Fatemeh Maghsoud; Mohammad Reza Yazdani; Mohammad Rahimi; Arash Malekian; ali asghar zolfaghari
Abstract
Overview, drought is effected an unusual dry period which is enough continued and causes imbalance in the hydrologic status, as depletion of surface water and groundwater resources. The purpose of this research is modeling meteorological drought prediction using Neural Network- Multi layer Perceptron, ...
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Overview, drought is effected an unusual dry period which is enough continued and causes imbalance in the hydrologic status, as depletion of surface water and groundwater resources. The purpose of this research is modeling meteorological drought prediction using Neural Network- Multi layer Perceptron, parameters and climatic signals in three time scales include short, middle and long term in a rain-gauge station located at south plain of Qazvin Province. Three different scenarios were tested as inputs model. Optimal combination of variables was determinate by Gamma-Test after identification of input variables using cross-correlation. Results showed, influence of climatic signals increased and against the influence of meteorological parameters decreased when time scale were increased from short-term to long-term. MEI (Multivariate ENSO Index) and rainfall were introduced as the most effective climatic signals and meteorological parameter for each scale, respectively. Neural Network modeling which has hidden layer with enough neurons, Sigmoid Function in middle layer and linear function at output layer was used. The most appropriate of the number neurons was determined in each scenario and wasn’t observed significant correlation between increasing or decreasing the error and number of neurons. Finally, the most appropriate network structure was determined based on evaluation indexes in three scenarios and each time scale.
Mohammad Reza Yazdani; Ali Asghar zolfaghari
Abstract
Watershed outflow has influenced by different factors such as climatic, human and physical aspects and this Variability of effective factors can cause complex conditions, difficulty of flow forecasting and it mainly originates by different local and temporal scales of these factors. Also, some remote ...
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Watershed outflow has influenced by different factors such as climatic, human and physical aspects and this Variability of effective factors can cause complex conditions, difficulty of flow forecasting and it mainly originates by different local and temporal scales of these factors. Also, some remote meteorological signals can cause changes in meteorological conditions in different regions. Hablehrud river flow has a vital role in regional development, especially for agricultural section. Thus research of river flow forecasting should be done for water resources management especially when there are drought and climate change conditions in order to facilitate sustainable development. In this study four nonlinear models of artificial neural networks including Generalized Feed Foreward Networks (JFNNs), Jordan/Elman Networks(JENs), Time Lag Recurrent Networks(TLRNs) and Radius Basis Function Networks(RBF) was used to modeling Hablehrud river flow(Bonkuh station) during 1982 to 2011. Input variables after sensitivity analysis were used in 4 models and 4 scenarios. Ten teleconnection indexes were used as input of the model to evaluate their roles in model capability. Results indicated that in the test stage Jordan/Elman Networks represented lower error compared with selected models (RMSE for 4 scenarios are5.57, 4.9, 5.35 and 4.62 respectively). In general error showed decreasing trend from first scenario to the last. Error was decreased of 15 to 31 percent by using teleconnection patterns as inputs (GFFN=%26, JEN=%15.8, TLRN=%25.5 and RBF=%31.7). Totally using teleconnection indexes as inputs in the modeling stage can diminish error of flow forecasting, although selected models indicated different results due to its variable topologies.
