Predicting Standardized Streamflow index for hydrological drought using machine learning models
Shahabbodin Shamshirband,Sajjad Hashemi,Hana Salimi,Saeed Samadianfard,Esmaeil Asadi,Sadra Shadkani,Katayoun Kargar,Amir Mosavi,Narjes Nabipour,Kwok Wing Chau +9 more
TLDR
Three indices of drought are modeled using Support Vector Regression, Gene Expression Programming, and M5 model trees and the results indicate that SPI delivered higher accuracy than SSI.Abstract:
Hydrological droughts are characterized based on their duration, severity, and magnitude. Among the most critical factors, precipitation, evapotranspiration, and runoff are essential in modeling t...read more
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Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
Saeed Samadianfard,Sajjad Hashemi,Katayoun Kargar,Mojtaba Izadyar,Ali Mostafaeipour,Amir Mosavi,Narjes Nabipour,Shahaboddin Shamshirband +7 more
TL;DR: It was concluded that the WOA optimization algorithm could improve the prediction accuracy of the MLP model and may be recommended for accurate wind speed prediction.
Journal ArticleDOI
Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River
TL;DR: A deep neural network was employed to predict the streamflow of the Hankou Hydrological Station on the Yangtze River using the Empirical Mode Decomposition (EMD) algorithm and Encoder Decoder Long Short-Term Memory (En-De-LSTM) architecture, and the results showed the reliability of this method in catastrophic flood years and longtime continuous rolling forecasting.
Journal ArticleDOI
Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
Sedigheh Mohamadi,Saad Sh. Sammen,Fatemeh Panahi,Mohammad Ehteram,Ozgur Kisi,Amir Mosavi,Ali Najah Ahmed,Ahmed El-Shafie,Ahmed El-Shafie,Nadhir Al-Ansari +9 more
TL;DR: The general results indicated that the NPA and wavelet coherence analysis are useful tools for modelling drought indices and suggested that the hybrid models performed better than the standalone MLP, RBFNN, ANFIS, and SVM models.
Journal ArticleDOI
Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression
Shahab S. Band,Essam Heggy,Essam Heggy,Sayed M. Bateni,Hojat Karami,Mobina Rabiee,Saeed Samadianfard,Kwok Wing Chau,Amir Mosavi,Amir Mosavi,Amir Mosavi +10 more
TL;DR: In this paper, support vector regression (SVR), Gaussian process regression (GPR), and support vector clustering (SWC) were used to predict groundwater level in arid regions.
Journal ArticleDOI
Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation
Si Ha,Darong Liu,Lin Mu,Lin Mu +3 more
TL;DR: Wang et al. as mentioned in this paper used three deep neural network frameworks: stacked long shortterm memory, conv long short-term memory encoder-decoder long short term memory and Conv long-shortterm memory (LSTM) encoderdecoder gate recurrent unit to predict the monthly streamflow of the Yangtze River.
References
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TL;DR: The definition of drought has continually been a stumbling block for drought monitoring and analysis as mentioned in this paper, mainly related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.