Comparative evaluation of deep learning and machine learning in modelling pan evaporation using limited inputs-Reference-Cited by-同舟云学术

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Comparative evaluation of deep learning and machine learning in modelling pan evaporation using limited inputs

Published:2022-06-20 Issue:9 Volume:67 Page:1309-1327
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Kisi Ozgur¹²^ORCID,Mirboluki Amin³,Naganna Sujay Raghavendra⁴^ORCID,Malik Anurag⁵,Kuriqi Alban⁶,Mehraein Mojtaba⁷

Affiliation:

1. Department of Civil Engineering, University of Applied Sciences, Lübeck, Germany

2. Department of Civil Engineering, Ilia State University, Tbilisi, Georgia

3. Department of Civil Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

4. Department of Civil Engineering, Siddaganga Institute of Technology, Tumakuru, India

5. Punjab Agricultural University, Regional Research Station, Bathinda, India

6. Department of Civil Engineering, Architecture and Georesources, CERIS, Instituto Superior Tecnico, Universidade de Lisboa, Portugal

7. Department of Civil Engineering, Faculty of Engineering, Kharazmi University, - Tehran Iran

Funder

associated with the work

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2022.2063724

Reference82 articles.

1. Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada

2. Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

3. Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs

4. Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

5. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction

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1. Evaluation of CatBoost Method for Predicting Weekly Pan Evaporation in Subtropical and Sub-Humid Regions;Pure and Applied Geophysics;2024-02-06

2. Boruta extra tree-bidirectional long short-term memory model development for Pan evaporation forecasting: Investigation of arid climate condition;Alexandria Engineering Journal;2024-01

3. A new strategy for prediction of water qualitative and quantitative parameters by deep learning-based models with determination of modelling uncertainties;Hydrological Sciences Journal;2023-12-19

4. Improving the accuracy of air relative humidity prediction using hybrid machine learning based on empirical mode decomposition: a comparative study;Environmental Science and Pollution Research;2023-04-12

5. An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation;Journal of Environmental Management;2023-02

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