PSO-Based Optimal Selection of Zernike Moments for Target Discrimination in High-Resolution SAR Imagery

Target discrimination is the key step of automatic target detection in synthetic aperture radar (SAR) images. In this paper, a new algorithm, effective and robust feature sets for target discrimination in high resolution SAR images has been proposed. Two main steps in target discrimination of SAR images have been developed, the feature extraction based on Zernike moments (ZMs) having linear transformation invariance properties and the PSO based feature selection to select the optimal feature subset of Zernike moments for decreasing computational complexity of feature extraction step. The input regions of interest (ROIs) have been segmented and passed to a number of preprocessing stages such as histogram equalization, position and size normalization. Two groups of Zernike moments (shape and margin (intensity) characteristic) have been extracted from the preprocessed images and they have been applied to the feature selection step. Each group includes 34 moments with different orders and iterations. The selected moments have been applied to a SVM classifier. The proposed scheme has been tested on the MSTAR database. The Receiver Operational Characteristics (ROC) curve and the performance of proposed method using some measured data have been analyzed. Experimental results demonstrate the efficiency of the proposed approach in target discrimination of SAR imagery.     

Spatial patterns and temporal trends of daily precipitation indices in Iran

Spatial patterns of daily precipitation indices and their temporal trends over Iran are investigated using the APHRODITE gridded daily precipitation dataset for the period 1961–2004. The performance and limitations of the gridded dataset are checked against observations at ten rain-gauge stations that are representative of different climates in Iran. Results suggest that the spatial patterns of the indices reflect the role of orography and sea neighborhoods in differentiating central-southern arid and semi-arid regions from northern and western mountainous humid areas. It is also found that western Iran is impacted by the most extreme daily precipitation events occurring in the country, though the number of rainy days has its maximum in the Caspian Sea region. The time series of precipitation indices is checked for long-term trends using the least squares method and Mann-Kendall test. The maximum daily precipitation per year shows upward trends in most of Iran, though being statistically significant only in western regions. In the same regions, upward trends are also observed in the number of wet days and in the accumulated precipitation and intensity during wet days. Conversely, the contribution of precipitation events below the 75th percentile to the annual total precipitation is decreasing with time, suggesting that extreme events are responsible for the upward trend observed in the total annual precipitation and in the other indices. This tendency towards more severe/extreme precipitation events, if confirmed by other datasets and further analyses with longer records, would require the implementation of adequate water resources management plans in western Iran aimed at mitigating the increasing risk of intense precipitation and associated flash floods and soil erosion.     

Data-snooping procedure applied to errors-in-variables models

The theory of Baarda’s data snooping — normal and F tests respectively based on the known and unknown posteriori variance — is applied to detect blunders in errors-invariables (EIV) models, in which gross errors are in the vector of observations and/or in the coefficient matrix. This work is a follow-up to an earlier work in which we presented the formulation of the weighted total least squares (WTLS) based on the standard least squares theory. This method allows one to directly apply the existing body of knowledge of the least squares theory to the errors-in-variables models. Among those applications, data snooping methods in an EIV model are of particular interest, which is the subject of discussion in the present contribution. This paper generalizes the Baarda’s data snooping procedure of the standard least squares theory to an EIV model. Two empirical examples, a linear regression model and a 2-D affine transformation, using simulated and real data are presented to show the efficacy of the presented formulation. It is highlighted that the method presented is capable of detecting outlying equations (rather than outlying observations) in a straightforward manner. Further, the WTLS method can be used to handle different TLS problems. For example, the WTLS problem for the conditions and mixed models, the WTLS problem subject to constraints and variance component estimation for an EIV model can easily be established. These issues are in progress for future publications.     

Anomalies in the US precipitation extremes and their association with different modes of climate variability

ABSTRACT

In this study, we investigate the temporal oscillations of precipitation extremes in different climate regions of the United States. We apply quantile perturbation analysis to average daily precipitation and, to 1041 weather stations with high-quality data from 1900 to 2016. Moreover, we explore the relationship between the extreme precipitation and different well-known cyclical climate modes. Overall, the analysis of average daily precipitation identifies a drier condition in the middle decades of the twentieth century and, a wetter climate in the early century and recent decades. Moreover, the in situ analysis reveals a significant anomaly, mainly prevalent in the Central and Southern regions of the United States. We applied a finite set of linear regression models with different combinations of cyclical climate modes to inform the variability of anomalies with best performing models. Our results highlight the dominant effect of ENSO and NAO in the wide area of the United States.     

