Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Sigaroodi, Shahram Khalighi; Chen, Qiuwen; Ebrahimi, Shiva; Nazari, Ali; Choobin, B.

doi:10.5194/hess-18-1995-2014

Cited by 58 publications

(20 citation statements)

References 24 publications

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“…It is more probably because of the low fluctuation within the data during the drought periods.Since the standard deviation of data reduces during the dry months the uncertainty of forecasting reduces consequently. Throughout the long-term prediction of precipitation in Maharloo Lake Basin, south-west of Iran,Sigaroodi et al (2013) reported a similar result. They attributed low accuracy of wet months' forecast to the long distance of the case study to the Pacific and Atlantic oceans.…”

supporting

confidence: 74%

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Chitsaz

Azarnivand

Araghinejad

2016

Hydrological Sciences Journal

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An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfallrunoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis, and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in Downloaded by [Rutgers University] at 15:43 27 August 2015A c c e p t e d M a n u s c r i p t 2 forecasting accuracy. The results reveal that SVD was highly effective in preprocessing of data-driven evaluations.

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supporting

confidence: 74%

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Chitsaz

Azarnivand

Araghinejad

2016

Hydrological Sciences Journal

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“…Among these indices, correlation of the AMO, NINO3.4 NINO4, NTA, and TNA is significant with SPI time series at the 5% level of confidence (all of the meaningful indices are related in different lag-times (Table 1) with SPI but not simultaneously). The goal of the Cross-Correlation Function (CCF) is to determine the best time lag for independent variables that have the most influence on the dependent variable (Sigaroodi et al 2013). So, we considered a MLR equation by using these significant climate indices in diffirent lag-times.…”

Section: Cross-correlation and Mlr Prediction Resultsmentioning

confidence: 99%

“…According the Sigaroodi et al (2013) This study indicated that regression model cannot predict Standard Deviation (SD) or fluctuations of observation data ( Figure 5). So, MLR is not capable enough to predict the wet years and droughts.…”

Section: Discussionmentioning

confidence: 98%

Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

Choubin

Khalighi-Sigaroodi

Malekian

et al. 2016

Hydrological Sciences Journal

148

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Nowadays mathematical models are widely used to predict climate processes, but less has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer perceptron (MLP) network and adaptive neuro-fuzzy inference system (ANFIS) models were compared in precipitation prediction. The large-scale climate signals were considered as inputs to the applied models. After selecting the most effective climate indices, the effects of large-scale climate signals in seasonal Standardized Precipitation Index (SPI) of Maharlu-Bakhtaran catchment, simultaneously and with the delay, was analysed by cross-correlation function.Consequently, SPI time series was forecasted up to four time intervals by using MLR, MLP and ANFIS. The results showed that the most of the indices were significant with SPI of different lag times. Comparison of the SPI forecast results by MLR, MLP and ANFIS models showed better performance of the MLP network than the other two models (RMSE=0.86, MAE=0.74 for the 1st step ahead of SPI forecasting).

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“…The Taylor diagram graphically shows how the prediction models are matched with observations in terms of correlation, their root-mean-square difference, and the ratio of their variance considered in a single diagram [82]. On this diagram, the mode that is closer to the observation (validation dataset) has the highest predictive performance [83]. The abovementioned statistical indexes can be calculated by Equations (7)-(9) as below [84]:…”

Section: Statistical Metricsmentioning

confidence: 99%

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

et al. 2020

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Local scour depth at complex piers (LSCP) cause expensive costs when constructing bridges. In this study, a hybrid artificial intelligence approach of random subspace (RS) meta classifier, based on the reduced error pruning tree (REPTree) base classifier, namely RS-REPTree, was proposed to predict the LSCP. A total of 122 laboratory datasets were used and portioned into training (70%: 85 cases) and validation (30%: 37 cases) datasets for modeling and validation processes, respectively. The statistical metrics such as mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (R), and Taylor diagram were used to check the goodness-of-fit and performance of the proposed model. The capability of this model was assessed and compared with four state-of-the-art soft-computing benchmark algorithms, including artificial neural network (ANN), support vector machine (SVM), M5P, and REPTree, along with two empirical models, including the Florida Department of Transportation (FDOT) and Hydraulic Engineering Circular No. 18 (HEC-18). The findings showed that machine learning algorithms had the highest goodness-of-fit and prediction accuracy (0.885 < R < 0.945) in comparison to the other models. The results of sensitivity analysis by the proposed model indicated that pile cap location (Y) was a more sensitive factor for LSCP among other factors. The result also depicted that the RS-REPTree ensemble model (R = 0.945) could well enhance the prediction power of the REPTree base classifier (R = 0.885). Therefore, the proposed model can be useful as a promising technique to predict the LSCP.

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Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Cited by 58 publications

References 24 publications

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers

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