Development of generalized feed forward network for predicting annual flood (depth) of a tropical river

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable

A review on theoretical consideration and types of models in hydrology

The conception of modeling in hydrology is involved with relationships of water, climate, soil and land use. Moreover, hydrological models include temporal and spatial features. Behavior of each feature controlled by its own and therefore it makes a vast variety for types of hydrological models. Hydrological models are the main tools for hydrologists with different purposes to use such as water resource management, ground water modeling, urban and rural watershed management and so on. Many hydrological models have been developed and refined during the past four decades and it is required to fully understand their characteristics to effortlessly employ them. Therefore, hydrologists need to familiarize themselves with the classification of hydrological models and understand the theoretical definition behind them. However, in regard to this issue, only a few discrete studies had been done. Classification of hydrological models is not exact and different hydrologist may give different definitions. The reason is that the nature of models is often the same but many models have overlapping characteristics. Thus, this study was aimed at showing the dominant classifications for hydrological models alongside the different views from past to present but generally, they have common meaning even though they may be classified under different categories. In addition, although there are overlapping features in different hydrological models, their nature is not that hard to understand

Streamflow modeling of a large arid catchment using semi-distributed hydrological model and modular neural network

Calibration and validation of hydrological models for simulating stream flow can usually be a promising procedure for future sustainable watershed development. Therefore, development of hydrological models with attributed capabilities is vital to explore the models. Recently, arid climate regions are facing critical water resource problems due to elevated water scarcity. The main objective of this research is to compare the Soil and Water Assessment Tool (SWAT), a knowledge driven by semi-distributed hydrological model, with the Modular Neural Network (MNN), a data driven technique, in predicting the daily flow in arid and large scale. Development of SWAT required digital elevation map, hydro-meteorological data, land use map, and soil maps; whilst, the MNN only needed hydro-meteorological data. For both models, a sensitivity analysis that included both calibration and validation with individual uncertainty evaluation methods was carried out. Generally, results for relative errors such as Nash-Sutcliffe, coefficient of determination and percent of bias favored the SWAT for the validation period. Not only that, the absolute error criteria such as root mean square error, mean square error and mean relative error obtained were close to zero for the SWAT as well within the same period. The mean absolute error for both models was similar during the validation period. Results of the uncertainty evaluation were in satisfactory range. Both models had given similar trend for flow prediction during the validation period. Results of box plot, according to 50% (median) of daily flow, showed that both models had respectively overestimated (MNN) and underestimated (SWAT) the daily flow during validation period. Evaluation on runoff volume for each year showed that both models had a one-year underestimation and three-year overestimation in the same period. However, the overestimation of MNN was more obvious. As a conclusion, this study showed that both models have promising prediction performance for daily flow in a large scale watershed with arid climat

An assessment on base and peak flows using a physically-based model

A physically-based model namely the Soil Water Assessment Tool (SWAT) was used on the Roodan watershed in southern part of Iran; the watershed has an area of 10570 km2. The main objectives were to simulate monthly discharge and evaluate the base and peak flows separately. Required parameters to run the model were meteorological data, soil type, land use, management practices and topography maps at watershed scale. To find the sensitive parameters, an initial sensitivity analysis was performed using the Latin Hypercube sampling One-at-A-Time (LH-OAT) method embedded in the SWAT model. Then, the model was calibrated and validated for stream flow using the SWAT-CUP program. Generally, the model was assessed using the modified coefficient of determination (bR2), Nash-Sutcliffe (NS) and PBIAS. Values of bR2 and NS were 0.93 and 0.92 for calibration respectively and 0.69 and 0.83, respectively, for validation. For calibration and validation, PBIAS were obtained at 23 and 5%, respectively. Reviewing the results, it seems that simulation of the monthly peak flows has better harmony (fluctuation) than monthly base flows for Roodan watershed. To summarize, the simulated SWAT stream flow was within the acceptable range for Roodan watershed as an arid catchment

