejad V (2012) Prediction of daily pan evaporation using wavelet neural networks. Water Resour Manage 26(12):3639–3652

2. Adnan MN, Islam MZ (2016) Optimizing the number of trees in a decision forest to discover a subforest with high ensemble accuracy using a genetic algorithm. Knowl - Based Syst 110:86–97

3. Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12:606

4. Alipour A, Yarahmadi J, Mahdavi M (2014) Comparative study of M5 model tree and artificial neural network in estimating reference evapotranspiration using MODIS products. J Climatol 2014:1-11

5. Allawi MF, El-Shafie A (2016) Utilizing RBF-NN and ANFIS methods for multi-lead ahead prediction model of evaporation from reservoir. Water Resour Manag 30(13):4773–4788

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