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## Artificial neural network technique for rainfall temporal distribu-tion simulation (Case study: Kechik region) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Caspian Journal of Environmental Sciences | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

مقاله 6، دوره 13، شماره 1، بهار 2015، صفحه 53-60
اصل مقاله (673.52 K)
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نوع مقاله: Research Paper | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

نویسندگان | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

V. Gholami^{1}؛ Z. Darvari^{2}؛ M. Mohseni Saravi^{3}
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^{1}University of Guilan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{2}University of Mazandaran | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

^{3}University of Tehran | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

چکیده | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Artificial neural networks (ANNs) have become one of the most promising tools for rainfall simulation since a few years ago. However, most of the researchers have focused on rainfall intensity records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. The present study was conducted in Kechik station, Golestan Province (northern Iran). The normal multi-layer perceptron form of ANN (MLP–ANN) was selected as the baseline ANN model. The efficiency of GDX, CG and L–M training algorithms were compared to improve computed performances. The inputs of ANN included temperature, evaporation, air pressure, humidity and wind velocity in a 10 minute increment The results revealed that the L–M algorithm was more efficient than the CG and GDX algorithm, so it was used for training six ANN models for rainfall intensity forecasting. The results showed that all of the parameters were proper inputs for simulating rainfall, but temperature, evaporation and moisture were the most important factors in rainfall occurrence. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

کلیدواژهها | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

Intensity؛ Rainfall؛ Algorithm؛ ANN؛ Kechik station | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

اصل مقاله | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

*, Z. Darvari^{2}, M. Mohseni Saravi^{3}
(Received: Dec. 05.2013 Accepted: May. 18.2014)
Artificial neural networks (ANNs) have become one of the most promising tools for rainfall simulation since a few years ago. However, most of the researchers have focused on rainfall intensity records as well as on watersheds, which generally are utilized as input records of other hydro-meteorological variables. The present study was conducted in Kechik station, Golestan Province (northern Iran). The normal multi-layer perceptron form of ANN (MLP–ANN) was selected as the baseline ANN model. The efficiency of GDX, CG and L–M training algorithms were compared to improve computed performances. The inputs of ANN included temperature, evaporation, air pressure, humidity and wind velocity in a 10 minute increment The results revealed that the L–M algorithm was more efficient than the CG and GDX algorithm, so it was used for training six ANN models for rainfall intensity forecasting. The results showed that all of the parameters were proper inputs for simulating rainfall, but temperature, evaporation and moisture were the most important factors in rainfall occurrence.
Rainfall forecasting is vital for the planning and operation of water resources. Two basic approaches exist for this purpose, namely conceptual or physical modeling and system-theoretic or black box modeling. Conceptual models which are generally non-linear, time-invariant, and deterministic, that involve parameters representing climatologic characteristics are used to model rainfall (Solaimani & Darvari, 2008). Rainfall forecasting plays an important role in water resource management and natural disasters management Therefore, different types of models with various degrees of complexity have been developed for this purpose. Black box models normally contain no physically-based input and output transfer functions and, therefore, are considered to be purely empirical models (Solaimani & Darvari, 2008). A series of applications have been reported since then and they range from the most recent studies (Kisi, 2004; Olsson
The present study was conducted in Kechik station, located in eastern longitude 55° 51´ 51˝ and northern latitude 37° 42´ 34˝ (Fig.1) of Golestan Province (northern Iran). The elevation of Kechik station is 600 m and the precipitation pattern is rainfall. The mean annual precipitation is about 490 mm. The climate of the zone is semi-humid and the total precipitation changes from 400 to 700 mm in different areas of the region. Kechik watershed contains poor range lands and dry farming terrains and a small part of the watershed is sparse forest (Gholami Artificial neural network comprises a network of neurons and takes the cue from their biological counterparts, in the manner that neurons being capable of learning can be trained to find solutions, recognize patterns, classify data and even forecast future events. Such a network usually comprises many layers arranged in a series, each layer containing one or a group of neurons, and each having the same pattern of connections to the neurons in the other layer(s). A typical multi-layer feed forward neural network architecture is shown in Fig. 2. In general, the selection of input variables and output variables is problem dependent. The appropriate input variables will allow the network to successfully map the desired output and avoid the loss of important information. In the present study, the input dimensions were determined by the input variables. To determine an appropriate artificial neural network structure for simulating the rainfall in a 10 minute interval, six different models for the GDX, CG and LM algorithms were developed. Table 1 shows the summary of the different inputs. The hidden neurons from 1-20 are varied in each model and all the simulations are terminated after 1,400 iterations. Table 2 shows the comparison of different models for GDX, CG and LM algorithm, and the performance of different models is presented based on the criteria of correlation coefficient (Rsqr) and Root Mean Square Error (RMSE) Six input models planned for rainfall simulation are as follows: = ƒ{ , , , , } (1) =ƒ { , , , } (2) =ƒ { , , } (3) =ƒ { , } (4) =ƒ { , } (5) =ƒ { , } (6) Where;
1. Root mean squared error (RMSE) RMSE= )7)
2. The Pearson’s R-Squared statistics (Rsqr) )8) Where Qi is the observed value, Ôi is the estimated value and Ōi is the mean of the observed data and Õi is the mean of the estimated data and n is the number of data in each stage of test and terrain.
The data used in ANN were climatic data in a 10 minute increment with two years duration from 2006-7. For the mentioned models, 1000 records were used for their development; however, for the validation/testing of the model, 400 records were applied. Six model structures were developed to investigate the probability impacts of enabling/disabling temperature, evaporation, wind velocity, air pressure and air moisture as input dimensions of Kechik station. Based on the results of Root Mean Square Error (RMSE) and coefficient of determination (R) the measures were: 2.1, 0.81 (model 1); 2.4, 0.7 (model 2); 2.3, 0.78 (model 3); 2.6, 0.67 (model 4); 3.3, 0.36 (model 5); 4.1, 0.24 (model 6). Optimum network structure in rainfall simulation included a MLP with five inputs, and LM (Levenberg-Marquart) training technique and one neuron. Fig.3 shows the selection of the hidden neurons for the best structure. The results showed that the first model produces the best performance for GDX, CG and LM algorithms. After optimizing network, testing stage or efficiency evaluation is performed. Evaluation of ANN efficiency was done through comparison between estimated and actual values. From the comparison of the results in Table 2, it can be concluded that it is easy to tell the LM algorithm is superior to the GDX and CG algorithm among the used models and phases.
The results showed clearly that the artificial neural networks are capable of modeling the rainfall process. Thus, these results further confirms the general enhancement achieved by using neural networks in many other hydrological fields (Luk
We thank FRWM (Forest, Range & Watershed Management Organization of Iran) for providing the data regarding Kechik station and also for helping us with data-preprocessing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||

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