What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Ecology
His primary areas of investigation include Land use, Environmental resource management, Arable land, Central china and Urban sprawl.
His Land use research integrates issues from Competition, Status quo, Genetic algorithm, Mathematical optimization and Type.
His Environmental resource management research is multidisciplinary, incorporating elements of Yangtze river and Urban planning.
Farmland preservation, Variety and Natural resource economics is closely connected to Food security in his research, which is encompassed under the umbrella topic of Arable land.
His Central china study integrates concerns from other disciplines, such as Cartography, Urban expansion, Recreation and Externality.
His work deals with themes such as Remote sensing, Spatial analysis, Urban climate and Environmental planning, which intersect with Cartography.
His most cited work include:
- Visible and near-infrared reflectance spectroscopy-an alternative for monitoring soil contamination by heavy metals. (155 citations)
- Methods and strategy for modeling daily global solar radiation with measured meteorological data – A case study in Nanchang station, China (111 citations)
- Concurrent monitoring of vessels and water turbidity enhances the strength of evidence in remotely sensed dredging impact assessment (108 citations)
What are the main themes of his work throughout his whole career to date?
Yaolin Liu mainly focuses on Land use, Remote sensing, Data mining, Artificial intelligence and Urban planning.
He works mostly in the field of Land use, limiting it down to topics relating to Environmental planning and, in certain cases, Rural settlement, as a part of the same area of interest.
His Remote sensing research includes themes of Image resolution, Pixel and Moderate-resolution imaging spectroradiometer.
The concepts of his Data mining study are interwoven with issues in Spatial analysis, Fuzzy logic and Geographic information system.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
He has included themes like Spatial ecology and Urbanization in his Urban planning study.
He most often published in these fields:
- Land use (23.85%)
- Remote sensing (14.23%)
- Data mining (11.92%)
What were the highlights of his more recent work (between 2017-2021)?
- Urbanization (6.54%)
- Economic geography (6.54%)
- Urban planning (8.85%)
In recent papers he was focusing on the following fields of study:
Yaolin Liu focuses on Urbanization, Economic geography, Urban planning, Soil science and Artificial intelligence.
Yaolin Liu interconnects Geospatial analysis, Ecosystem services, Sustainable development, Recreation and Urban land in the investigation of issues within Urbanization.
His Economic geography study integrates concerns from other disciplines, such as Central china, Urban expansion and Process.
His research in Urban planning intersects with topics in Volcano, Driving factors, Environmental planning, Carbon sequestration and Socioeconomic development.
His Artificial intelligence research includes elements of Computer vision and Pattern recognition.
The various areas that Yaolin Liu examines in his Land use study include Resource, Natural resource economics and Transfer.
Between 2017 and 2021, his most popular works were:
- Rapid identification of soil organic matter level via visible and near-infrared spectroscopy: Effects of two-dimensional correlation coefficient and extreme learning machine. (38 citations)
- Urban expansion and form changes across African cities with a global outlook: Spatiotemporal analysis of urban land densities (37 citations)
- Identifying the relationship between urban land expansion and human activities in the Yangtze River Economic Belt, China (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Ecology
The scientist’s investigation covers issues in Urbanization, Soil test, Second derivative, Soil organic matter and Correlation coefficient.
His Urbanization research is multidisciplinary, incorporating elements of Population growth, International comparisons, Sustainability and Socioeconomics.
Yaolin Liu combines topics linked to Partial least squares regression with his work on Soil test.
In his research, Similarity and Extreme learning machine is intimately related to Support vector machine, which falls under the overarching field of Soil organic matter.
His work in Pattern recognition covers topics such as Regression analysis which are related to areas like Field.
His research integrates issues of Soil science, Soil water, Soil contamination and Pollution in his study of Coefficient of determination.
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