Machine Learning Techniques for Soil-Sediment-Water Systems

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Soil-Sediment-Water Systems".

Deadline for manuscript submissions: closed (25 August 2023) | Viewed by 1260

Special Issue Editors

Department of Soils and Agri-Food Engineering, Université Laval, Québec, QC G1V0A6, Canada
Interests: climate change; deep learning; hydroinfomatics; machine learning; sediment transport; time series; water resource management
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Guest Editor
Department of Civil and Environmental Engineering, Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: satellite data processing; land surface product algorithm; remote sensing classification with machine learning;agrometeorology; agrometeorological disater monitoring with remote sensing
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Guest Editor
Department of Civil Engineering, Faculty of Engineering and Applied Sciences, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
Interests: Arctic subsea hazards; Iceberg-seabed interaction; computational fluid dynamics; machine learning; AI application

Special Issue Information

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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • 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

Published Papers (1 paper)

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15 pages, 21321 KiB  
Runoff and Sediment Yield Processes in a Tropical Eastern Indian River Basin: A Multiple Machine Learning Approach
by Alireza Moghaddam Nia, Debasmita Misra, Mahsa Hasanpour Kashani, Mohsen Ghafari, Madhumita Sahoo, Marzieh Ghodsi, Mohammad Tahmoures, Somayeh Taheri and Maryam Sadat Jaafarzadeh
Land 2023, 12(8), 1565; - 07 Aug 2023
Viewed by 763
Tropical Indian river basins are well-known for high and low discharges with high peaks of flood during the summer and the rest of the year, respectively. A high intensity of rainfall due to cyclonic and monsoon winds have caused the tropical Indian rivers [...] Read more.
Tropical Indian river basins are well-known for high and low discharges with high peaks of flood during the summer and the rest of the year, respectively. A high intensity of rainfall due to cyclonic and monsoon winds have caused the tropical Indian rivers to witness more runoff. These rivers are also known for carrying a significant amount of sediment load. The complex and non-linear nature of the sediment yield and runoff processes and the variability of these processes depend on precipitation patterns and river basin characteristics. There are a number of other elements that make it difficult to forecast with great precision. The present study attempts to model rainfall–runoff–sediment yield with the help of five machine learning (ML) algorithms—support vector regression (SVR), artificial neural network (ANN) with Elman network, artificial neural network with multilayer perceptron network, adaptive neuro-fuzzy inference system (ANFIS), and local linear regression, which are useful in river basins with scarce hydrological data. Daily, weekly, and monthly runoff and sediment yield (SY) time series of Vamsadhara river basin, India for a period from 1 June to 31 October for the years 1984 to 1995 were simulated using models based on these multiple machine learning algorithms. Simulated results were tested and compared by means of three evaluation criteria, namely Pearson correlation coefficient, Nash–Sutcliffe efficiency, and the difference of slope. The results suggested that daily and weekly predictions of runoff based on all the models can be successfully employed together with precipitation observations to predict future sediment yield in the study basin. The models prepared in the present study can be helpful in providing essential insight to the erosion–deposition dynamics of the river basin. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Soil-Sediment-Water Systems)
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