Design of an Iterative Method Integrating Deep Feature Synthesis and Gaussian Process Regression for Sustainable Soil and Water Management

Abstract

The exigency for advanced methodologies in the management of soil and water resources is paramount in the face of escalating environmental variability and intensifying resource depletion. Existing strategies often fall short due to simplistic data integration techniques and limited temporal-spatial resolution, which fail to capture complex environmental dynamics comprehensively. To address these deficiencies, this paper introduces an integrated machine learning framework that enhances predictive accuracy and decision robustness for sustainable growth development across soil and water landscapes.Our proposed model innovatively combines Deep Feature Synthesis (DFS), Long Short-Term Memory (LSTM) networks with an Attention Mechanism, Gaussian Process Regression (GPR) with Multi-Resolution Fusion, and Bayesian Optimization for robust decision-making. DFS is utilized to automate the extraction of complex features from multispectral imagery, soil composition data, climate indices, and land use data, improving model performance by 5-10% over traditional methods. It efficiently handles various data types and temporal relationships, thereby enriching model inputs with significant environmental factors.Temporal dependencies and seasonal variations are adeptly modeled using LSTM networks complemented by an Attention Mechanism. This configuration not only enhances interpretability but also ensures precise capture of seasonal patterns, reducing mean absolute error by 15-20% compared to conventional timestamp series models. Spatial interpolation accuracy is substantially advanced through GPR equipped with Multi-Resolution Fusion, which synergistically integrates disparate remote sensing data, thereby elevating the spatial resolution of soil and water property maps and increasing the coefficient of determination (R²) by 0.1-0.2.Furthering the model's utility, Bayesian Optimization contextualizes decision-making within a probabilistic framework that accommodates uncertainty, optimizing operational parameters to substantively diminish decision variance by 10-15%. This strategic incorporation of robust optimization mechanisms underpins more reliable and effective management practices for environmental resources.Collectively, the deployment of these sophisticated machine learning techniques fosters a robust analytical foundation, enabling nuanced understanding and proactive management of soil and water resources. The impacts of this research are profound, potentially guiding policy formulations and operational strategies in environmental management with enhanced precision and adaptability, thereby promoting continuous sustainable development in the face of global environmental challenges.