Improving the performance of daily pan evaporation (Evp) prediction using the ensemble empirical mode decomposition combined with deep learning models
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new method for predicting daily pan evaporation (Evp) using optimal input combinations and advanced AI models. The approach enhances water resource management, especially in arid regions.
Area Of Science
- Hydrology and Water Resources
- Artificial Intelligence in Environmental Science
- Climate Change Impact Assessment
Background
- Pan evaporation (Evp) is a critical factor in water resource management, particularly in arid and semi-arid regions.
- Accurate Evp prediction is essential for efficient water allocation and minimizing water loss.
- Traditional Evp prediction models often struggle with the complex, non-linear dynamics of meteorological data.
Purpose Of The Study
- To develop and evaluate a novel approach for daily pan evaporation (Evp) prediction.
- To identify an optimal combination of input variables for Evp modeling.
- To enhance the accuracy of Evp prediction using ensemble empirical mode decomposition (EEMD) with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models.
Main Methods
- Input variable selection was performed using the Gamma Test and Genetic Algorithm (GTGA).
- Ensemble Empirical Mode Decomposition (EEMD) was employed to decompose input data (temperature, precipitation, past Evp) into Intrinsic Mode Functions (IMFs).
- LSTM and CNN models were utilized with the decomposed IMFs as inputs for Evp prediction.
Main Results
- The CNN model achieved Root Mean Square Error (RMSE) of 0.33 mm, Mean Absolute Error (MAE) of 0.24 mm, and a Skill Index (SI) of 0.06.
- The LSTM model demonstrated superior performance with RMSE of 0.043 mm, MAE of 0.11 mm, and SI of 0.016.
- Decomposition into IMFs simplified data patterns, significantly improving the performance of both CNN and LSTM models.
Conclusions
- The proposed GTGA-EEMD-LSTM/CNN methodology offers a robust and accurate approach for daily pan evaporation prediction.
- This method provides valuable insights for water resource managers, aiding in water conservation strategies, especially in water-scarce environments.
- The approach is adaptable for Evp prediction in diverse geographical regions, contributing to better climate change adaptation.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...