Estimation Drought in Lorestan using Intelligent Networks

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torabipodeh hasan; DEHGHANI REZA; ROSTAMI SAEED

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Journal: JOURNAL OF CLIMATE RESEARCH Year:2018 | Volume:9 | Issue:35 Page(s): 41-52

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Title

Estimation Drought in Lorestan using Intelligent Networks

Author(s)

torabipodeh hasan | DEHGHANI REZA | ROSTAMI SAEED | Issue Writer Certificate

Keywords

PrecipitationQ2

DroughtQ2

Standardized Precipitation IndexQ1

Wavelet Neural NetworkQ1

Abstract

Drought is a natural and irreversible phenomenon that results from a reduction in rainfall over a given period of time. This phenomenon begins slowly and its impact gradually and over a relatively long period of time appears in different sectors, such as water resources, agriculture, the environment, and so on. Therefore, it is difficult to determine precisely the time of the onset and end of this phenomenon, due to the nature of the Drought, it is difficult to detect the beginning and the end of the Drought. Drought prediction in water resource systems plays an important role in reducing Drought damage. Traditionally, in the last few decades, Drought has been widely used to predict fit and mathematical models. For prediction of Drought, a variety of approaches have been introduced in hydrology, in which intelligent models are the most important ones. In this study, monthly rainfall data of Nahrabad, Alshtar, Dorood and Boroujerd stations in Lorestan province were used to evaluate the accuracy of models in Drought prediction. For modeling, wavelet network and artificial neural network models were used and the results were compared to each other for the accuracy of the studied models. Materials and methods: In this research, four rain-impact stations of Nurabad, Alshatr, Dorood and Borujerd in Lorestan province were selected as the study area and Drought analysis was carried out using SPI standard rainfall index at a 12-month time scale at these stations. . For this purpose, rainfall parameter was selected on monthly basis during the statistical period (1394-1374) as input and standard rainfall index as the output parameter of the models. Wavenet called wavelet-based neural network which combined with wavelet theory and neural networks have been created. It also have supportive of the benefits and features of neural networks and charm and flexibility and strong mathematical foundations and analysis of multi-scale wavelet. a combination of wavelet theory with neural network concepts to the creation of Wavelet Neural Network and feedforward neural shock can be a good alternative for estimating approximate nonlinear functions. Feedforward neural network with sigmoid activation function is in the hidden layer While at the nerve shocked wavelet, wavelet functions as activation function of hidden layer feedforward networks are considered, In both these networks and scale wavelet transformation parameters are optimized with their weight. Artificial neural networks inspired by the brain's information processing systems, design and emerged intoTo help the learning process and with the use of processors called neurons trying to understand the inherent relationships between data mapping between input space and optimal space. Hidden layer or layers, the information received from the input layer and output layer are the processing and disposal. Based on the artificial neural network structure, its major features high processing speed, the ability to learn the pattern, The ability to extend the model after learning, flexibility against unwanted errorsNo disruption to error on the part of the connection due to weight distribution network. The first practical application of synthetic networks with the introduction of Multilayer Perceptron network wasConsultants. for training this network back propagation algorithm is used. The basis of this algorithm is based on error correction learning ruleThat consists of two main routes. By adjusting the parameters in the MLP model error signal and input signal occurs. Determine the number of layers and neurons is the most important issues in simulation with artificial neural network. The criteria of correlation coefficient, root mean square error and of mean absolute error were used to evaluate and performance compare of models. Results and Discussion: The results showed that both models have a good ability to estimate the standard rainfall index, but in terms of accuracy, the Wavelet Neural Network model has shown better performance than artificial neural network. The results also showed that the Wavelet Neural Network model has less error than the artificial neural network, and this model (Wavelet Neural Network) has shown an acceptable accuracy in estimating most of the values. On the other hand, the results of the Drought index test showed that in both models, the Drood station is more consistent with observational values. Conclusion: Overall, the results showed that the use of Wavelet Neural Network model can be effective in Drought estimation, which in turn is useful for facilitating the development and implementation of management strategies to prevent Drought.

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APA: Copy

torabipodeh, hasan, DEHGHANI, REZA, & ROSTAMI, SAEED. (2018). Estimation Drought in Lorestan using Intelligent Networks. JOURNAL OF CLIMATE RESEARCH, 9(35 ), 41-52. SID. https://sid.ir/paper/213119/en

Vancouver: Copy

torabipodeh hasan, DEHGHANI REZA, ROSTAMI SAEED. Estimation Drought in Lorestan using Intelligent Networks. JOURNAL OF CLIMATE RESEARCH[Internet]. 2018;9(35 ):41-52. Available from: https://sid.ir/paper/213119/en

IEEE: Copy

hasan torabipodeh, REZA DEHGHANI, and SAEED ROSTAMI, “Estimation Drought in Lorestan using Intelligent Networks,” JOURNAL OF CLIMATE RESEARCH, vol. 9, no. 35 , pp. 41–52, 2018, [Online]. Available: https://sid.ir/paper/213119/en

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