Hossein Emami; Ali Salajegh; Alireza Moghaddamnia; Shahram Khalighi; Abolhassan Fathbabadi
Abstract
Precipitation is of the most important inputs of runoff modeling. The availability of precipitation data with appropriate temporal and spatial accuracy is very important and necessary for watersheds with small and scattered rainfall stations. Nowadays, climatic satellites are practical and widely-used ...
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Precipitation is of the most important inputs of runoff modeling. The availability of precipitation data with appropriate temporal and spatial accuracy is very important and necessary for watersheds with small and scattered rainfall stations. Nowadays, climatic satellites are practical and widely-used tools in precipitation estimations. In this study, first the efficiency of TRMM satellite precipitation data in the monthly time series of Chehelchai Watershed was evaluated using R2, RMSE, NSE and Bias statistical indices by comparing the precipitation data of rain gauge stations (observed) and the values of these statistical indices were 0.54, 22.70, 0.44 and -14.86, respectively. Considering the value of the coefficient of determination (R2), it can be concluded that the TRMM satellite was able to estimate the 0.54 of observed precipitation. In the next step, three base data models including MLP, ANFIS and SVR were used to estimate the monthly runoff. Two different input scenarios were selected :1) observed precipitation data in t and t-1 time steps and runoff in t-1 time step and 2) satellite precipitation data in t and t-1 time steps and runoff in t-1 time step. To compare the accuracy and error of the models, R2 and RMSE of the validation stage were used. The ANFIS model with the values of R2 and RMSE were 0.80 and 0.97 for the first type input combination and 0.78 and 1.02 for the second type input combination, respectively, as the suitable single model for estimating runoff in the study area were selected. Then weighted-mean method was used in the data fusion approach to provide a data driven combination model for each combination of inputs into the model in the studied watershed. This data fusion approach data-driven model improved the values (R2=0.81) and (Bias=-4.85) for the first type input combination and also improved the value (R2=0.79) for the second type input combination.
Aboalhasan Fathabadi; Vahid Anamoradi
Abstract
In hydrological models, in order to better model the runoff process, it is necessary to calibrate the model using observational data. In the process of calibration of hydrological models, in addition to the quality of observation data and the optimization algorithm, the objective function also affects ...
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In hydrological models, in order to better model the runoff process, it is necessary to calibrate the model using observational data. In the process of calibration of hydrological models, in addition to the quality of observation data and the optimization algorithm, the objective function also affects the efficiency of the model. In most studies, statistical criteria such as NSE and RMSE are used as objective functions in the calibration process of hydrological models. Given the structure of the model and the relationships used in each of the evaluation criteria, each of them has good performance in simulating a part of the hydrograph. One of the important parameters of each basin, which is a kind of basin reaction indicator for different discharge values, is the Flow Continuity Curve (FDC). In this study, the efficiency of objective functions based on flow continuity curve and statistical objective functions in optimizing the parameters of the HBV hydrological model in Ziyarat Watershed of Golestan Province was investigated and compared. After introducing input data to model using DDS algorithm, model was calibrated 100 times for each objective function. When model was calibrated, using optimized parameter sets model output for calibration and validation period was obtained. Results showed that criteria such as NSE and KGE have better performance in predicting high flows, criteria such as RMSE and AME predicted moderate flow discharge better and criteria based on FDC had better performance in predicting low flows. In prediction different parts of hydrograph FDC objective function has the best performance, RMSE and MAE were in sound order and NSE and KGE did not have suitable performance.
Aboalhasan Fathbabadi; Mahnaz Kohneshin; Ali Heshmatpour; Masome Farasati
Abstract
During last decades hydrological models were extesively used in rainfall-runoff modeling. These models contain some constant parameters that must be optimized through appropraite mthods. In addation to model structur, the efficieny of hydrological models depend on these optimized parameters. In this ...
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During last decades hydrological models were extesively used in rainfall-runoff modeling. These models contain some constant parameters that must be optimized through appropraite mthods. In addation to model structur, the efficieny of hydrological models depend on these optimized parameters. In this study, the efficiency of three automatic optimizing algorithms including Dynamically Dimensioned Search (DDS), Shuffled Complex Evolution and Genetic algorithms in calibration lumped hydrological model HyMod in Ghorchay Ramian Catchment were investigated. For these mehods, convergence speed and variability of final optimized values were investigated. Results showed that Genetic algorithm converged faster than two other methods. Following, DDS algorithm converged faster than Shuffled Complex Evolution algorithm. Shuffled Complex Evolution and Genetic algorithms took shorter and longer time per each epock, respectively. Highest and the least variability of final results were obtained for DDS and Shuffled Complex Evolution algorithms, respectively. With respect to final results variability, Shuffled Complex Evolution algorithm was more satable and had better performance than other methods. Using analyse variance and comper means in Shuffled Complex Evolution algorithms for complexes less than 12, the model performance was increased as the number of complexe increased. As alpha value increased, the model performance decreased and model had the best performance at the value of 0.58. Conversely, model performance was increase as beta values increasd and the best perfromnce was obtained for beta equal to 1. For Genetic algorithm, the best performance was obtained when the value of values crossover, mutation and chromosome number was equal to 0.2 and 0.3 and 16, respectively.
Meisam Samadi; Abdolreza Bahremand; Abolhasan Fathabadi
Abstract
In any water resource management plan, there is a pivotal need to undertake the future conditions to allocate the water resources to different sectors (e.g. drinking-water supply, agriculture sector, etc.) more efficiently. Meanwhile, it is important to forecast water resources inflow for future months. ...
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In any water resource management plan, there is a pivotal need to undertake the future conditions to allocate the water resources to different sectors (e.g. drinking-water supply, agriculture sector, etc.) more efficiently. Meanwhile, it is important to forecast water resources inflow for future months. To this aim, it is of prime interest to adopt models that are capable of coping with data scarcity problem and able to forecast the stream flow with the least possible error. The current study was aimed at forecasting the monthly inflow of the Boustan Dam by employing three models namely: time series method, Artificial Neural Network (ANN), Support Vector Machine (SVM), and their ensembles. The hydrometric data was obtained from the Tamar Station. Afterward, the models were compared by using several evaluation criteria. According to the Akaike and Schwarz criteria, the ARIMA (2, 0, 0) (1, 0, 1) was found to be the best time series model with a parsimonious behavior. Moreover, the ANN model with two and four input neurons and the SVM model with three input neurons were the best performing models compared to their other counterparts with different input numbers. Considering the evaluation criteria altogether, the time series method was the best performing model with the RMSE, AARE, MBE, and CE values of 0.88, 4.71, -0.024, and 0.36, respectively. Therefore, the time series method was introduced as the premier model for monthly inflow forecasting in the studied stations.
