Machine Learning Techniques for Soil-Sediment-Water Systems

Dear Colleagues,

In recent years, machine learning (ML) has become increasingly prevalent in engineering and science applications, as illustrated by the wide range of applications in solving practical and technical engineering problems. Significant progress has been made in the application and development of numerous ML techniques in different fields of science, especially in soil, sediment, and water systems. Data-driven models based on machine learning can efficiently solve more complex non-linear problems in water-related studies to address engineering and practical challenges. Generally speaking, ML has exhibited significant progress in forecasting and modeling non-linear hydrological applications and in capturing the noise complexity in the dataset. A data-driven model based on machine learning can effectively solve more complex non-linear problems than traditional models employed in various research studies in the field of soil–sediment–water systems. Soil–sediment–water systems are part of the geological environment, and they are essential components of the biosphere, assuring the sustainability of ecosystems. Ecosystem stability and development are affected by both anthropogenic and natural factors in the geochemical composition of these environmental elements. In addressing the issue of computational complexity, ML has been recognized as a helpful tool in analyzing soil–sediment–water systems. As land science faces several societal challenges caused by soil–sediment–water systems, we would like to encourage researchers to contribute their latest ideas, developments, and review papers in the current Special Issue. Potential topics include, but are not limited to, the following:

• the application of deep learning in the geochemistry and mineralogy of soil and water sediments;
• the application of machine learning in sustainable land and water management;
• artificial intelligence in nutrient cycling in land management;
• climate change impacts on soil and water sediments;
• deep learning application in land restoration;
• machine learning-based analysis of soil and water sediments.

Dr. Isa Ebtehaj
Dr. Sayed M. Bateni
Dr. Hamed Azimi
Guest Editors

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• climate change mitigation
• deep learning
• land use
• satellite data
• sediment soil
• soil health/pollution
• diverse landscapes
• sustainable land and water management
• water systems
• watershed management