Optimizing water management using artificial intelligence
| dc.contributor.author | Benseghir, Neila | |
| dc.date.accessioned | 2025-04-23T10:30:33Z | |
| dc.date.available | 2025-04-23T10:30:33Z | |
| dc.date.issued | 2024-12-17 | |
| dc.description.abstract | Effective irrigation management has become increasingly vital due to limited water resources, largescale crop demands, climate change, soil salinity, and reliance on outdated agricultural practices. While widely used, traditional irrigation methods often result in inefficient water usage, exacerbating water scarcity in regions like Algeria, where center pivots consume up to 12,000 m³/hectare/year. This thesis proposes an intelligent irrigation model leveraging artificial intelligence (AI) and machine learning (ML) to optimize water usage in agriculture. The model integrates ensemble learning techniques, combining logistic regression and support vector machine (SVM) classifiers to determine irrigation needs, Long Short-Term Memory (LSTM), and CNN to predict precise water requirements. These AI-driven tools aim to modernize irrigation systems, particularly center pivots, to reduce water consumption and mitigate salinization risks sustainably. | |
| dc.identifier.uri | http://dspace.ensa.dz/handle/123456789/3814 | |
| dc.language.iso | en | |
| dc.subject | Irrigation management | |
| dc.subject | Water optimization | |
| dc.subject | Artificial intelligence (AI) | |
| dc.subject | Machine learning (ML) | |
| dc.subject | Smart irrigation | |
| dc.title | Optimizing water management using artificial intelligence | |
| dc.type | Thesis |