Papers by MILAD Jajarmizadeh
Air, Soil and Water Research, 2017
One of the major issues for semidistributed models is calibration of sensitive parameters. This s... more 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 km2. 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.
Journal of Applied Sciences, 2013
Flood Frequency Analysis Based on Gaussian Copula
Flood duration, volume, and peak flow are important considerations in flood risk analysis and man... more Flood duration, volume, and peak flow are important considerations in flood risk analysis and management of hydraulic structures. The conventional flood frequency analysis assumed that the marginal distribution functions of flood parameters follow a certain pattern. However, such assumption is impractical because a flood event is multivariate and the flood parameter distributions can be different. These discrepancies were addressed using bivariate joint distributions and Copula function which allow flood parameters having different marginal distributions to be analyzed simultaneously. The analysis used hourly stream flow data for 45 years recorded at the Rantau Panjang gauging station on the Johor River in Malaysia. It was found that flood duration and volume are best fitted by the generalized extreme value distribution while peak flow by the Generalized Pareto. Inference function for margin (IFM) method was applied to model the joint distributions of correlated flood variables for each pair and the results showed that all the calculated θ values were in acceptable range of Gaussian Copula. By horizontally cutting the joint cumulative distribution function (CDF), a set of contour lines were obtained for Gaussian Copula which represented the occurrence probabilities for the joint variables. Also the joint return period for pair of flood variables was calculated.
Identification of Vulnerable Regions for TNB’s Electric Substations during Flood in Peninsular Malaysia
Flood is usually an environmental hazard which has been increased in recent years by forcing the ... more Flood is usually an environmental hazard which has been increased in recent years by forcing the pushing factors such as climate change and urbanization. This study presents flood-prone area related to the electric substations for Tenaga Nasional Berhad (TNB) in Peninsular Malaysia. The objective of this research was to identify the related regions that the electric substations are in vulnerable condition by flood risk. For this research, two types of maps are generated, namely flood inundation map (FIM), which indicates the area and capacity of the flood, and flood hazard map (FHM), which provides the area, depth, velocity, and extension of the flood for the TNB’s location of substation. For this issue, different classes of substations are involved in analysis, namely transmission main intake (PMU), main distribution (PPU), main switching station (SSU), and distribution substation (PE). An integration of TNB’s substation maps performed with FIM and FHM due to identify substations which are in flood-prone regions. Generally, result shows that Kelantan is classified as the highest flood-prone region for TNB’s infrastructures especially for PMU which they are affected by flood. Kelantan, Terengganu, and Perlis are involved with the highest flooded, respectively, based on PPU and SSU infrastructure. Finally, for PE substations, Kelantan, Perlis, and Terengganu have the highest contribution for flooded substations for TNB’s structures.
An Overview: Flood Catastrophe of Kelantan Watershed in 2014
One of the challenging topics in Malaysia is flood occurrence, which have important impacts in hu... more One of the challenging topics in Malaysia is flood occurrence, which have important impacts in human life and socioeconomic subjects. Malaysia, periodically, have faced with huge floods since previous years. Kelantan river basin, which located in the northeast of Peninsular Malaysia, is prone to flood events in Malaysia. Kelantan River has been badly affected with flood during recent monsoon season on December 2014 due to heavy monsoons rainfall and climate change issues. In this study, available rainfall and water-level data are analyzed and presented based on the flood event on December 2014. Generally, the flood area affected includes the districts of Kota Bharu, Kuala Krai, Machang, Pasir Mas, Pasir Puteh, Tanah Merah, Gua Musang, and Tumpat at Kelantan State. In the northeast monsoon season, the Kelantan State suffers from two phase of flood. The first phase began on December 14–17, 2014, and the second phase occurred on December 20–24, 2014. A comparison between accumulated rainfall on December and whole year of 2014 at Gagau station shows that contribution of rainfall on December is roughly 50 % of all of 2014. Overview of water-level results at Kelantan watershed shows that all areas are involved with highest record in 2014 in comparison with previous decades except Golok area. Results of water-level ranges show that most of the parts of Kelantan watershed are involved with over danger values for flood in 2014, which Lebir and Kelantan rivers have high increasing. In conclusion, it is suggested that there is a need to have study on flood mitigation and recognition of critical hydrological phenomena for sustainable strategies in Kelantan watershed. Consequently, this research provides primary information as baseline study for upcoming research for water resource management projects.
Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data
Springer
Meteorological data are key variables for hydrologists to simulate the rainfall-runoff process us... more Meteorological data are key variables for hydrologists to simulate the rainfall-runoff process using hydrological models. The collection of meteorological variables is sophisticated, especially in arid and semi-arid climates where observed time series are often scarce. Climate Forecast System Reanalysis (CFSR) Data have been used to validate and evaluate hydrological modeling throughout the world. This paper presents a comprehensive application of the Soil and Water Assessment Tool (SWAT) hydrologic simulator, incorporating CFSR daily rainfall-runoff data at the Roodan study site in southern Iran. The developed SWAT model including CFSR data (CFSR model) was calibrated using the Sequential Uncertainty Fitting 2 algorithm (SUFI-2). To validate the model, the calibrated SWAT model (CFSR model) was compared with the observed daily rainfall-runoff data. To have a better assessment, terrestrial meteorological gauge stations were incorporated with the SWAT model (Terrestrial model). Visualization of the simulated flows showed that both CFSR and terrestrial models have satisfactory correlations with the observed data. However, the CFSR model generated better estimates regarding the simulation of low flows (near zero). The results of the uncertainty analysis showed that the CFSR model predicted the validation period more efficiently. This might be related with better prediction of low flows and closer distribution to observed flows. The Nash-Sutcliffe (NS) coefficient provided good- and fair-quality modeling for calibration and validation periods for both models. Overall, it can be concluded that CFSR data might be promising for use in the development of hydrological simulations in arid climates, such as southern Iran, where there are shortages of data and a lack of accessibility to the data
“Gheorghe Asachi” Technical University of Iasi, Romania , 2016
Soil and water are the two major resources in the Earth's hydro biological and geological systems... more Soil and water are the two major resources in the Earth's hydro biological and geological systems. The hydrology of arid areas has become a topic of interest recently for hydrologists as water shortage at these areas can affect the agriculture, irrigation, and industry as a whole. This has also prompted water resource planners to more thoroughly investigate water resource crisis at arid areas. In this respect, the Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, can be a subsidiary tool to be used in the prediction of surface runoff (blue water). This paper presents the application of SWAT on the Roodan watershed, which is located in the southern part of Iran and has 215 mm of annual precipitation. SWAT was engaged to know more about the daily flow and to evaluate the runoff volume. Three continuous scenarios were defined over the 21 years (1988-1992, 1993-2001, 2002-2008) for the land use map as it was found that continuous update of this layer were basically done during these periods. Results of sensitivity analysis showed that parameters related to transmission losses are most sensitive for this watershed. Furthermore, the SWAT had also visualized from the input data that the sub basins which have been designated for agricultural activities from 1988 to 2008 were at the southwest, center and northeast parts of Roodan watershed. Strength of modeling was evaluated by percentage of observations covered by the 95 Percentage prediction uncertainty (P-factor) and relative width of 95 % probability band (R-factor). The P and R factors in this study were recorded at, for calibration and validation periods, 50 % and 0.18 (calibration), and 50 % and 0.17 (validation) respectively. Nash-Sutcliffe and PBIAS obtained for calibration period were 0.75 and 1.5 %, and those for validation period were 0.64 and 21 %. However, results showed an underestimation trend for most peak flows during the modeling of daily stream flow. Nevertheless, the annual runoff volume for calibration and validation periods depicted a promising performance and thus validated the usage of SWAT as a subsidiary hydrological tool for water management projects attributed with stream flow and runoff volume.
