Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms
water; algorithm; machine learning; Algorithms; Deep Learning; Machine Learning; Neural Networks, Computer; Water
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2023
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my.uniten.dspace-268192023-05-29T17:36:57Z Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. 36612762700 6506146119 57214837520 55807263900 water; algorithm; machine learning; Algorithms; Deep Learning; Machine Learning; Neural Networks, Computer; Water Evaporation is the primary aspect causing water loss in the hydrological cycle; therefore, water loss must be precisely measured. Evaporation is an intricate nonlinear process occurring as a result of several climatic aspects. The purpose of this research is to assess the feasibility of using Random Forest (RF) and two deep learning techniques, namely convolutional neural network (CNN), and deep neural network (DNN) to accurately estimate monthly pan evaporation rates. Month-based weather data gathered from four Malaysian weather stations during the 2000-2019 timeframe was used to train and evaluate the models. Several input attributes (predictor variables) were investigated to select the most suitable variables for machine learning models. Every approach was tested with several models, each with a different set of model aspects and input parameter combinations. The formulated ML approaches were benchmarked against two commonly used empirical methods: Stephens & Stewart and Thornthwaite. Model outcomes were assessed using standard statistical measures to determine their effectiveness in predicting evaporation. The results indicated that the three ML models developed in the study performed better than empirical models and could significantly improve the precision of monthly Ep estimates even with the identical input sets. The performance assessment metrics also show that the formulated CNN approach was acceptable for modelling monthly water loss due to evaporation with a higher degree of accuracy than other ML frameworks explored in this study. In addition, the CNN framework outperformed other AI techniques evaluated for the same areas using identical data inputs. The investigation's findings in relation to the various performance criteria show that the proposed CNN model is capable of capturing the highly non-linearity of evaporation and could be regarded as an effective tool to predict evaporation. � 2022. The Author(s). Final 2023-05-29T09:36:56Z 2023-05-29T09:36:56Z 2022 Article 10.1038/s41598-022-17263-3 2-s2.0-85135246670 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135246670&doi=10.1038%2fs41598-022-17263-3&partnerID=40&md5=af6f09ecb8f259acc531a65ef12de28d https://irepository.uniten.edu.my/handle/123456789/26819 12 1 13132 All Open Access, Gold NLM (Medline) Scopus |
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| description |
water; algorithm; machine learning; Algorithms; Deep Learning; Machine Learning; Neural Networks, Computer; Water |
| author2 |
36612762700 |
| author_facet |
36612762700 Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. |
| format |
Article |
| author |
Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. |
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Abed M. Imteaz M.A. Ahmed A.N. Huang Y.F. Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms |
| author_sort |
Abed M. |
| title |
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms |
| title_short |
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms |
| title_full |
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms |
| title_fullStr |
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms |
| title_full_unstemmed |
Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms |
| title_sort |
modelling monthly pan evaporation utilising random forest and deep learning algorithms |
| publisher |
NLM (Medline) |
| publishDate |
2023 |
| _version_ |
1806423326581587968 |