Integration of remote sensing data and GIS technologies in river management system

Abstract

Abstract Effective River system management is essential for conserving water resources, improving agricultural productivity, and sustaining ecological health. Remote sensing is crucial for evaluating and tracking several elements of river systems. The study explores the incorporation of remote sensing into Geographic Information Systems (GIS) and Artificial Intelligence (AI) to acquire a thorough comprehension of river dynamics and accurately record minor fluctuations in river conditions. The study demonstrates the utilization of satellite series such as Landsat, Sentinel to enhance monitoring and management methods through the analysis of high-resolution imagery and data. AI helps remote sensing by automating data processing, finding patterns, and making predictions about river conditions and trends. Machine learning techniques enhance the analytical capabilities of GIS and remote sensing data by accurately classifying land cover, predicting flood events, and evaluating water quality. The research highlights the novel approaches of utilizing remote sensing and GIS to tackle the issues related to data accessibility, analysis, and verification. The study also acknowledges specific constraints and difficulties, such as concerns over the accessibility of data, intricacies in analysis, and the processes involved in validation. The statement underscores the importance of ongoing research, technical progress, and collaboration among stakeholders to overcome these limitations and fully exploit the capabilities of remote sensing, artificial intelligence, and geographic information systems. An integrated approach is crucial for the development of successful policies and strategies that improve the resilience and sustainable management of river systems. This approach eventually promotes sustainable water resource practices and ecological preservation.

Keywords

• Monitoring
• River management
• Remote sensing & geographic information system
• Sustainable
• Machine learning