Third-order secular Solution of the variational equations of motion of a satellite in orbit around a non-spherical planet

We constructed an analytical theory of satellite motion up to the third order relative to the oblateness parameter of the Earth (J ₂). Equations of secular variations was developed for the first three orbital elements (a, e, i) of an artificial satellite. The secular variations are solved in a closed form.     

A study of the contaminated banks of the Mahoning River, Northeastern Ohio, USA: characterization of the contaminated bank sediments and river water–groundwater interactions

The Mahoning River is one of the five most contaminated rivers in the U.S. This study characterized the contaminated sediments in the river banks and investigated the hydraulic interconnection between shallow aquifer in the banks with the river water. The study was conducted along the most polluted section of the river, which is 50-km long, using over 50 monitoring wells. The characterization part of the study investigated the sedimentology, hydraulic conductivity, and spatial distribution of the contaminated sediments. Results of the characterization revealed that the contaminated sediments consist of fine-grained sand, silt, mud, and clay. The spatial distribution of the contaminated sediment is heterogeneous and positively correlates with the hydraulic conductivity values, i.e., the greatest contamination occurs in high conductivity areas. Hydraulic conductivity was determined by the Hazen formula using 82 sediment samples. Bioremediation, which is one of the remedial options considered for the banks, is found to be hydraulically feasible because of sufficient hydraulic conductivity values (≥10^?4 cm/s) that ensure reasonable rates of nutrient delivery. Monitoring of water levels in the river and groundwater for a 10-month period shows that flow occurs from the river to groundwater and vice versa. The exchange of flow is influenced by rainfall. Flow of groundwater to the river will continually transport the dissolved contaminants in groundwater to the river. Therefore, findings of this study show that one of the remedial options that proposes dredging of channel sediments and permits no action for bank sediments cannot be chosen due to river water–groundwater interactions.     

Prediction of drought in dry lands through feedforward artificial neural network abilities

Drought is one of the most important natural hazards in Iran. It is especially more prevalent in arid and hyper arid regions where there are serious limitations in regard to providing sufficient water resources. On the other hand, drought modeling and particularly its prediction can play important role in water resources management under conditions of lack of sufficient water resources. Therefore, in this study, drought prediction in a hyper arid location of Iran (Ardakan region) has been surveyed based on the abilities of artificial neural. Standardized Precipitation Index (SPI) in different time scales (3, 6, 9, 12, and 24 monthly time series) computed based on the data gathered from four rain gauge stations. After evaluation and testing of different artificial neural networks (ANN) structures, gradient descent back propagation (traingd) network showed higher abilities than others. Then, the predictions of SPI time series with different monthly lag times (1:12 months) were tested. Generally, drought prediction by ANNs in the Ardakan region has shown considerable results with the correlation coefficient (R) more than 0.79 and in the most cases and it rises more than 0.90, which indicates the ANN’s ability of drought prediction.     

Identification of homogenous regions in Gorganrood basin (Iran) for the purpose of regionalization

Estimation of flood in basins with poor condition of hydrometric stations as in quantity and quality is a dominant problem around the world, mainly in developing country where lack of funds and human resources cause more limitation in number of gauging stations. One of the areas that experience frequent floods and also suffer from small number of stations in Iran is Gorganrood basin. So there is a great need for the estimation and prediction of runoff in this area to prevent any future floods. Due to insufficient station in this area, direct prediction of flood is not applicable. Regional flood frequency analysis is a practical and widely used solution for these situations, which involves the identification of homogenous regions. Gorganrood region was hydrologically homogenized according to the extracted parameters that influence the floods. One of these parameters was Normalized Difference Vegetation Index (NDVI) driven from MODIS images. Curvature is another parameter that relates to topographic attributes. From factor analysis, the most appropriate variables were selected. According to these parameters (NDVI, curvature, area, slope…), the regions were classified into homogenous regions. For the purpose of homogenization, hierarchical (wards) clustering, fuzzy clustering and Kohonen method were applied. L-moment technique was used for the investigation of the results. The heterogeneity measure for one of the groups (Group 1) was more than two; therefore some modifications were applied. The region was grouped into two homogenous subregions. All of the clustering methods showed same results. The models showed that class 4 of NDVI is influential on flood in some return periods. The resulted models can be applied in future studies in different aspects of practical hydrology.     

Stability Analysis of Rock Slopes Against Block-Flexure Toppling Failure

Block-flexure is the most common type of toppling failure in rock slopes. In this case, some rock blocks fail due to tensile bending stresses and some overturn under their own weights. In this paper, first, a literature review of toppling failures is summarized. Then, a theoretical model is proposed for rock slopes with a potential for block-flexure toppling instability. Next, a new analytical approach is presented for the stability analysis of such slopes. Finally, a special computer code is developed for a quick stability assessment of the failures based on the proposed method. This code receives the rock slope parameters from the user as the input data and predicts its stability, along with the corresponding factor of safety against the failure, as the output. In addition, two case studies are used for practical verification of the proposed approach and the corresponding computer code as well.     

Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models

Abstract

Much of the prairie region in North America is characterized by relatively flat terrain with many depressions on the landscape. The hydrological response (runoff) is a combination of the conventional runoff from the contributing areas and the occasional overflow from the non-contributing areas (depressions). In this study, we promote the use of a hybrid modelling structure to predict runoff generation from prairie landscapes. More specifically, the Soil and Water Assessment Tool (SWAT) is fused with artificial neural networks (ANNs), so that SWAT and the ANN module deal with the contributing and non-contributing areas, respectively. A detailed experimental study is performed to select the best set of inputs, training algorithms and hidden neurons. The results obtained in this study suggest that the fusion of process-based and data-driven models can provide improved modelling capabilities for representing the highly nonlinear nature of the hydrological processes in prairie landscapes.

Editor D. Koutsoyiannis; Associate editor L. See     

Three-Dimensional Wideband Beamforming for Imaging Through a Single Wall

Through-the-wall imaging and urban sensing is an emerging area of research and development. The incorporation of the effects of signal propagation through wall material in producing an indoor image is important for reliable through-the-wall mission operations. We have previously analyzed wall effects, such as refraction and change in propagation speed, and designed a wideband beamformer for 2D imaging using line arrays. In this letter, we extend the analysis to 3D imaging via delay-and-sum beamforming in the presence of a single uniform wall. The third dimension provides valuable information on target heights that can be used for enhancing target discrimination/identification. Supporting simulation results are provided.     

Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation.