Artificial Intelligence Optimization of Water Resource Placement for Dryland Wildlife

Abstract

Introduction: Water shortages present a serious issue in arid ecosystems as far as the movement and survival of animals are concerned. Proper positioning of water sources is critical to biodiversity conservation, but the current approaches to the positioning of water sources are mostly based on field observations and decision-making by experts, which could ignore some ecological variations. This is where Artificial Intelligence (AI) comes into play.Materials and Methods:In this research work, an AI-enabled approach is suggested to find out the best place for water resources in dryland areas which serve as habitat for wildlife. In this research work, data related to environment, topography, wildlife distribution, climatic conditions, vegetation, and water availability was used. The collected data was first preprocessed by applying normalization, missing value handling, and feature selection. Then the AI optimization model finds out the best places to place the water resources based on spatial relationship between ecological factors and water needs of wildlife. Results and Discussion:The results of the experiment proved that the AI optimization model proposed by the outperformed all random placement, expert placement, and traditional machine learning methods. The model yielded 94.2% wildlife access, 92.5% habitat suitability, and 93.8% placement effectiveness while decreasing the average distance to water resources to 3.2 km. Conclusion:The results show that through AI-based optimization, sustainable water resource planning can be enhanced significantly because of better accessibility of resources and less wastage of resources. The suggested approach offers a dependable solution for future intelligent management of wildlife habitats.