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11.
Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting. 相似文献
12.
Vahid Mahboub Mohammad Saadatseresht Alireza A. Ardalan 《Studia Geophysica et Geodaetica》2017,61(1):19-34
An applicable algorithm for Total Kalman Filter (TKF) approach is proposed. Meanwhile, we extend it to the case in which we can consider arbitrary weight matrixes for the observation vector, the random design matrix and possible correlation between them. Also the updated dispersion matrix of the predicted unknown is given. This approach makes use of condition equations and straightforward variance propagation rules. It is applicable to data fusion within a dynamic errors-in-variables (DEIV) model, which usually appears in the determination of the position and attitude of mobile sensors. Then, we apply for the first time the TKF algorithm and its extended version named WTKF to a DEIV model and compare the results. The results show the efficiency of the proposed WTKF algorithm. In particular in the case of large weights, WTKF shows approximately 25% improvement in contrast to TKF approach. 相似文献
13.
Vahid?NouraniEmail authorView author&#;s OrcID profile Afshin?Partoviyan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(2):545-562
Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds. 相似文献
14.
Maziar Khosravi Seyed Mostafa Siadatmousavi Ross Vennell Vahid Chegini 《Ocean Dynamics》2018,68(2):239-254
Vessel-mounted ADCP measurements were conducted to describe the transverse structure of flow between the two headland tips in Khuran Channel, south of Iran (26° 45′ N), where the highest tidal velocities in spring tides were ~?1.8 m/s. Current profiles were obtained using a 614.4 kHz TRDI WorkHorse Broadband ADCP over nine repetitions of three cross-channel transects during one semidiurnal tidal cycle. The 2.2-km-long transects ran north/south across the channel. A least-square fit to semidiurnal, quarter-diurnal, and sixth diurnal harmonics was used to separate the tidal signals from the observed flow. Spatial gradients showed that the greatest lateral shears and convergences were found over the northern channel and near the northern headland tip due to very sharp bathymetric changes in this area. Contrary to the historical assumption, the across-channel momentum balance in the Khuran Channel was ageostrophic. The current study represents one of the few examples reported where the lateral friction influences the across-channel momentum balance. 相似文献
15.
Acta Geotechnica - Evidence from recent earthquakes reminds us that fault-induced permanent ground displacement has a devastating effect on structures in addition to damage caused by wave... 相似文献
16.
Khosravi Vahid Ardejani Faramarz Doulati Aryafar Ahmad Yousefi Saeed Karami Shawgar 《Environmental Earth Sciences》2020,79(7):1-16
The pore structure characteristics of soil are closely related to soil engineering properties. For saline soil distributed in seasonally frozen areas, existing studies have focused on the influence of freeze–thaw cycles on pore structure, while the influence of soluble salt in the soil is not well understood. This study aims to explore the influence of salt content and salt type on the pore structure of freeze-thawed soil. Soil samples with different salt contents (0–2%) and types (bicarbonate salt and sulfate salt) were subjected to 10 freeze–thaw tests, and their pore size distributions (PSDs) were obtained by mercury intrusion porosimetry tests. In addition, the PSDs were quantitatively analyzed by fractal theory. For both salts, the PSDs of the tested soil samples were bimodal after the freeze–thaw cycles, and the porosity of saline soil samples increased with increasing salt content overall. However, the contents of various types of pores in soil samples with two salt types were quite different. The variation in bicarbonate salt content mainly affected the mesopore and macropore contents in the soil samples, and their change trends were opposite to each other. For soil samples with sulfate salt, the porosity and macropore content increased significantly when the salt content exceeded 1%. In addition, the pore structures in saline soil presented fractal characteristics after the freeze–thaw cycles, and the fractal dimension was positively correlated with macropore content. This study may provide references for understanding the engineering properties of saline soil in seasonally frozen areas at the microscale. 相似文献
17.
Mohammad Hadi Moeini Amir Etemad-Shahidi Vahid Chegini Iraj Rahmani 《Ocean Dynamics》2012,62(5):785-797
The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in
the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data
with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches.
It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not
result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating
of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were
used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output
variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the
best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave
parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further
improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN. 相似文献
18.
Mohammad E. Hekmatian Vahid E. Ardestani Mohammad A. Riahi Ayyub M. K. Bagh Jalal Amini 《Acta Geophysica》2015,63(4):1000-1024
In gravity interpretation methods, an initial guess for the approximate shape of the gravity source is necessary. In this paper, the support vector classifier (SVC) is applied for this duty by using gravity data. It is shown that using SVC leads us to estimate the approximate shapes of gravity sources more objectively. The procedure of selecting correct features is called feature selection (FS).In this research, the proper features are selected using inter/intra class distance algorithm and also FS is optimized by increasing and decreasing the number of dimensions of features space. Then, by using the proper features, SVC is used to estimate approximate shapes of sources from the six possible shapes, including: sphere, horizontal cylinder, vertical cylinder, rectangular prism, syncline, and anticline. SVC is trained using 300 synthetic gravity profiles and tested by 60 other synthetic and some real gravity profiles (related to a well and two ore bodies), and shapes of their sources estimated properly. 相似文献
19.
Hesam Seyedin Reza HabibiSaravi Nasrin sayfouri Vahid Hoseini Djenab Fariba Ghasemi Hamedani 《Natural Hazards》2017,88(2):965-975
Flood is the most common disaster in the world and has acute or chronic health consequences including psychological sequels. Post-traumatic stress disorder (PTSD) is one of the main consequences. This study aimed to explore the psychological impacts (PTSD) in two flooded cities of Mazandaran Province, Neka and Behshahr, in the southeastern Caspian region that experienced flooding in 2012. A cross-sectional community-based study was performed on randomly selected samples of 400 individuals using GIS-based sampling from 139931 residents of the two flooded cities. The PTSS-10 questionnaire was used for data collection. The results showed that the overall stress disorder mean score among the participants was 2.59 out of 6. PTSD prevalence in the affected population was 64%. It was also found that stress scores significantly increased in younger people, male gender, the divorced, the widows or the widowers, and those who lost their properties (p < 0.05). Study findings showed that flood is a considerable stressor which develops PTSD. This finding should be taken into account in all four phases of disaster management cycle, and subsequently, specialized post-disaster mental health services must be provided for the afflicted population. 相似文献
20.
Maysam Abedi Ahmad Afshar Vahid E. Ardestani Gholam H. Norouzi Caro Lucas 《Acta Geophysica》2010,58(2):317-336
This paper presents a neural network approach to determine 2D inverse modeling of a buried structure from gravity anomaly
profile. The results of the applied neural network method are compared with the results of two other methods, least-squares
minimization and the simple method. Sphere, horizontal cylinder and vertical cylinder and their gravity effects are considered
as the synthetic models and the synthetic data, respectively. The synthetic data are also corrupted with noise to evaluate
the capability of the methods. Then the Dehloran bitumen map in Iran is chosen as a real data application. Anomaly value of
the cross-section, which is taken from the gravity anomaly map of Dehloran bitumen, is very close to those obtained from these
methods. 相似文献