Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study

Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian p...

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Bibliographic Details
Published in:Advances in meteorology 2022-02, Vol.2022, p.1-13
Main Authors: Al Sudani, Zainab Abdulelah, Salem, Golam Saleh Ahmed
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Algorithms
Aquatic resources
Artificial intelligence
Artificial neural networks
Case studies
China
Climate change
Comparative analysis
Comparative studies
Correlation coefficient
Correlation coefficients
Error analysis
Evaporation
Evaporation loss
Evaporation rate
Gaussian process
Humidity
Hydrology
Inspection
Iraq
Irrigation water
Machine learning
Management
Modelling
Monthly
Performance evaluation
Performance prediction
Precipitation
Rain
Statistical analysis
Water
Water requirements
Water resources
Water resources management
Wavelet transforms
Weather
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Summary:Accurately estimating the amount of evaporation loss is necessary for scheduling and calculating irrigation water requirements. In this study, four machine learning (ML) modeling approaches, extreme learning machine (ELM), gradient boosting machine (GBM), quantile random forest (QRF), and Gaussian process regression (GPR), have been developed to estimate the monthly evaporation loss over two stations located in Iraq. Monthly climatical parameters have been used as an input variable for simulating the evaporation rate. Several statistical measures (e.g., mean absolute error (MAE), correlation coefficient (R), mean absolute percentage error (MAPE), and modified index of agreement (Md)), as well as graphical inspection, were used to compare the performances of the applied models. The results showed that the GBM model has much better performance in predicting monthly evaporation over two stations compared to other applied models. For the first case study which was in Diyala, the results showed a prediction enhancement in terms of MAE and RMSE by 7.17%, 21.01%; 16.51%, 15.74%; and 23.14%, 26.64%; using GBM compared to ELM, GPR, and QRF, respectively. However, for the second case study (in Erbil), the prediction enhancement was improved in terms of reduction of MAE and RMSE by 10.88%, 9.24%; 15.24%, 5%; and 16.06%, 15.76%; respectively, compared to ELM, GPR, and QRF models. The results of the proposed GMBM model can therefore assist local stakeholders in the management of water resources.
ISSN:1687-9309
1687-9317
DOI:10.1155/2022/1433835