A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction
出版年份 2022 全文链接
标题
A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction
作者
关键词
-
出版物
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 845, Issue -, Pages 157220
出版商
Elsevier BV
发表日期
2022-07-12
DOI
10.1016/j.scitotenv.2022.157220
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Rainfall and runoff time-series trend analysis using LSTM recurrent neural network and wavelet neural network with satellite-based meteorological data: case study of Nzoia hydrologic basin
- (2021) Yashon O. Ouma et al. Complex & Intelligent Systems
- Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models
- (2021) Peng Deng et al. THEORETICAL AND APPLIED CLIMATOLOGY
- Projections of soil loss by water erosion in Europe by 2050
- (2021) Panos Panagos et al. ENVIRONMENTAL SCIENCE & POLICY
- Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia
- (2021) Demeke Endalie et al. Water Science and Technology-Water Supply
- Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility
- (2021) Fengjie Wang et al. Alexandria Engineering Journal
- Deep learning convolutional neural network in rainfall–runoff modelling
- (2020) Song Pham Van et al. JOURNAL OF HYDROINFORMATICS
- Assessing Soil Erosion Hazards Using Land-Use Change and Landslide Frequency Ratio Method: A Case Study of Sabaragamuwa Province, Sri Lanka
- (2020) Sumudu Senanayake et al. Remote Sensing
- Intelligent vulnerability prediction of soil erosion hazard in semi-arid and humid region
- (2020) Deepak Agnihotri et al. ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
- Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river
- (2020) Thi-Thu-Hong Phan et al. ADVANCES IN WATER RESOURCES
- Development of advanced artificial intelligence models for daily rainfall prediction
- (2020) Binh Thai Pham et al. ATMOSPHERIC RESEARCH
- Propagation of parameter uncertainty in SWAT: A probabilistic forecasting method based on polynomial chaos expansion and machine learning
- (2020) Maysara Ghaith et al. JOURNAL OF HYDROLOGY
- Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India
- (2020) Rabin Chakrabortty et al. NATURAL HAZARDS
- Soil erosion modeling using erosion pins and artificial neural networks
- (2020) Vahid Gholami et al. CATENA
- Predicting flood susceptibility using LSTM neural networks
- (2020) Zhice Fang et al. JOURNAL OF HYDROLOGY
- Lake level dynamics exploration using deep learning, artificial neural network, and multiple linear regression techniques
- (2019) Jinfeng Wen et al. Environmental Earth Sciences
- Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network
- (2019) S. Poornima et al. SOFT COMPUTING
- Streamflow and rainfall forecasting by two long short-term memory-based models
- (2019) Lingling Ni et al. JOURNAL OF HYDROLOGY
- Correction model for rainfall forecasts using the LSTM with multiple meteorological factors
- (2019) Chang‐Jiang Zhang et al. METEOROLOGICAL APPLICATIONS
- Prediction of Rainfall Using Intensified LSTM Based Recurrent Neural Network with Weighted Linear Units
- (2019) S. Poornima et al. Atmosphere
- Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia
- (2019) Sahar Hadi Pour et al. ATMOSPHERIC RESEARCH
- State and trends of hillslope erosion across New South Wales, Australia
- (2019) Xihua Yang CATENA
- Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data
- (2018) V. Gholami et al. CATENA
- Current and future assessments of soil erosion by water on the Tibetan Plateau based on RUSLE and CMIP5 climate models
- (2018) Hongfen Teng et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A Decadal Historical Satellite Data and Rainfall Trend Analysis (2001–2016) for Flood Hazard Mapping in Sri Lanka
- (2018) et al. Remote Sensing
- Machine Learning in Agriculture: A Review
- (2018) Konstantinos Liakos et al. SENSORS
- Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
- (2018) Wei Chen et al. CATENA
- Recent rainfall-induced rapid and long-traveling landslide on 17 May 2016 in Aranayaka, Kagelle District, Sri Lanka
- (2018) Khang Dang et al. Landslides
- Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion
- (2017) Omid Rahmati et al. GEOMORPHOLOGY
- A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework
- (2017) Zhongmin Liang et al. THEORETICAL AND APPLIED CLIMATOLOGY
- Physically based soil erosion and sediment yield models revisited
- (2016) Ashish Pandey et al. CATENA
- Soil erosion risk associated with climate change at Mantaro River basin, Peruvian Andes
- (2016) Sly W. Correa et al. CATENA
- Century scale climate change in the central highlands of Sri Lanka
- (2016) J De Silva et al. Journal of Earth System Science
- Reducing risks to food security from climate change
- (2016) Bruce M. Campbell et al. Global Food Security-Agriculture Policy Economics and Environment
- Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin
- (2016) B.P. Ganasri et al. Geoscience Frontiers
- Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European scale
- (2015) Panos Panagos et al. ENVIRONMENTAL SCIENCE & POLICY
- Spatial and temporal variation of rainfall trends of Sri Lanka
- (2015) P. Wickramagamage THEORETICAL AND APPLIED CLIMATOLOGY
- NDVI time series for monitoring RUSLE cover management factor in a tropical watershed
- (2014) V.L. Durigon et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Addressing key limitations associated with modelling soil erosion under the impacts of future climate change
- (2012) Donal Mullan et al. AGRICULTURAL AND FOREST METEOROLOGY
- Performance of an artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a tropical site
- (2007) Akila D. Kumarasiri et al. HYDROLOGICAL PROCESSES
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