Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling

Mohammadi, Babak; Ahmadi, Farshad; Mehdizadeh, Saeid; Guan, Yiqing; Pham, Quoc Bao; Linh, Nguyễn Thị Thùy; Tri, Doan Quang

doi:10.1007/s11269-020-02619-z

Cited by 66 publications

(19 citation statements)

References 52 publications

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“…So, in this study, the initial point of view to select of the decomposition level was taken from L but since many seasonal characteristics may be embedded in hydrological signals, 2-8 resolution levels (L ± x) for the daily and 2-5 resolution levels (L ± x) for the monthly modeling were examined via the proposed WANN and WES models which, respectively, denote to the 2 2 -day mode and 2 3 -day mode (which is nearly weekly mode), 2 4 -day mode (which is nearly semimonthly mode), 2 5 -day mode (which is nearly monthly mode), 2 6 -day mode, 2 7 -day mode (which is nearly semiyearly mode), and 2 8 -day mode (which is nearly yearly mode) in the daily scale and 2 2 -month mode, 2 3 -month, 2 4 month, and 2 5 -month mode in the monthly scale. Besides, the Daubechies 4 wavelet (db4) that has been frequently assessed in hydrological modeling was considered as the mother wavelet in this study.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, many researchers are developing new models to improve streamflow modeling. For instance, recently, new models were developed for streamflow forecasting by improving artificial intelligence (AI) models [6,7]. Accurate modeling of the streamflow is a significant step in any study for a river improvement and management of the related watershed and includes highly nonlinear and interacting components that cause complexity.…”

Section: Introductionmentioning

confidence: 99%

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Using Hybrid Wavelet‐Exponential Smoothing Approach for Streamflow Modeling

et al. 2021

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Considering the three intrinsic components (of autoregressive, seasonality, and error) of streamflow time series, the overall performance of the streamflow modeling tool is associated with the correct estimation of these components. In this study, a new hybrid method based on the wavelet transform (WT) as a multiresolution forecasting tool and exponential smoothing (ES) method, with two presented scenarios (WES1 and WES2), was introduced. To this end, the performance of the proposed method was investigated versus four conventional methods of the autoregressive integrated moving average (ARIMA), ES ad-hoc, artificial neural network (ANN), and wavelet-ANN (WANN) for daily and monthly streamflow modeling of West Nishnabotna and Trinity River watersheds with different hydro-geomorphological conditions. In the presented WES technique, firstly, WT is employed for decomposing the observed signal to one approximation (deterministic trend) and more diverse components of subseries (each at a specific frequency). Then, for the first scenario (WES1), only two subseries are introduced to the model as input parameters; however, for the second scenario (WES2), decomposed subseries are separately used as the inputs of ES models. The obtained results indicated that combining WT with the ES method and ANN led to more accurate modeling. The proposed methodology (WES2) that used all decomposed subseries separately improved the efficiency of models up to 30% and 10% for the daily dataset and up to 88% and 57% for the monthly dataset, respectively, for the West Nishnabotna and Trinity Rivers.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Hybrid Wavelet‐Exponential Smoothing Approach for Streamflow Modeling

et al. 2021

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“…The results are transferred to the next layer that is per se employed as an input layer for the next layer of the network [40]. For more information, refer to articles by Ivakhnenko [44], Aghelpour and Varshavian [45], Ashrafzadeh et al [46] and Aghelpour et al [47] 2.4.…”

Section: Group Methods Of Data Handling (Gmdh)mentioning

confidence: 99%

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Aghelpour

Mohammadi

Biazar

et al. 2020

IJGI

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Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.

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“…This is accomplished by connecting neurons from one layer to another one (previous or next layer). The output of each neuron is multiplied by weight coefficients and given as the input for a nonlinear excitation function [32]. In the training phase, the training data are given to the perceptron, and the grid weights are then adjusted to minimize the error between the target and output of the model, or to reach the default number of training times.…”

Section: Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

et al. 2020

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Snow is one of the essential factors in hydrology, freshwater resources, irrigation, travel, pastimes, floods, avalanches, and vegetation. In this study, the snow cover of the northern and southern slopes of Alborz Mountains in Iran was investigated by considering two issues: (1) Estimating the snow cover area and the (2) effects of droughts on snow cover. The snow cover data were monitored by images obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The meteorological data (including the precipitation, minimum and maximum temperature, global solar radiation, relative humidity, and wind velocity) were prepared by a combination of National Centers for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) points and meteorological stations. The data scale was monthly and belonged to the 2000–2014 period. In the first part of the study, snow cover estimation was conducted by Multiple Linear Regression (MLR), Least Square Support Vector Machine (LSSVM), Group Method of Data Handling (GMDH), Multilayer Perceptron (MLP), and MLP with Grey Wolf Optimization (MLP-GWO) models. The most accurate estimations were produced by the MLP-GWO and GMDH models. The models produced better snow cover estimations for the northern slope compared to the southern slope. The GWO improved the MLP’s accuracy by 10.7%. In the second part, seven drought indices, including the Palmer Drought Severity Index (PDSI), Bahlme–Mooley Drought Index (BMDI), Standardized Precipitation Index (SPI), Multivariate Standardized Precipitation Index (MSPI), Modified Standardized Precipitation Index (SPImod), Joint Deficit Index (JDI), and Standardized Precipitation-Evapotranspiration Index (SPEI) were calculated for both slopes. The results showed that the effects of a drought event on the snow cover area would remain up to 5 (or 6) months in the region. The highest impact of drought appears after two months in the snow cover area, and the drought index most related to snow cover variations is the 2–month time window of SPI (SPI2). The results of both subjects were promising and the methods can be examined in other snowy areas of the world.

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Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling

Cited by 66 publications

References 52 publications

Using Hybrid Wavelet‐Exponential Smoothing Approach for Streamflow Modeling

Using Hybrid Wavelet‐Exponential Smoothing Approach for Streamflow Modeling

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area

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