Municipal water demand forecasting under peculiar fluctuations in population: a case study of Mashhad,a tourist city

ABSTRACT

Forecasting future water demands has always been of great complexity, especially in the case of tourist cities which are subject to population fluctuations. In addition to the usual uncertainties related to climate and weather variables, daily water consumption in Mashhad, a tourist city is affected by a significant different fluctuation. Mashhad is the second most populous city in Iran. The number of tourists visiting the city is subject to national and religious events, which are respectively based on the Iranian formal calendar (secular calendar) and the Arabic Hijri calendar (Islamic religious calendar). Since religious events move relative to the secular calendar, the coincidence of the two calendars results in peculiar wild fluctuations in population. Artificial neural networks (ANNs) are chosen to predict water demand under such conditions. Three types of ANNs, feedforward back-propagation, cascade-forward and radial basis functions, are developed. In order to track how population fluctuation propagates in the model and affects the outputs, two sets of inputs are considered. For the first set, based on evaluating several repetitions, a typical combination of variables is selected as inputs, whereas for the second set, new calendar-based variables are included to decrease the effect of population fluctuations; the results are then compared using some performance criteria. A large number of runs are also conducted to assess the impact of random initialization of the weights and biases of networks and also the effect of calendar-based inputs on improvement of network performance. It is shown that, from the points of view of performance measures and unchanging outputs through numerous runs, the radial basis network that is trained by patterns including calendar-based inputs can provide the best domestic water demand forecasting under population fluctuations.

Editor D. Koutsoyiannis Associate editor E. Rozos     

Energy of electron acoustic solitons in plasmas with superthermal electron distribution

The propagation of nonlinear waves in plasmas consisting of cold electron fluid and superthermal hot electrons and stationary ions is studied. The Korteweg-de Vries (KdV) equation is derived using the reductive perturbation theory. It is found that only the rarefractive solitons can be created. Moreover, the linear dispersion relation and energy of solitary waves in the presence of hot superthermal electrons are derived. Our investigation is of wide relevance to astronomers and space scientists working on interstellar space plasmas.     

Head-on collision of electron acoustic solitary waves in a plasma with nonextensive hot electrons

The head-on collision between two electron-acoustic solitary waves (EASWs) in an unmagnetized plasma is investigated, including a cold electrons fluid, hot electrons, obeying a nonextensive distribution and stationary ions. By using the extended Poincaré-Lighthill–Kuo (PLK) perturbation method, the analytical phase shifts following the head-on collision are derived. The effects of the ratio of the number density of hot electrons to the number density of cold electrons α, and the nonextensive parameter q on the phase shifts are studied. It is found that q and the hot-to-cold electron density ratio significantly modify the phase shifts.     

Early Berriasian ammonites from Shal, Talesh region (NW Alborz Mountains, Iran)

An early Berriasian (Berriasella jacobi Zone) ammonite fauna is described for the first time from the Alborz Mountains in northwest Iran. It has been collected from a section located near the village of Shal (Talesh region); in addition to rare phylloceratids, lytoceratids and Neolissoceras, the majority of ammonites belong to the neocomitid subfamily Berriasellinae. With the exception of a new genus and species, Taleshites fuersichi, these taxa are common in European and North African Tethyan successions. Associated calpionellids confirm the early Berriasian age of the ammonite-bearing levels.     

A survey of challenges in reducing the impact of geological hazards associated with earthquakes in Iran

Most of the recent earthquakes in Iran were associated with some types of geological hazards that were the cause of additional damage and casualties in the earthquake-affected areas. In order to reduce the impacts of geo-hazards, several policies and plans were prepared, approved, and implemented by the Iranian government during the last two decades. However, such activities have not yet resulted in risk reduction to an acceptable level; since they are not formulated based on local conditions and are not linked to location-specific comprehensive plans. In this paper, after introducing some of the impacts of geo-hazards associated with recent earthquakes in Iran, the country trends in development of relevant risk reduction plans and policies are introduced, evaluated, and compared with some other countries having similar challenges. Strategies toward risk mitigation in the country are addressed, and a number of indices for the assessment of the geo-hazard risk reduction plans and activities are introduced. Finally, a conceptual model for the evaluation of the preparedness level against geo-hazards is proposed and discussed.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.