A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction-Reference-Cited by-同舟云学术

A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction

Published:2022-11 Issue: Volume:845 Page:157220
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Senanayake Sumudu,Pradhan Biswajeet,Alamri Abdullah,Park Hyuck-Jin

Funder

NSW Department of Education and Training

Faculty of Engineering and Information Technology, University of Technology Sydney

University of Technology Sydney

King Saud University

Centre for Advanced Modelling and Geospatial lnformation Systems, University of Technology Sydney

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference69 articles.

1. Erosion vulnerability assessment in REINA, Quezon Province, Philippines with raster-based tool built within GIS environment;Adornado;Agric. Inf. Res.,2009

2. Intelligent vulnerability prediction of soil erosion hazard in semi-arid and humid region;Agnihotri;Environ. Dev. Sustain.,2021

3. Unpacking barriers to socially inclusive weather index insurance: towards a framework for inclusion;Aheeyar;Water (Switzerland),2019

4. A decadal historical satellite data and rainfall trend analysis (2001–2016) for flood hazard mapping in Sri Lanka;Alahacoon;Remote Sens.,2018

5. Reducing risks to food security from climate change;Campbell;Glob. Food Sec.,2016

Cited by 42 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Impact of land use/land cover change on surface water hydrology in Akaki river catchment, Awash basin, Ethiopia;Physics and Chemistry of the Earth, Parts A/B/C;2024-10

2. Conjunct applicability of MCDM-based machine learning algorithms in mapping the sediment formation potential;Environment, Development and Sustainability;2024-08-13

3. Soil temperature prediction based on explainable artificial intelligence and LSTM;Frontiers in Environmental Science;2024-07-31

4. Machine learning in modelling the urban thermal field variance index and assessing the impacts of urban land expansion on seasonal thermal environment;Sustainable Cities and Society;2024-07

5. Mapping wetland habitat health in moribund deltaic India using machine learning and deep learning algorithms;Ecohydrology & Hydrobiology;2024-07