Modeling of rainfall-runoff relationship in a watershed is of prime importance to water resources... more Modeling of rainfall-runoff relationship in a watershed is of prime importance to water resources
engineers and hydrologists in designing hydraulic structures, flood control plans and stormwater
management strategies. The study site, Sungai Johor watershed, has annual precipitation of about
2500 mm. This study aimed at developing an Artificial Neural Network (ANN) model for annual
peak flow during 1980-2010 without employing exogenous runoff-generating process variables.
ANNs have been known as having the ability to model nonlinear mechanisms. In the present
study, the sensitivity of input data, namely the initial discharge, average temperature, average
evaporation, average wind speed, and average relative humidity were also evaluated. Multi layer
perceptron (MLP) network was chosen for modeling the annual flood/peakflow. The data was
divided into three sets, notably data for training, cross-validation and testing. The data analysis
process involved cleansing, normalization and data division. Next, for the best architecture, the
behavior of the input data was assessed separately. Results showed that the most sensitive input
data were the initial discharge, relative humidity and temperature. The best architecture was
obtained by neurons 3-9-1 (input-hidden-output layer). This was computed by using the linear
tangent hyperbolic axon as transfer function. The best learning algorithm for the hidden and
output layers were conjugate gradient and momentum (back propagation) respectively. For this
study the coefficient of determination (R2), Nash and Sutcliffe (NS) and Root Mean Square error
(RMSE) for validation period were 0.64, 0.6 and 48.9 (m3/s) respectively. This study revealed that
the application of various inputs data together did not significantly improve the modeling
performance in ANN. The use of exogenous variables such as initial flows can be beneficial for
primary evaluation when there is significant missing data or when the data accuracy is
questionable.
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... more 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.
The modeling of rainfall-runoff relationship in a watershed is very important in designing hydrau... more 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.
Engineering and water resource management responses to hydrological variability depend ... more Engineering and water resource management responses to hydrological variability depend on daily,
monthly and yearly timeframes. Annual runoff volume and flows are significant for long-term decisions on planning
water resources and regulatory programs. The objective of this study was to evaluate the average annual discharge
and runoff volume derived from different time steps run by SWAT (Soil and Water Assessment Tool) as hydrologic
simulator in order to explore the impact of different time step run simulations on yearly runoff yield. Three scenarios
has been performed namely Annual (D), Annual (M) and Annual (Y). Annual (D) and Annual (M) are related with
derived average annual flow from daily and monthly run simulations by SWAT. Annual (Y) is yearly simulation run
via SWAT. The Nash-Sutcliffe (NS) coefficient, Mean Square Error (MSE) and ratio of the Root-MSE (RSR) on
standard deviation of measured data during validation period were 0.73, 6.3 and 0.5 for Annual (D), 0.82, 4 and 0.38
for Annual (M) and 0.81, 4 and 0.38 for Annual (Y), respectively. Also, relative error (%) for validation period
obtained 0.97, 0.35 and 0.33 for Annual (D), Annual (M) and Annual (Y) scenarios, respectively. The study
concludes that Annual M and Annual Y scenarios obtained closer results in validation period. In regard to relative
error for average runoff volume in each year over modeling period, Annual (D) scenario obtained highest
contribution with shortest relative errors in comparison with the two other scenarios.
The soil and water assessment tool (SWAT) is a physically based model that is used extensively to... more The soil and water assessment tool (SWAT) is a physically based model that is used extensively to simulate hydrologic processes in a wide range of climates around the world. SWAT uses spatial hydrometeorological data to simulate runoff through the computation of a retention curve number. The objective of the present study was to compare the performance of two approaches used for the calculation of curve numbers in SWAT, that is, the Revised Soil Moisture Index (SMI), which is based on previous meteorological conditions, and the Soil Moisture Condition II (SMCII), which is based on soil features for the prediction of flow. The results showed that the sensitive parameters for the SMI method are land-use and land-cover features. However, for the SMCII method, the soil and the channel are the sensitive parameters. The performances of the SMI and SMCII methods were analyzed using various indices. We concluded that the fair performance of the SMI method in an arid region may be due to the inherent characteristics of the method since it relies mostly on previous meteorological conditions and does not account for the soil features of the catchment.
