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1
Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not
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Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not

Geoscientific Model Development, 2022-07, Vol.15 (14), p.5481-5487 [Peer Reviewed Journal]

2022. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1991-9603 ;ISSN: 1991-962X ;ISSN: 1991-959X ;EISSN: 1991-9603 ;DOI: 10.5194/gmd-15-5481-2022

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2
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
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Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation

Nature communications, 2022-04, Vol.13 (1), p.2261-2261, Article 2261 [Peer Reviewed Journal]

2022. The Author(s). ;The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2022 ;ISSN: 2041-1723 ;EISSN: 2041-1723 ;DOI: 10.1038/s41467-022-29837-w ;PMID: 35477711

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3
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

PeerJ. Computer science, 2021-07, Vol.7, p.e623-e623, Article e623 [Peer Reviewed Journal]

COPYRIGHT 2021 PeerJ. Ltd. ;2021 Chicco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 Chicco et al. 2021 Chicco et al. ;ISSN: 2376-5992 ;EISSN: 2376-5992 ;DOI: 10.7717/peerj-cs.623 ;PMID: 34307865

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4
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance
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Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance

Climate research, 2005-12, Vol.30 (1), p.79-82 [Peer Reviewed Journal]

Inter-Research 2005 ;ISSN: 0936-577X ;EISSN: 1616-1572 ;DOI: 10.3354/cr030079

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5
A Deep CNN-LSTM Model for Particulate Matter (PM 2.5 ) Forecasting in Smart Cities
A Deep CNN-LSTM Model for Particulate Matter (PM 2.5 ) Forecasting in Smart Cities
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A Deep CNN-LSTM Model for Particulate Matter (PM 2.5 ) Forecasting in Smart Cities

Sensors (Basel, Switzerland), 2018-07, Vol.18 (7), p.2220 [Peer Reviewed Journal]

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2018 by the authors. 2018 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s18072220 ;PMID: 29996546

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6
Process‐Guided Deep Learning Predictions of Lake Water Temperature
Process‐Guided Deep Learning Predictions of Lake Water Temperature
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Process‐Guided Deep Learning Predictions of Lake Water Temperature

Water resources research, 2019-11, Vol.55 (11), p.9173-9190 [Peer Reviewed Journal]

2019. The Authors. ;2019. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0043-1397 ;EISSN: 1944-7973 ;DOI: 10.1029/2019WR024922

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7
ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station
ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station
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ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station

Water Resources Research, 2020-04, Vol.56 (4), p.n/a [Peer Reviewed Journal]

2020. The Authors. ;2020. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Copyright ;ISSN: 0043-1397 ;EISSN: 1944-7973 ;DOI: 10.1029/2019WR026058

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8
Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition
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Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition

IEEE access, 2019, Vol.7, p.158820-158846 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2019.2945545 ;CODEN: IAECCG

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9
Forecast of rainfall distribution based on fixed sliding window long short-term memory
Forecast of rainfall distribution based on fixed sliding window long short-term memory
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Forecast of rainfall distribution based on fixed sliding window long short-term memory

Engineering applications of computational fluid mechanics, 2022-12, Vol.16 (1), p.248-261 [Peer Reviewed Journal]

2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2022 ;2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1994-2060 ;EISSN: 1997-003X ;DOI: 10.1080/19942060.2021.2009374

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10
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
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Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches

Energies (Basel), 2018, Vol.11 (7), p.1636 [Peer Reviewed Journal]

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en11071636

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11
Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm
Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm
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Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Energies (Basel), 2018, Vol.11 (5), p.1067 [Peer Reviewed Journal]

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en11051067

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12
Random Forests for Global and Regional Crop Yield Predictions
Random Forests for Global and Regional Crop Yield Predictions
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Random Forests for Global and Regional Crop Yield Predictions

PloS one, 2016-06, Vol.11 (6), p.e0156571-e0156571 [Peer Reviewed Journal]

COPYRIGHT 2016 Public Library of Science ;COPYRIGHT 2016 Public Library of Science ;This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0156571 ;PMID: 27257967

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13
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration
Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration
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Performance evaluation of hybrid WOA-XGBoost, GWO-XGBoost and BO-XGBoost models to predict blast-induced ground vibration

Engineering with computers, 2022-12, Vol.38 (Suppl 5), p.4145-4162 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021 ;The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021. ;ISSN: 0177-0667 ;EISSN: 1435-5663 ;DOI: 10.1007/s00366-021-01393-9

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14
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Mathematical problems in engineering, 2021, Vol.2021, p.1-15 [Peer Reviewed Journal]

Copyright © 2021 Quang Hung Nguyen et al. ;COPYRIGHT 2021 Hindawi Limited ;Copyright © 2021 Quang Hung Nguyen et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 ;ISSN: 1024-123X ;ISSN: 1563-5147 ;EISSN: 1563-5147 ;DOI: 10.1155/2021/4832864

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15
Comparison of Performance of Data Imputation Methods for Numeric Dataset
Comparison of Performance of Data Imputation Methods for Numeric Dataset
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Comparison of Performance of Data Imputation Methods for Numeric Dataset

Applied artificial intelligence, 2019-08, Vol.33 (10), p.913-933 [Peer Reviewed Journal]

ISSN: 0883-9514 ;EISSN: 1087-6545 ;DOI: 10.1080/08839514.2019.1637138

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16
Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods
Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods
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Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods

Theoretical and applied climatology, 2020-08, Vol.141 (3-4), p.1135-1150 [Peer Reviewed Journal]

Springer-Verlag GmbH Austria, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer-Verlag GmbH Austria, part of Springer Nature 2020. ;ISSN: 0177-798X ;EISSN: 1434-4483 ;DOI: 10.1007/s00704-020-03271-8

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17
Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm
Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm
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Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Materials, 2021-02, Vol.14 (4), p.794 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2021 by the authors. 2021 ;ISSN: 1996-1944 ;EISSN: 1996-1944 ;DOI: 10.3390/ma14040794 ;PMID: 33567526

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18
Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework
Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework
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Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

Sensors (Basel, Switzerland), 2020-03, Vol.20 (5), p.1399 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s20051399 ;PMID: 32143371

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19
A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)
A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)
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A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)

IEEE access, 2020, Vol.8, p.26933-26940 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.2971348 ;CODEN: IAECCG

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20
Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery
Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery
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Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

Monthly weather review, 2019-06, Vol.147 (6), p.2261-2282 [Peer Reviewed Journal]

Copyright American Meteorological Society Jun 2019 ;ISSN: 0027-0644 ;EISSN: 1520-0493 ;DOI: 10.1175/MWR-D-18-0391.1

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