Integrating remote sensing with swarm intelligence and artificial intelligence for modelling wetland habitat vulnerability in pursuance of damming

Satellite system advances, remote sensing and drone technology are continuing. These progresses produce high-quality images that need efficient processing for smart agricultural applications. These possibilities to merge computer vision and artificial intelligence in agriculture are exploited with recent deep educational technology. This involves essential phenomena of data and huge quantities of data stored, analysed and used when making decisions. This paper demonstrates how computer vision in agriculture can be used.

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REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Ensemble ◽

10.37948/ensemble-2020-0202-a016 ◽

2021 ◽

Vol 2 (2) ◽

pp. 145-165

Author(s):

Tanmoy Sarkar ◽

◽

Tapas Pal ◽

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Information System ◽

Geographic Information System ◽

Soil Erosion ◽

Geographic Information ◽

Methodological Development ◽

Advantages And Disadvantages ◽

The Future ◽

Soil Erosion By Water

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

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Study on control of artificial intelligence on swarm intelligence

Computer, Intelligent Computing and Education Technology ◽

10.1201/9781315760827-125 ◽

2014 ◽

pp. 596-601

Keyword(s):

Artificial Intelligence ◽

Swarm Intelligence

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Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence

Journal of Chemistry ◽

10.1155/2020/7390545 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Yun Liu ◽

Yuqin Jing ◽

Yinan Lu

Keyword(s):

Artificial Intelligence ◽

Remote Sensing ◽

Air Pollution ◽

Transmission Model ◽

Atmospheric Radiation ◽

Remote Sensing Monitoring ◽

Quantitative Remote Sensing ◽

Monitoring Accuracy ◽

Radiation Transmission ◽

Monitoring Algorithm

When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.

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Swarm-Based Techniques in E-Learning

Advances in E-Learning ◽

10.4018/978-1-59904-756-0.ch011 ◽

2011 ◽

pp. 199-212

Author(s):

Sergio Gutiérrez ◽

Abelardo Pardo

Keyword(s):

Artificial Intelligence ◽

Swarm Intelligence ◽

Social Insects ◽

Learning System ◽

The Internet ◽

Artificial Intelligence Technique ◽

E Learning ◽

Weak Points ◽

Intelligence Technique

This chapter provides an overview of the use of swarm-intelligence techniques in the field of e-learning. Swarm intelligence is an artificial intelligence technique inspired by the behavior of social insects. Taking into account that the Internet connects a high number of users with a negligible delay, some of those techniques can be combined with sociology concepts and applied to e-learning. The chapter analyzes several of such applications and exposes their strong and weak points. The authors hope that understanding the concepts used in the applications described in the chapter will not only inform researchers about an emerging trend, but also provide with interesting ideas that can be applied and combined with any e-learning system.

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