Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria

Djerbouai, Salim; Souag-Gamane, Doudja

doi:10.1007/s11269-016-1298-6

Cited by 89 publications

(30 citation statements)

References 37 publications

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“…The results of proposed model were compared and validated against the nature-inspired algorithm and stochastic (time-series) model built by numerous drought indices (DIs). For instance, there are studies conducted on the SPI prediction using various versions of AI models [40,[51][52][53][54][55]. Memarian et al [56] applied the CANFIS model to predict the meteorological drought in Birjand, Iran using global climatic indicators and lagged values of SPI.…”

Section: Plos Onementioning

confidence: 99%

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

et al. 2020

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A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipitation index (SPI). Multiple scales of drought information at six meteorological stations located in Uttarakhand State, India, are used. Different lead times of SPI were computed for prediction, including 1, 3, 6, 9, 12, and 24 months, with inputs abstracted by autocorrelation function (ACF) and partial-ACF (PACF) analysis at 5% significance level. The proposed CANFIS model was validated against two models: classical artificial intelligence model (e.g., multilayer perceptron neural network (MLPNN)) and regression model (e.g., multiple linear regression (MLR)). Several performance evaluation metrices (root mean square error, Nash-Sutcliffe efficiency, coefficient of correlation, and Willmott index), and graphical visualizations (scatter plot and Taylor diagram) were computed for the evaluation of model performance. Results indicated that the CANFIS model predicted the SPI better than the other models and prediction results were different for different meteorological stations. The proposed model can build a reliable expert intelligent system for predicting meteorological drought at multi-time scales and decision making for remedial schemes to cope with meteorological drought at the study stations and can help to maintain sustainable water resources management.

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Section: Plos Onementioning

confidence: 99%

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

et al. 2020

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“…Its popularity stems from its flexibility and ease of use for detecting droughts at multiple timescales (Ganguli and Ganguly, ). Since the SPI was designed to provide a dimensionless index, SPI values can often be used to spatio‐temporally compare an overall view of the drought at a national or global scale for a range of practical applications (Djerbouai and Souag‐Gamane, ). To compute the SPI, daily precipitation data are first aggregated at different timescales (e.g., 3, 6, 12, 24 or 36 months).…”

Section: Hydrologic Data and Drought Indicesmentioning

confidence: 99%

Spatio‐temporal drought patterns of multiple drought indices based on precipitation and soil moisture: A case study in South Korea

Kwon

Han

2019

Intl Journal of Climatology

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This study aims to explore the spatio‐temporal characteristics of meteorological and agricultural droughts using the standardized precipitation index (SPI) and standardized soil moisture index (SSI), respectively, as well as their relationships over the past three decades (1986–2016) in South Korea. The SSI shows less frequent droughts and longer drought duration compared to the SPI, due to the gradual decrease in the autocorrelation functions of the SSI. The strongest cross‐correlations are observed at a 1‐month lag between the SPI and SSI for most stations. Thus, the SPI could be more appropriate for defining the onset of a drought, whereas the SSI appears to be more effective for describing drought persistence. Moreover, the transition from meteorological to agricultural droughts is significantly dependent on the season, indicating that the transition between them is highly correlated with antecedent moisture conditions. The copula‐based multivariate standardized drought index (MSDI) is introduced to explicitly postulate interdependence between the SPI and SSI in the context of a multivariate probability distribution. We employ a hierarchical agglomerative clustering approach along with a quantile regression model to better understand the spatio‐temporal pattern of the MSDI. More drought episodes under moderate to severe drought conditions are observed along the southern coast of South Korea. Additionally, persistent droughts with higher severity are observed in the northern part of South Korea, which may be attributed to a significant decreasing trend (or increasing drought risk).

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“…DWT is applied to decompose the original streamflow, precipitation, and temperature data for each zone 1, 2, and 3 and decomposed sub-series for each zone 1, 2, and 3 are shown in Figures 4-6, respectively. The decomposition process is suggested by [100,101] and is used to obtain the subseries of two decomposition levels, which is recommended. Selecting an appropriate mother wavelet is critical to obtain a better wavelet hybrid model.…”

Section: Empirical Mode Decomposition and Ensemble Empirical Mode Decmentioning

confidence: 99%

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

et al. 2018

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Consistent streamflow forecasts play a fundamental part in flood risk mitigation. Population increase and water cycle intensification are extending not only globally but also among Pakistan’s water resources. The frequency of floods has increased in the last few decades in the country, which emphasizes the importance of efficient practices needed to adopt for various aspects of water resource management such as reservoir scheduling, water sustainability, and water supply. The purpose of this study is to develop a novel hybrid model for streamflow forecasting and validate its efficiency at the upper Indus basin (UIB), Pakistan. Maximum streamflow in the River Indus from its upper mountain basin results from melting snow or glaciers and climatic unevenness of both precipitation and temperature inputs, which will, therefore, affect rural livelihoods at both a local and a regional scale through effects on runoff in the Upper Indus basin (UIB). This indicates that basins receive the bulk of snowfall input to sustain the glacier system. The present study will help find the runoff from high altitude catchments and estimated flood occurrence for the proposed and constructed hydropower projects of the Upper Indus basin (UIB). Due to climate variability, the upper Indus basin (UIB) was further divided into three zone named as sub-zones, zone one (z1), zone two (z2), and zone three (z3). The hybrid models are designed by incorporating artificial intelligence (AI) models, which includes Feedforward backpropagation (FFBP) and Radial basis function (RBF) with decomposition methods. This includes a discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD). On the basis of the autocorrelation function and the cross-correlation function of streamflow, precipitation and temperature inputs are selected for all developed models. Data have been analyzed by comparing the simulation outputs of the models with a correlation coefficient (R), root mean square errors (RMSE), Nash-Sutcliffe Efficiency (NSE), mean absolute percentage error (MAPE), and mean absolute errors (MAE). The proposed hybrid models have been applied to monthly streamflow observations from three hydrological stations and 17 meteorological stations in the UIB. The results show that the prediction accuracy of the decomposition-based models is usually better than those of AI-based models. Among the DWT and EEMD based hybrid model, EEMD has performed significantly well when compared to all other hybrid and individual AI models. The peak value analysis is also performed to confirm the results’ precision rate during the flood season (May-October). The detailed comparative analysis showed that the RBFNN integrated with EEMD has better forecasting capabilities as compared to other developed models and EEMD-RBF can capture the nonlinear characteristics of the streamflow time series during the flood season with more precision.

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Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Stochastic Models: Case of the Algerois Basin in North Algeria

Cited by 89 publications

References 37 publications

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India

Spatio‐temporal drought patterns of multiple drought indices based on precipitation and soil moisture: A case study in South Korea

Application of Integrated Artificial Neural Networks Based on Decomposition Methods to Predict Streamflow at Upper Indus Basin, Pakistan

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