An assessment of a proposed hybrid neural network for daily flow prediction in arid climate

Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear relationships using neural networks. In this study, a hybrid network presented as a feedforward modular neural network (FF-MNN) has been developed to predict the daily rainfall-runoff of the Roodan watershed at the southern part of Iran. This FF-MNN has three layers - input, hidden, and output. The hidden layer has two types of neural expert or module. Hydrometeorological data of the catchment were collected for 21 years. Heuristic method was used to develop the MNN for exploring daily flow generalization. Two training algorithms, namely, backpropagation with momentum and Levenberg-Marquardt, were used. Sigmoid and linear transfer functions were employed to explore the network's optimum behavior. Cross-validation and predictive uncertainty assessments were carried out to protect overtiring and overparameterization, respectively. Results showed that the FF-MNN could satisfactorily predict stream flow during testing period. The Nash-Sutcliff coefficient, coefficient of determination, and root mean square error obtained using MNN during training and test periods were 0.85, 0.85, and 39.4 and 0.57, 0.58, and 32.2, respectively. The predictive uncertainties for both periods were 0.39 and 0.44, respectively. Generally, the study showed that the FF-MNN can give promising prediction for rainfall-runoff relation

Modeling daily stream flow using plant evapotranspiration method

In hydrological models, soil conservation services (SCS) are one of the most widely used procedures to calculate the curve number (CN) in rainfall run-off simulation. Recently, another new CN accounting procedure has been mentioned, namely the plant evapotranspiration (ET) method or simply known as the plant ET method. This method is embedded in the Soil and Water Assessment Tool (SWAT) model which has been developed for watersheds covered by shallow soils or soils with low storage characteristics. It uses antecedent climate and plant evapotranspiration for calculation of daily curve number. In this study, the same method had been used to simulate the daily stream flow for Roodan watershed located in the southern part of Iran. The watershed covers 10570 km2 and its climate is arid to semi-arid. The modeling process required data from digital elevation model (DEM), land use map, and soil map. It also required daily meteorological data which were collected from weather stations from 1988 to 2008. Other than that, the Sequential Uncertainty Fitting-2 (SUFI-2) algorithm was utilized for calibration and uncertainty analysis of daily stream flow. Criteria of modeling performance were determined through the Nash-Sutcliffe and coefficient of determination for calibration and validation. For calibration, the values were reported at 0.66 and 0.68 respectively and for validation; the values were 0.51 and 0.55. Moreover, percentiles of absolute error between observed and simulated data in calibration and validation period were calculated to be less than 21.78 and 6.37 (m3/s) for 95% of the data. The results were found to be satisfactory under the climatic conditions of the study area

An assessment on base and peak flows using a physically-based model.

Abstract: A physically-based model namely the Soil Water Assessment Tool (SWAT) was used on the Roodan watershed in southern part of Iran; the watershed has an area of 10570 km 2 . The main objectives were to simulate monthly discharge and evaluate the base and peak flows separately. Required parameters to run the model were meteorological data, soil type, land use, management practices and topography maps at watershed scale. To find the sensitive parameters, an initial sensitivity analysis was performed using the Latin Hypercube sampling One-at-ATime (LH-OAT) method embedded in the SWAT model. Then, the model was calibrated and validated for stream flow using the SWAT-CUP program. Generally, the model was assessed using the modified coefficient of determination (bR 2 ), Nash-Sutcliffe (NS) and PBIAS. Values of bR 2 and NS were 0.93 and 0.92 for calibration respectively and 0.69 and 0.83, respectively, for validation. For calibration and validation, PBIAS were obtained at 23 and 5%, respectively. Reviewing the results, it seems that simulation of the monthly peak flows has better harmony (fluctuation) than monthly base flows for Roodan watershed. To summarize, the simulated SWAT stream flow was within the acceptable range for Roodan watershed as an arid catchment

Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran

The present study compares the results of the Soil and Water Assessment Tool (SWAT) with a Support Vector Machine (SVM) to predict the monthly streamflow of arid regions located in the southern part of Iran, namely the Roodan watershed. Data collected over a period of 19 years (1990–2008) was used to predict the monthly streamflow. Calibration (training) and validation (testing) were performed within the same period for both the models after the preparation of the required data. A semi auto-calibration was performed for the SWAT model. Also, the best input combination of the SVM model was identified using the Gamma Test (GT). Finally, the reliability of the SWAT and SVM models were evaluated based on performance criteria such as the Nash-Sutcliffe (NS) model efficiency coefficient and the Root Mean Square Error (RMSE). The obtained results from the development of the SWAT model and SVM model indicated satisfying performance in predicting the monthly streamflow in the large arid region. The SWAT obtained NS and RMSE values of 0.83 and 6.1 respectively, and the SVM obtained NS and RMSE values of 0.84 and 6.75 respectively for the validation (testing) period. Results indicate that for high flows of more than 19 (m3/s), both models predict flow with over and under estimation in the validation (testing) period. Moreover, the SVM has a closer value for the average flow in comparison to the SWAT model; whereas the SWAT model outperformed for total runoff volume with a lower error in the validation period

Optimal Calibration and Uncertainty Analysis of SWAT for an Arid Climate

One of the major issues for semidistributed models is calibration of sensitive parameters. This study compared 3 scenarios for Soil and Water Assessment Tool (SWAT) model for calibration and uncertainty. Roodan watershed has been selected for simulation of daily flow in southern part of Iran with an area of 10 570 km(2). After preparation of required data and implementation of the SWAT model, sensitivity analysis has been performed by Latin Hypercube One-factor-At-a-Time method on those parameters which are effective for flow simulation. Then, SWAT Calibration and Uncertainty Program (SWAT-CUP) has been used for calibration and uncertainty analysis. Three schemes for calibration were followed for the Roodan watershed modeling in calibration analysis as evolution. These include the following: the global method (scheme 1), this is a method that takes in all globally adjusted sensitive parameters for the whole watershed; the discretization method (scheme 2), this method considered the dominant features in calibration such as land use and soil type; the optimum parameters method (scheme 3), this method only adjusted those sensitive parameters by considering the effectiveness of their features. The results show that scheme 3 has better performance criteria for calibration and uncertainty analysis. Nash-Sutcliffe (NS) coefficient has been obtained 0.75 for scheme 3. However, schemes 1 and 2 resulted in NS 0.71 and 0.74, respectively, between predicted and observed daily flows. Moreover, percentage bias (P-bias) obtained was 6.7, 5.2, and 1.5 for schemes 1, 2, and 3, respectively. The result also shows that condition of parameters (parameter set) during calibration in SWATCUP program model has an important role to increase the performance of the model

Comparison of semi-distributed, GIS-based hydrological models for the prediction of streamflow in a large catchment

Predicting streamflow in a large arid and semi-arid basin is of great importance in understanding the availability of water for spatial planning and water resource management. In this study, two geographic information system-based (GIS-based) semi-distributed hydrological models were compared for predicting flow. TOPMODEL and SWAT require the use of a GIS to process input data obtained from various sources, such as the digital elevation model (DEM), topographic index (TI), hydrologic response unit (HRU), meteorological stations, and soil- and land-use maps. Daily hydro-meteorological data were collected from 1989 to 2007, and 90-m resolution of DEM was considered. The models were compared, and their performances for the prediction of peak flows and runoff volumes were discussed. TOPMODEL and SWAT obtained good coefficient values for the validation period, i.e., 0.61 and 0.68, respectively. According to relative error percentage (RE %) criteria, TOPMODEL provided a promising value for the validation period (64 %) for peak flows, whereas SWAT provided about 70 %. TOPMODEL provided 5-year overestimation and 1-year underestimation for runoff volume; SWAT provided 2-year underestimation and 4-year overestimation. For this study, both models obtained promising simulation results for surface flow