Hydrodynamic Tidal Model of the Persian Gulf Based on Spatially Variable Bed Friction Coefficient

The aim of this study is to develop a two-dimensional hydrodynamic tidal model for the Persian Gulf (PG2017) using 2D-MIKE21 software. The advantages of present study is accounting for the spatial variation of bed friction coefficient besides a precise bathymetry together with a 23-year of combined records of satellite altimetry data. We found that the bed friction coefficient has a significant effect on sea level changes in the region under our modeling consideration. Since the tidal behavior in the northern part of the Qeshm Island is significantly different from the other parts of the Persian Gulf, to present a more accurate hydrodynamic tidal model, the Gulf is divided into two regions where the bed friction coefficient is modeled separately for each region. The root mean square value of the differences between the amplitude of dominant constituents; M2, S2, K1, and O1 derived from the PG2017 model and that of 98 altimetry and coastal tide gauge stations are respectively equal to 1.6, 1.9, 2.8, and 1.3?cm. Moreover, comparing the PG2017 model efficiency with the FES2014, OSU12, EOT11a, DTU10, and Admiralty models shows that the PG2017 model has an improvement of 22.1%, 47.2%, 43.2%, 44.2%, and 57.6% in terms of relative error, respectively.     

Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran

Human‐induced land use/land cover (LULC) changes are among the most important processes that shape the dynamics of the earth’s surface. This phenomenon, which is occurring at an astonishing rate, and its consequential environmental impacts have become an important area of research for scientists.Therefore, a wide range of methods and models have been developed to detect and predict these alterations, among which cellular automata (CA) models such as the CA‐Markov model, due to their affinity to geographic information system (GIS) and remote sensing (RS), are appropriate for detailed resolution modelling and simulating dynamic spatial processes. In Iran, the district of Ravansar has undergone severe LULC changes recently, thus to take the necessary precautions, decision‐makers need to predict and determine the extent of these changes. In this study, using spatial analysis methods the LULC changes in Ravansar were investigated from 1992 to 2015. Subsequently, the CA‐Markov model was applied to simulate the spatial pattern changes of LULC until 2030. Our results indicated that from 1992 to 2015, this region has witnessed a noticeable increase in the areas of the built‐up and agricultural lands (both aquatic and non‐aquatic), resulting in the decrease of the gardens, range, and bare lands. The simulated LULC map showed that this trend will continue due to more urbanization and development of agricultural areas.     

Monthly suspended sediment load prediction using artificial intelligence: testing of a new random subspace method

ABSTRACT

The predictive capability of a new artificial intelligence method, random subspace (RS), for the prediction of suspended sediment load in rivers was compared with commonly used methods: random forest (RF) and two support vector machine (SVM) models using a radial basis function kernel (SVM-RBF) and a normalized polynomial kernel (SVM-NPK). Using river discharge, rainfall and river stage data from the Haraz River, Iran, the results revealed: (a) the RS model provided a superior predictive accuracy (NSE = 0.83) to SVM-RBF (NSE = 0.80), SVM-NPK (NSE = 0.78) and RF (NSE = 0.68), corresponding to very good, good, satisfactory and unsatisfactory accuracies in load prediction; (b) the RBF kernel outperformed the NPK kernel; (c) the predictive capability was most sensitive to gamma and epsilon in SVM models, maximum depth of a tree and the number of features in RF models, classifier type, number of trees and subspace size in RS models; and (d) suspended sediment loads were most closely correlated with river discharge (PCC = 0.76). Overall, the results show that RS models have great potential in data poor watersheds, such as that studied here, to produce strong predictions of suspended load based on monthly records of river discharge, rainfall depth and river stage alone.     

Micromorphology and quality attributes of the loess derived soils affected by land use change: A case study in Ghapan watershed, Northern Iran

In order to study the effects of different land vegetative covers on soil quality attributes, a loess hill slope was selected in eastern Golestan Province, Ghapan watershed, Iran. Four profiles in four land uses, including Quercus natural forest; Pinus artificial forest; Cupressus artificial forest and a cultivated land, were studied. Results showed that MWD was significantly different in the studied land uses, and it varied between 1.6 mm in Quercus natural forest and o.31 mm in cultivated land use. The lowest CEC, microbial respiration rate and organic carbon were 28.4 cmol·kg^1, 177 μgCO2·g^-1·day^-1 and 1.32 % found in cultivated land use, respectively. The organic matter was considerably higher content in the forest areas than that of cultivated land use. The studies on soil profile development revealed that the natural forest soils were highly developed. The soils of the Quercus natural forest were classified as Calcic Haploxeralfs with a well developed argillie horizon unlike the cultivated soils which showed the minimum development and classified as Typic Xerorthents. The soils of the artificial forests had both mollic epipedons and were classified as Typic Calcixerolls with moderate profile development. Micromorphological studies revealed that argillic horizons had speckled and partly crystallitic b-fabric in the natural forest indicating the high landscape stability. In contrast, the crystallitic b-fabric of other land uses shows the absence of enough leaching of carbonate and the subsequent migration of clay particles indicating the unstable conditions and high soil erosion. Intense erosion of the surface horizons of cultivated land use has resulted in the outcropping of the subsurface carbonate rich horizons preventing soil development.     

