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Maryam Rahimzad

Bio: Maryam Rahimzad is an academic researcher from University of Isfahan. The author has co-authored 1 publications.

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Journal ArticleDOI
TL;DR: In this paper, the authors compared the performance of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting.
Abstract: Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent decades. In this study, the main objective was to compare the accuracy of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting. For this purpose, three scenarios were defined based on historical precipitation and streamflow series for 26 years of the Kentucky River basin located in eastern Kentucky, US. Statistical criteria including the coefficient of correlation ( $$R$$ ), Nash-Sutcliff coefficient of efficiency ( $$E$$ ), Nash-Sutcliff for High flow ( $${E}_{H}$$ ), Nash-Sutcliff for Low flow ( $${E}_{L}$$ ), normalized root mean square error ( $$NRMSE$$ ), relative error in estimating maximum flow ( $$REmax$$ ), threshold statistics ( $$TS$$ ), and average absolute relative error ( $$AARE$$ ) were employed to compare the performances of these methods. The results show that the LSTM network outperforms the other models in forecasting daily streamflow with the lowest values of $$NRMSE$$ and the highest values of $${E}_{H}$$ , $${E}_{L}$$ , and $$R$$ under all scenarios. These findings indicated that the LSTM is a robust data-driven technique to characterize the time series behaviors in hydrological modeling applications.

40 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper , the authors compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting.
Abstract: Hydrological forecasting is one of the key research areas in hydrology. Innovative forecasting tools will reform water resources management systems, flood early warning mechanisms, and agricultural and hydropower management schemes. Hence, in this study, we compared Stacked Long Short-Term Memory (S-LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), and Gated Recurrent Unit (GRU) with the classical Multilayer Perceptron (MLP) network for one-step daily streamflow forecasting. The analysis used daily time series data collected from Borkena (in Awash river basin) and Gummera (in Abay river basin) streamflow stations. All data sets passed through rigorous quality control processes, and null values were filled using linear interpolation. A partial autocorrelation was also applied to select the appropriate time lag for input series generation. Then, the data is split into training and testing datasets using a ratio of 80 : 20, respectively. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) were used to evaluate the performance of the proposed models. Finally, the findings are summarized in model variability, lag time variability, and time series characteristic themes. As a result, time series characteristics (climatic variability) had a more significant impact on streamflow forecasting performance than input lagged time steps and deep learning model architecture variations. Thus, Borkena’s river catchment forecasting result is more accurate than Gummera’s catchment forecasting result, with RMSE, MAE, MAPE, and R2 values ranging between (0.81 to 1.53, 0.29 to 0.96, 0.16 to 1.72, 0.96 to 0.99) and (17.43 to 17.99, 7.76 to 10.54, 0.16 to 1.03, 0.89 to 0.90) for both catchments, respectively. Although the performance is dependent on lag time variations, MLP and GRU outperform S-LSTM and Bi-LSTM on a nearly equal basis.

16 citations

Journal ArticleDOI
01 Sep 2022-Catena
TL;DR: In this article , the authors proposed a novel step-wise binary prediction framework for the susceptibility assessment of geo-hydrological hazards specific to floods and landslides in the Kentucky River basin, United States.
Abstract: This research proposes a novel step-wise binary prediction framework for the susceptibility assessment of geo-hydrological hazards specific to floods and landslides. The framework of the study comprises two major steps: prediction of geo-hydrological hazard-prone locations (Step-1: hazard/non-hazard), and classification of geo-hydrological hazards by identifying the locations of floods and landslides separately (Step-2: floods/landslides). We used 1326 historically experienced hazard locations (i.e., 726 for floods and 690 for landslides) in the Kentucky River basin, United States, along with the 13 hazard conditioning factors. Extremely randomized trees (ERT) coupled with the particle swarm optimization (PSO) was adopted to provide an effective classification scheme. Based on the predictions of the ERT-PSO in the first step, correctly classified hazard instances were used in the second step of the prediction task to further deepen the machine learning application. The results revealed a strong agreement between the predicted and observed hazard locations with an AUROC of 0.8032 and 0.8845 for geo-hydrological hazard (Step-1) and flood/landslide classifications (Step-2), respectively. The proposed hybrid prediction framework introduced considerably accurate performance as 73.78% and 72.91% of the hazard and non-hazard classes were correctly identified at Step-1, respectively, while at Step-2, 72.31% of the flooding points and 84.85% of the landslide points were ascertained accurately. Overall findings emerged from Step-1 illustrated that nearly 10% of the entire basin is susceptible to geo-hydrological hazards with very high probability, whereas very low susceptible areas cover only 20% of the basin. A model-agnostic game-theory based SHapley Additive exPlanations (SHAP) algorithm was employed to anatomize the contribution of hazard conditioning factors on the incident outcome predictions aiding to increase the interpretability of the adopted methodology. The holistic approach adopted in the present research has significant potential in providing insights into the practical and theoretical grounds of the literature.

13 citations

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a novel hybrid modelling framework integrating Long Short Term Memory (LSTM) and Multivariate Empirical Mode Decomposition (MEMD) aided with Time Dependent Intrinsic Cross-Correlation (TDICC) analysis algorithm for monthly rainfall predictions.

12 citations

Journal ArticleDOI
TL;DR: The results reveal that the LSTM-GWO method has a better ability in estimating Epan using limited inputs compared to other ML and empirical methods and indicate that an increase in the amount of training data used improves the accuracy of the models.
Abstract: ABSTRACT Estimation of pan evaporation (Epan) is an important issue for planning and management of available water resources. In the present study, the accuracy of a new deep learning method, long short-term memory (LSTM) with grey wolf optimization (GWO), in modelling Epan using limited climatic variables as input is investigated. The outcomes of the LSTM-GWO are compared with the single LSTM and advanced machine learning (ML) methods. Minimum and maximum temperatures and extra-terrestrial radiation are used as inputs to the models. Three data splitting scenarios are considered and the outcomes of the abovementioned methods are also compared with the Stephen-Stewart (SS) and calibrated Hargreaves-Samani (CHS) empirical methods. The results reveal that the LSTM-GWO method has a better ability in estimating Epan using limited inputs compared to other ML and empirical methods. They also indicate that an increase in the amount of training data used improves the accuracy of the models.

8 citations