Variable equation of state for Bianchi type-VI<Subscript>0</Subscript> dark energy models

We present dark energy models in an anisotropic Bianchi type-VI₀ (B-VI₀) space-time with a variable equation of state (EoS). The EoS for dark energy ω is found to be time dependent and its existing range for derived models is in good agreement with the recent observations of SNe Ia data (Knop et al. in Astrophys. J. 598:102 2003), SNe Ia data with CMBR anisotropy and galaxy clustering statistics (Tegmark et al. in Astrophys. J. 606:702, 2004b) and latest a combination of cosmological datasets coming from CMB anisotropies, luminosity distances of high redshift type Ia supernovae and galaxy clustering (Hinshaw et al. in Astrophys. J. Suppl. 180:225, 2009; Komatsu et al. in Astrophys. J. Suppl. 180:330, 2009). The cosmological constant Λ is found to be a positive decreasing function of time and it approaches a small positive value at late time (i.e. the present epoch) which is corroborated by results from recent supernovae Ia observations. The physical and geometric aspects of the models are also discussed in detail.     

Coupling of remote sensing data aided with field investigations for geological hazards assessment in Jazan area,Kingdom of Saudi Arabia

The city of Jazan is situated on the eastern flank of the Read Sea and considered as one of the fastest growing cities in the Kingdom of Saudi Arabia. This zone attracts a lot of investors for various development projects. Recently, many new projects have been implemented and constructed in this region including new urban areas, infrastructures, and industrial projects. However, historically this area has been challenged from different types of geological hazards. These geological hazards are catastrophic events that can cause human injury, loss of life, and economic devastation. The current study is aimed at evaluating the different types of geological hazards in Jazan city. This study is based on interpretation of satellite data such as LANDSAT and QuickBird images, existing geological maps, and physiographical characteristics with the help of field and laboratory analyses. The results of the analysis indicate that there exist various types of geological hazards in the study area mostly related to the natural factors which include (1) Sabkha soil; (2) Salt dome; (3) Loess soil; and (4) Sand dune/drift. Further, the findings of this study revealed that, most of these geological hazards have a severe impact on the ongoing development activities in Jazan area.     

The relationship between geology and rock weathering on the rock instability along Mugling–Narayanghat road corridor, Central Nepal Himalaya

The present study was conducted along the Mugling–Narayanghat road section and its surrounding region that is most affected by landslide and related mass-movement phenomena. The main rock types in the study area are limestone, dolomite, slate, phyllite, quartzite and amphibolites of Lesser Himalaya, sandstone, mudstone and conglomerates of Siwaliks and Holocene Deposits. Due to the important role of geology and rock weathering in the instabilities, an attempt has been made to understand the relationship between these phenomena. Consequently, landslides of the road section and its surrounding region have been assessed using remote sensing, Geographical information systems and multiple field visits. A landslide inventory map was prepared and comprising 275 landslides. Nine landslides representing the whole area were selected for detailed studies. Field surveys, integrated with laboratory tests, were used as the main criteria for determining the weathering zones in the landslide area. From the overall study, it is seen that large and complex landslides are related to deep rock weathering followed by the intervention of geological structures as faults, joints and fractures. Rotational types of landslides are observed in highly weathered rocks, where the dip direction of the foliation plane together with the rock weathering plays a principle role. Shallow landslides are developed in the slope covered by residual soil or colluviums. The rock is rather fresh below these covers. Some shallow landslides (rock topples) are related to the attitude of the foliation plane and are generally observed in fresh rocks. Debris slides and debris flows occur in colluviums or residual soil-covered slopes. In few instances, they are also related to the rock fall occurring at higher slopes. The materials from the rock fall are mixed with the colluviums and other materials lying on the slope downhill and flow as debris flow. Rock falls are mainly related to the joint pattern and the slope angle. They are found in less-weathered rocks. From all these, it is concluded that the rock weathering followed by geological structures has prominent role in the rock slope instability along Mugling–Narayanghat road section and its surrounding regions.     

