Drought forecasting using feed-forward recursive neural network
By Anshuman Mishra, V. Desai. 2006
374 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/ccfb9195def94bb9afe9036293892047dcc5cfc7
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In Electrical and Electronic Engineering
In Environmental Engineering
In Global and Planetary Change
Citations in the corpus
- Application of linear stochastic models for drought forecasting in the Büyük Menderes river basin, western Turkey (2010, methodology): “The nature of the SPI allows an analyst to determine the rarity of a drought or an anomalously wet event at a particular time scale for any location in the world that has a precipitation record ( Mishra and Desai 2006 ).”
- Meteorological drought prediction using heuristic approaches based on effective drought index: a case study in Uttarakhand (2020, methodology): “…studies conducted on meteorological drought forecasting using machine learning and stochastic models based on various drought indices such as (Mishra and Desai 2006; Belayneh et al. 2014; Deo and Şahin, 2015a, b; Deo et al. 2017a; Mouatadid et al. 2018; Ali et al. 2018; Abbasi et al. 2019).”
- Multi-models for SPI drought forecasting in the north of Haihe River Basin, China (2017, methodology): “…Environment and Tourism, Capital Normal University, Beijing 100048, China 2 Beijing Laboratory of Water Resource Security, Beijing 100048, China 3 School of Mathematical Sciences, Capital Normal University, Beijing 100048, China to make drought forecasting more accurate (Mishra and Desai 2006).”
- Hydrological Drought Class Transition Using SPI and SRI Time Series by Loglinear Regression (2015, background): “…2001), time series analysis (Han et al. 2010), probability models (Haan 2002; Cancelliere et al. 2007), Artificial neural network model (Mishra and Desai 2006) and long-lead drought forecasting using climate indices as well as the periodic nature of hydro-meteorological variables…”
- Brain Intelligence: Go beyond Artificial Intelligence (2017, methodology): “Recursive neural network [41] is another deep neural network created by applying the same set of weights recursively over a structure in order to produce a structured prediction over the input by transferring a given structure in topological”
- Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, methodology): “Second, the index makes it possible to describe drought on multiple time scales (Tsakiris and Vangelis, 2004; Mishra and Desai, 2006; Cacciamani et al., 2007).”
- SPI-based drought category prediction using loglinear models (2008, background): “Present trends also include neural networks and stochastic models applied to time series of precipitation or streamflow (Mishra and Desai, 2006; Thyer et al., 2006).”