Self-Attention Mechanism-Long Short Term Memory Neural Network Models for Irrigation Demand Prediction: An Evaluation and Performance Analysis

Abstract

Introduction

Predicting irrigation demand provides valuable information for agricultural planning and decision-making. By accurately predicting irrigation needs, farmers can optimize water distribution and avoid wasting water. Farmers can use this information to set planting schedules, crop rotation, and optimize land use based on water availability. this study introduces a new model for predicting irrigation demand.This study intrudes a new model for predicting irrigation demand. The self-attention-mechanism (SA) is coupled with the long short term memory (LSTM) neural network to predicting irrigation demand. SALSTM incorporates self-attention mechanisms, which enable the model to focus on the most relevant parts of the input sequence while making predictions. The attention mechanism allows SALSTM to assign different weights to different time steps or features, emphasizing the most informative ones for predicting irrigation demands. SALSTM can capture complex non-linear relationships between different input features, such as meteorological data, soil conditions, and crop characteristics.

Predicting irrigation demand provides valuable information for agricultural planning and decision-making.

Materials and Methods

By combining the power of LSTM and attention mechanisms, SALSTM can learn intricate patterns and interactions between these factors, enabling it to make more accurate predictions of irrigation demands. This ability is particularly beneficial in capturing the nuanced relationships that exist in agricultural systems. Relative humidity, temperature, wind speed, rainfall, and potential crop evapotranspiration were used as the inputs to the models. The SALSTM model was benchmarked against the LSTM, recurrent neural network (RNN), Radial Basis Function Neural Network (RBFN), and multiple linear regression (MLR) models. The study also evaluates and compares the performance of SALSTM models for irrigation demand prediction in multiple programming languages, including Python, MATLAB, R, and JavaScript.

Accurate prediction of irrigation demand is crucial for efficient water management in agriculture and can contribute to sustainable engineering practices. Overall, the study contributes to advanced engineering informatics by providing a comparative analysis of SALSTM models, incorporating self-attention mechanisms, and exploring their application in irrigation demand prediction. The study combines concepts from various disciplines, including data science, machine learning, and irrigation engineering. By applying advanced informatics techniques to irrigation demand prediction, the study bridges the gap between these areas and encourages interdisciplinary collaboration

Results and Discussion

In this research, the self-attention mechanism was integrated with the LSTM model to forecast irrigation demands. The SALSTM model leverages self-attention mechanisms, allowing it to concentrate on the most pertinent segments of the input sequence during predictions. This attention mechanism enables SALSTM to allocate varying weights to different time steps or features, highlighting the most significant ones for predicting irrigation needs. The findings showed that the SALSTM model surpassed the performance of other models.

By comparing the performance of SALSTM models implemented in Python, MATLAB, R, and JavaScript, the study provides insights into the advantages and drawbacks of different programming languages for implementing machine learning models in climate studies. This knowledge can aid researchers and practitioners in selecting appropriate programming languages for their specific needs, promoting the efficient and effective utilization of computational resources in climate studies.

The results indicated that the SALSTM model outperformed other models. The SALSTM model had the lowest mean absolute error (MAE) of 1.212, followed by LSTM (1.345), RNN (1.555), RBFN (1.678), and MLR (1.879).

Conclusion

The SALSTM model the lowest MAE of 1.212, followed by LSTM (1.345), RNN (1.555), RBFN (1.678), and MLR (1.879). The median value of the observed data, SALSTM, LSTM, RNN, RBF, and MLR was 18.5, 18.5, 18.5, 23, 22, and 27.5, respectively. SALSTM could capture complex non-linear relationships between different input features, such as meteorological data, soil conditions, and crop characteristics. By combining the power of LSTM and attention mechanisms, SALSTM could learn intricate patterns and interactions between these factors, enabling it to make more accurate predictions of irrigation demands. By accurately predicting irrigation demands, SALSTM enables farmers to avoid excessive water usage. By proactively adjusting irrigation plans based on SALSTM predictions, managers can minimize the risk of crop losses due to under- or over-irrigation. The next studies can develop current study based on the following comments:

• Comparative Analysis: Conduct a comparative analysis of different models for predicting irrigation demand, including other deep learning architectures, traditional machine learning models, or hybrid models.

• Feature Engineering: Discover additional features that can improve the prediction accuracy of the SALSTM model. Examine how different feature sets affect the performance of the model and identify the most meaningful features for predicting irrigation needs.

• Model Interpretability: Enhance the interpretability of the SALSTM model by investigating techniques such as attention visualization or feature importance analysis.

• Transfer Learning and Generalization: Explore the transferability of the pre-trained SALSTM model across different geographical regions, crops, or irrigation systems. Investigate the effectiveness of transfer learning by fine-tuning the pre-trained model on new datasets.

• Uncertainty Estimation: Incorporate uncertainty estimation techniques into the SALSTM model to quantify prediction uncertainty. This can help decision-makers assess the reliability of the predictions and make informed decisions based on the level of uncertainty associated with irrigation demand predictions..