Rainfall-runoff simulation in hydrology using artificial intelligence presents the nonlinear rela... more 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 relations.
Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran
In hydrological models, soil conservation services (SCS) are one of the most widely used procedur... more 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 km 2 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 (m 3 /s) for 95% of the data. The results were found to be satisfactory under the climatic conditions of the study area.
Rainfall-Runoff modelling is considered as one of the major hydrologic processes with a key role ... more Rainfall-Runoff modelling is considered as one of the major hydrologic processes with a key role in predicting flood forecasting and water resources. Furthermore, in order to prevent damages caused by the flood and control and inhibit as well as management and flood alarm, rainfall prediction is inevitable. In the present study, for a 10-year period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009), the rainfall has been predicted using historical rainfall data in Eskandari basin located in Iran using Adaptive Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). In addition, the best input combination was identified using Gamma Test (GT) for the rainfall prediction. Then, runoff discharge produced by the predicted rainfall and the observed rainfall were simulated by a conceptual hydrological model called MIKE11/NAM model and the results were compared together. The results indicated that ANFIS model might be better than ANN model for predicting rainfall. In this study, the efficiency coefficient of NAM model in runoff simulations was 0.7 based on observed rainfall and based on predicted rainfall was 0.58 in Eskandari basin. Moreover, the results indicated that NAM model simulated the base flow more accurate than simulating the peak flow in this basin.
Stream flow forecasting can be an appropriate indicator in estimating future conditions for water... more Stream flow forecasting can be an appropriate indicator in estimating future conditions for water resources management. The present study aimed to compare the efficiency of Support Vector Machine (SVM), Adaptive Neural Fuzzy Inference Systems (ANFIS) and conceptual hydrological model of MIKE11/NAM in simulating the daily stream flow. The studied area is Eskandari basin located in Iran. For this purpose, a ten-year period (1999-2009) of daily data including rainfall, runoff, temperature and evaporation were used. Furthermore, the performances of the models in flow simulation were investigated using statistical indicators of correlation coefficient (R 2 ), Root Mean Square Error (RMSE) and the Nash-Sutcliffe (NS) coefficient. The results showed that every three models possess an appropriate performance and efficiency in the studied area. During testing (verification) period, SVM with the highest correlation coefficient (R 2 =0.99) and lowest RMSE equal to (RMSE=2.13 s m / 3 ), had a better performance than ANFIS model (R 2 =0.82, RMSE=3.21 s m / 3 ) and NAM model (R 2 =0.75, RMSE=3.48 s m / 3 ). In addition, Nash-Sutcliffe coefficient for SVM, ANFIS and NAM models were 0.99, 0.79 and 0.70, respectively.
A physically-based model namely the Soil Water Assessment Tool (SWAT) was used on the Roodan wate... more 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-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 (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.
This study describes the application of a semi-distributed model for flow simulation and assessme... more This study describes the application of a semi-distributed model for flow simulation and assessment of sensitive parameters. Semi-distributed model is a trade-off between fully distributed and lumped models. In this study, the Soil and Water Assessment Tool (SWAT) model was applied for modeling the average monthly flow in Roodan watershed, Iran. This watershed has arid and semi-arid areas critical for development as they have the potential to preserve surface waters in spite of water scarcity. The major purposes of this research were (1) to identify sensitive parameters; and (2) to evaluate the monthly flow at arid region (south of Iran) with low precipitation. To formulate a better model, the impacts of three additional parameters, namely revap coefficient (GW_REVAP), reach evaporation adjustment factor (EVRCH) and length of main channel (CH_L(2)) were reviewed critically. To delineate the watershed, the kind of data used were the digital elevation map (DEM), land use map, soil layers properties and meteorological data. Then, the model was calibrated using the Sequential Uncertainty Fitting (SUFI-2) algorithm. This method is a kind of inverse modeling and considers uniqueness. A modeler defines a large limit range values for every parameter and after every iteration every parameter will get small limit range values. Generally, the model gave satisfactory values of Nash-Sutcliffe (NS) and coefficient of determination (R 2 ). Values of R 2 and NS were 0.93 and 0.92 respectively for calibration. For validation, both values were reported at 0.83. Usually, calibration and validation of hydrological models have different accuracy. The main reason is that the model validate for different phenomena. In such cases, the calibration of additional parameters, i.e. GW_REVAP, EVRCH and CH_L(2), cannot be substantially improved as well.
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Papers by MILAD Jajarmizadeh
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 km2. 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.
engineers and hydrologists in designing hydraulic structures, flood control plans and stormwater
management strategies. The study site, Sungai Johor watershed, has annual precipitation of about
2500 mm. This study aimed at developing an Artificial Neural Network (ANN) model for annual
peak flow during 1980-2010 without employing exogenous runoff-generating process variables.
ANNs have been known as having the ability to model nonlinear mechanisms. In the present
study, the sensitivity of input data, namely the initial discharge, average temperature, average
evaporation, average wind speed, and average relative humidity were also evaluated. Multi layer
perceptron (MLP) network was chosen for modeling the annual flood/peakflow. The data was
divided into three sets, notably data for training, cross-validation and testing. The data analysis
process involved cleansing, normalization and data division. Next, for the best architecture, the
behavior of the input data was assessed separately. Results showed that the most sensitive input
data were the initial discharge, relative humidity and temperature. The best architecture was
obtained by neurons 3-9-1 (input-hidden-output layer). This was computed by using the linear
tangent hyperbolic axon as transfer function. The best learning algorithm for the hidden and
output layers were conjugate gradient and momentum (back propagation) respectively. For this
study the coefficient of determination (R2), Nash and Sutcliffe (NS) and Root Mean Square error
(RMSE) for validation period were 0.64, 0.6 and 48.9 (m3/s) respectively. This study revealed that
the application of various inputs data together did not significantly improve the modeling
performance in ANN. The use of exogenous variables such as initial flows can be beneficial for
primary evaluation when there is significant missing data or when the data accuracy is
questionable.
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.
monthly and yearly timeframes. Annual runoff volume and flows are significant for long-term decisions on planning
water resources and regulatory programs. The objective of this study was to evaluate the average annual discharge
and runoff volume derived from different time steps run by SWAT (Soil and Water Assessment Tool) as hydrologic
simulator in order to explore the impact of different time step run simulations on yearly runoff yield. Three scenarios
has been performed namely Annual (D), Annual (M) and Annual (Y). Annual (D) and Annual (M) are related with
derived average annual flow from daily and monthly run simulations by SWAT. Annual (Y) is yearly simulation run
via SWAT. The Nash-Sutcliffe (NS) coefficient, Mean Square Error (MSE) and ratio of the Root-MSE (RSR) on
standard deviation of measured data during validation period were 0.73, 6.3 and 0.5 for Annual (D), 0.82, 4 and 0.38
for Annual (M) and 0.81, 4 and 0.38 for Annual (Y), respectively. Also, relative error (%) for validation period
obtained 0.97, 0.35 and 0.33 for Annual (D), Annual (M) and Annual (Y) scenarios, respectively. The study
concludes that Annual M and Annual Y scenarios obtained closer results in validation period. In regard to relative
error for average runoff volume in each year over modeling period, Annual (D) scenario obtained highest
contribution with shortest relative errors in comparison with the two other scenarios.