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.     

Relative contributions of crust and mantle to generation of Oligo-Miocene medium-K calc-alkaline I-type granitoids in a subduction setting—a case study from the Nabar Pluton,central Iran

The Nabar pluton with the age of Oligo-Miocene located northwest of Isfahan, the Urumieh-Dokhtar magmatic belt, is composed of gabbro, gabbro diorite, diorite, quartz diorite, tonalite, and quartz monzonite. These rocks contain plagioclase, quartz, alkali-feldspar, magnesiohornblende, actinolite, tremolite-hornblende, actinolite-hornblende, anthophyllite, biotite, and Na-poor pyroxene. Application of the Al-in-hornblende barometry indicates pressures of 2–2.15 kbar, whereas the clinopyroxene barometry shows a pressure of 5 kbar. The temperature (i.e., 750–800°C) is estimated using the amphibole-clinopyroxene thermometry in a dioritic sample. Magmatic water content was greater than 10% at the time of formation of dioritic rocks in the Nabar pluton. Based on chemistry of mafic minerals and geochemical data, the Nabar plutonic complex comprises medium-K, calc-alkaline, and I-type granitoid. The rocks are characterized by enrichment of lithophile elements (LILEs) and depletion of high-field-strength elements (HFSEs). The Nabar rocks have weak concave-upward rare earth element (REE) patterns, suggesting that amphibole played a significant role in their generation during magma segregation. Low (Al₂O₃/(FeO + MgO + TiO₂) and (Na₂O + K₂O)/(FeO + MgO + TiO₂) ratios, and the patterns of trace and rare earth elements suggest that these rocks formed along a destructive plate margin and were derived from a lower crustal source. The magma probably formed by partial melting of lower crustal protoliths (amphibolites). Lower crust contamination with magma derived from partial melting of the upper mantle has an important role in the formation of this intrusive body, and a fractional crystallization of melts in higher crustal levels generated this spectrum of rock types. Mantle-derived gabbroic magmas emplaced into the lower crust are the most likely heat sources for partial melting.     

Automatic Selection of Landmarks for Navigation Guidance

Although current navigation services provide significant benefits to people's mobility, the turn‐by‐turn instructions they provide are sometimes ineffective. These instructions require people to maintain a high level of attention and cognitive workload while performing distance or angle measurements on their own mental map. To overcome this problem, landmarks have been identified as playing a major role in turn‐by‐turn instructions. This requires the availability of landmarks in navigation databases. Landmarks are commonly selected manually, which involves time‐consuming and tedious tasks. Automatic selection of landmarks has recently gained the attention of researchers but currently there are only a few techniques that can select appropriate landmarks. In this article, we present a technique based on a neural network model, where both static and dynamic features are used for selecting landmarks automatically. To train and test this model, two labeling approaches, manual labeling and rule‐based labeling, are also discussed. Experiments on the developed technique were conducted and the results show that rule‐based labeling has a precision of approximately 90%, which makes the technique suitable and reliable for automatic selection of landmarks.     

Effect of topographic bias on geoid and reference ellipsoid of Venus, Mars, and the Moon

Since the continuation of an external gravity field inside topographic masses by a harmonic function results in topographic bias, geoid computation by means of global gravity models (GGMs) in terms of external-type series of spherical harmonics, at locations where the GGMs are evaluated inside the topographic masses, will be biased. Consequently, if the reference ellipsoid is defined based on the geoid, it will also be biased. In this paper, the effects of topographic bias on the geoid and reference ellipsoid of Venus, Mars, and the Moon are studied. Moreover, a thorough error analysis in the geoid and reference ellipsoid computation is presented, and it is shown that the estimated standard deviation (STD) of the geoid potential value, the geoidal heights, and the semimajor and semiminor axes of the reference ellipsoid are independent of the topographic bias. According to the results, the effects of topographic bias on the geoid potential value and the semimajor and semiminor axes of the reference ellipsoid in comparison with their estimated STDs are insignificant for Venus, Mars, and the Moon. Moreover, the effect of topographic bias on the geoidal heights of Venus as compared with the estimated STD of its geoidal heights is insignificant. However, the effects of topographic bias on the geoidal heights of Mars and the Moon can be significant, especially in high mountains such as the Tharsis volcanic region on Mars.