Prospecting droughts with stochastic artificial neural networks

Ochoa-Rivera, J. C.

doi:10.1016/j.jhydrol.2008.01.006

Cited by 23 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Stochastic models are linear models with limited ability to predict nonlinear data. To effectively predict nonlinear data, an increasing number of researchers have begun to use artificial neural networks (ANNs) to predict hydrological data in the past decade (Kousari et al 2017;Seibert et al 2017;Marj and Meijerink 2011;Ochoa-Rivera 2008;Sigaroodi et al 2013). Artificial neural networks have been used as drought prediction tools in many studies (Seibert et al 2017;Borji et al 2016;Deo and Ş ahin 2015;Chen et al 2017;Belayneh and Adamowski 2012;Belayneh et al 2016) and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Zhang

Ding

et al. 2020

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of their minimal information requirements. The motivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA–support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA–SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash–Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA–SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA–SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1–2 months ahead show reasonably good agreement with the actual data.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Zhang

Ding

et al. 2020

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

show abstract

“…Drought forecasting using ANN model: The ANN modelling has been greatly used for drought forecasting in the world [55,56]. For instance, it has successfully been used in India to forecast drought in Kansabati River Basin [51].…”

Section: Classification Of Ann Model Architecturesmentioning

confidence: 99%

Drought Forecasting Using Indices and Artificial Neural Networks for Upper Tana River Basin, Kenya-A Review Concept

Wambua¹

2014

J Civil Environ Eng

View full text Add to dashboard Cite

Due to increased impact of drought on water availability at different scales there is need to understand droughts especially in upper Tana River basin which is a critical and largest water system in Kenya. There is need to correlate trends of drought as influenced by the climate variability of the present times. Drought frequency, duration and intensity in the basin have been increasing. The influencing hydro-meteorological parameters and their interaction are necessary in developing measures for mitigating impacts of droughts. It is important to have a timely review of drought definitions and fundamental concepts of droughts, classification of droughts, types of drought indices, historical droughts and artificial neural networks with special focus of Kenyan a basin. Out of the review, this paper draws conclusions where gaps for more focused research especially for a typical river basin in Kenya exist. By developing effective drought forecasting tool for on-set detection and drought classification and drought forecasting, information on decision making on matters of drought preparedness and mitigation programmes will be available for proper water resources management.

show abstract

“…Despite the increasing popularity of ANN‐based methodologies, they have never been applied to the regional frequency prediction of low‐flow characteristics. A relatively limited number of publications dealt with drought forecasting and drought risk assessment based on ANNs [ Crespo and Mora , 1993; Incerti et al , 2007; Mishra et al , 2007; Morid et al , 2007; Ochoa‐Rivera , 2008]. It is the intent of this paper to apply ANN modeling techniques to regional low‐flow estimation at ungauged sites.…”

Section: Introduction and Reviewmentioning

confidence: 99%

Regional low‐flow frequency analysis using single and ensemble artificial neural networks

Ouarda

Shu

2009

Water Resources Research

View full text Add to dashboard Cite

[1] In this paper, artificial neural networks (ANNs) are introduced to obtain improved regional low-flow estimates at ungauged sites. A multilayer perceptron (MLP) network is used to identify the functional relationship between low-flow quantiles and the physiographic variables. Each ANN is trained using the Levenberg-Marquardt algorithm. To improve the generalization ability of a single ANN, several ANNs trained for the same task are used as an ensemble. The bootstrap aggregation (or bagging) approach is used to generate individual networks in the ensemble. The stacked generalization (or stacking) technique is adopted to combine the member networks of an ANN ensemble. The proposed approaches are applied to selected catchments in the province of Quebec, Canada, to obtain estimates for several representative low-flow quantiles of summer and winter seasons. The jackknife validation procedure is used to evaluate the performance of the proposed models. The ANN-based approaches are compared with the traditional parametric regression models. The results indicate that both the single and ensemble ANN models provide superior estimates than the traditional regression models. The ANN ensemble approaches provide better generalization ability than the single ANN models.Citation: Ouarda, T. B. M. J., and C. Shu (2009), Regional low-flow frequency analysis using single and ensemble artificial neural networks, Water Resour. Res., 45, W11428,

show abstract

Prospecting droughts with stochastic artificial neural networks

Cited by 23 publications

References 19 publications

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Application of a Hybrid ARIMA–SVR Model Based on the SPI for the Forecast of Drought—A Case Study in Henan Province, China

Drought Forecasting Using Indices and Artificial Neural Networks for Upper Tana River Basin, Kenya-A Review Concept

Regional low‐flow frequency analysis using single and ensemble artificial neural networks

Contact Info

Product

Resources

About