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Special Issue "Watershed Scale Forest Restoration and Sustainable Development"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Ecology and Management".

Deadline for manuscript submissions: 1 June 2019

Special Issue Editor

Guest Editor
Prof. Dr. Guangyu Wang

Univ British Columbia, Fac Forestry, Vancouver, BC V6T 1Z4, Canada
Website | E-Mail
Interests: Watershed management; climate change; conservation; ecosystems; forest management; forest policy; land-use change

Special Issue Information

Dear Colleagues,

Watershed management is an ever-evolving practice involving the management of land, water, biota, and other resources in a defined area for ecological, social, and economic purposes. In this Special Issue, we wish to explore the following questions: How has watershed management evolved? What new tools are available, such as developments in remote sensing, GIS, big data, cloud computing, and multi-level social-ecological systems analysis into watershed management strategies? How can the tools be integrated into sustainable watershed management? What kind of benefits might be obtained from integration across disciplines and jurisdictional boundaries, as well as the incorporation of technological advancements? We also welcome case studies of the successes and failures of integrated watershed management in addressing different ecological, social, and economic dilemmas in geographically diverse locations. We encourage studies from all fields, including integrated management policies, strategy development, traditional knowledge, cross-jurisdictional cooperation and information sharing, advanced data collection and analysis methods, and the consideration of both ecological and socio-economic concerns.

Prof. Dr. Guangyu Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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.

Keywords

  • Integrated watershed management
  • Adaptive management
  • Climate change impacts
  • Spatial analysis modelling
  • Cloud computing and big data
  • Social-ecological systems analysis
  • Traditional ecological knowledge
  • Watershed management
  • Carbon Sequestration and management
  • Ecosystem Services
  • Land-use and land-cover change

Published Papers (1 paper)

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Research

Open AccessArticle Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling
Forests 2019, 10(2), 157; https://doi.org/10.3390/f10020157
Received: 13 December 2018 / Revised: 19 January 2019 / Accepted: 23 January 2019 / Published: 12 February 2019
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Abstract
This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide [...] Read more.
This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different methods such as Area under the Receiver Operating Characteristics (ROC) curve (AUC), Mean Square Error (MSE), Root Mean Square Error (RMSE). Results indicate that the RFBFDT (AUC = 0.826, MSE = 0.189, and RMSE = 0.434) is the best method in comparison to other hybrid models, namely PSOANFIS (AUC = 0.76, MSE = 0.225, and RMSE = 0.474) and PSOANN (AUC = 0.72, MSE = 0.312, and RMSE = 0.558). Thus, it is reasonably concluded that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling. Full article
(This article belongs to the Special Issue Watershed Scale Forest Restoration and Sustainable Development)
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