AquaPredict: Deploying Data-Driven Aquatic Models for Optimizing Sustainable Agriculture Practices
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 6 |
| Year of Publication : 2024 |
| Authors : P. Laxmikanth, V. Vijayasherly, M. Mounika, Pendem Swetha, J Adilakshmi, M. Bhavsingh |
10.14445/23488379/IJEEE-V11I6P109
How to Cite?
P. Laxmikanth, V. Vijayasherly, M. Mounika, Pendem Swetha, J Adilakshmi, M. Bhavsingh, "AquaPredict: Deploying Data-Driven Aquatic Models for Optimizing Sustainable Agriculture Practices," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 6, pp. 76-90, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P109
Abstract:
This paper introduces AquaPredict, an innovative predictive framework designed to assess the impacts of agricultural practices on aquatic systems through advanced machine learning algorithms and the integration of diverse data sources. Utilizing Random Forests, Gradient Boosting Machines (GBMs), and Deep Neural Networks (DNNs), AquaPredict surpasses existing models like SWAT in predictive accuracy, precision, recall, F1 score, AUC-ROC, and other key metrics, demonstrating a marked improvement in the ability to forecast environmental outcomes. Through rigorous validation across various scenarios, including drought, heavy rainfall, and nutrient runoff, AquaPredict achieved notable accuracy (0.93), precision (0.95), and recall (0.91) alongside a low Log Loss (0.29) and high Explained Variance (0.93), underscoring its superior performance and reliability. The framework’s integration of satellite imagery, water quality sensors, and agricultural records into a coherent predictive model represents a significant contribution to sustainable agriculture, offering a nuanced understanding of the complex interplay between agricultural practices and aquatic ecosystem health. This paper not only details the development and evaluation of AquaPredict but also highlights its potential implications for informed decision-making in environmental management and policy. Recommendations for future research focus on expanding the model’s capabilities, including the incorporation of socio-economic factors and the exploration of emerging technologies, to further enhance its applicability and effectiveness in promoting sustainable agricultural practices and preserving aquatic environments.
Keywords:
Sustainable agriculture, Aquatic ecosystems, Machine Learning, Data integration, Environmental modeling, Predictive analytics.
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