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling�CNarayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling?CNarayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75?%) were randomly selected for building landslide susceptibility models, while the remaining 80 (25?%) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16?%. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57?% of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80?% accuracy (i.e. 89.15?% for IOE model, 89.10?% for LR model and 87.21?% for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling?CNarayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Response of west Indian coastal regions and Kavaratti lagoon to the November-2009 tropical cyclone Phyan

Response of the coastal regions of eastern Arabian Sea (AS) and Kavaratti Island lagoon in the AS to the tropical cyclonic storm `Phyan??, which developed in winter in the south-eastern AS and swept northward along the eastern AS during 9?C12 November 2009 until its landfall at the northwest coast of India, is examined based on in situ and satellite-derived measurements. Wind was predominantly south/south-westerly and the maximum wind speed (U<sub>10</sub>) of ~16 m/s occurred at Kavaratti Island region followed by ~8 m/s at Dwarka (Gujarat) and ~7 m/s at Diu (located south of Dwarka) as well as two southwest Indian coastal locations (Mangalore and Malpe). All other west Indian coastal sites recorded maximum wind speed of ~5?C6 m/s. Gust factor (i.e., gust-to-speed ratio) during peak storm event was highly variable with respect to topography, with steep hilly stations (Karwar and Ratnagiri) and proximate thick and tall vegetation-rich site (Kochi) exhibiting large values (~6), whereas Island station (Kavaratti) exhibiting ~1 (indicating consistently steady wind). Rainfall in association with Phyan was temporally scattered, with the highest 24-h accumulated precipitation (~60 mm) at Karwar and ~45 mm at several other west Indian coastal sites. Impact of Phyan on the west Indian coastal regions was manifested in terms of intensified significant waves (~2.2 m at Karwar and Panaji), sea surface cooling (~5°C at Calicut), and moderate surge (~50 cm at Verem, Goa). The surface waves were south-westerly and the peak wave period (T <sub>p</sub>) shortened from ~10?C17 s to ~5?C10 s during Phyan, indicating their transition from the long-period `swell?? to the short-period `sea??. Reduction in the spread of the mean wave period (T _z) from ~5?C10 s to a steady period of ~6 s was another manifestation of the influence of the cyclone on the surface wave field. Several factors such as (1) water piling-up at the coast supported by south/south-westerly wind and seaward flow of the excess water in the rivers due to heavy rains, (2) reduction of piling-up at the coast, supported by the upstream penetration of seawater into the rivers, and (3) possible interaction of upstream flow with river run-off, together resulted in the observed moderate surge at the west Indian coast. Despite the intense wind forcing, Kavaratti Island lagoon experienced insignificantly weak surge (~7 cm) because of lack of river influx and absence of a sufficiently large land boundary required for the generation and sustenance of wave/wind-driven water mass piling-up at the land?Csea interface.     

Performance assessment of evapotranspiration estimated from different data sources over agricultural landscape in Northern India

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydrometeorological datasets. This study addresses the performance analysis of reference evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state-of-the-art Hamon’s and Penman-Monteith’s methods were utilized for the ETo estimation in the Northern India. The performance indices such as bias, root mean square error (RMSE), and correlation (r) were calculated, which showed the values 0.242, 0.422, and 0.959 for NCEP data (without downscaling) and 0.230, 0.402, and 0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with bias, RMSE, and correlation values of 0.154, 0.348, and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less computationally expensive and easily available than performing WRF simulations.     

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.     

Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy process

Urban, industrial, and tourist developments are considered of high priority in Egypt. In the current research, the site suitability investigation for rating the different environmental, geological, and geotechnical conditions facing civil engineering projects were assessed using a geographic information system (GIS) multi-criteria approach. The study area is one of the most promising areas for urban and touristic as well as industrial developments in Egypt, which is located on the NW coast of the Gulf of Suez. This area may face several geo-environmental problems that will limit its suitability for civil projects. Weighted GIS model, which integrates different types of data sources, such as land use/cover, geological, geomorphological, geophysical, environmental, remote sensing, and field data, can be achieved to create a site suitability map. In this paper, an analytical hierarchy process approach has been used to develop the weighted model for different factors. As a result of this study, areas of potential geotechnical and geo-environmental hazards that could impact the design and construction of civil projects were identified. Therefore, changes can be made early in the design process before significant design efforts are being invested.     

Landslide susceptibility mapping at Zonouz Plain,Iran using genetic programming and comparison with frequency ratio,logistic regression,and artificial neural network models

Without a doubt, landslide is one of the most disastrous natural hazards and landslide susceptibility maps (LSMs) in regional scale are the useful guide to future development planning. Therefore, the importance of generating LSMs through different methods is popular in the international literature. The goal of this study was to evaluate the susceptibility of the occurrence of landslides in Zonouz Plain, located in North-West of Iran. For this purpose, a landslide inventory map was constructed using field survey, air photo/satellite image interpretation, and literature search for historical landslide records. Then, seven landslide-conditioning factors such as lithology, slope, aspect, elevation, land cover, distance to stream, and distance to road were utilized for generation LSMs by various models: frequency ratio (FR), logistic regression (LR), artificial neural network (ANN), and genetic programming (GP) methods in geographic information system (GIS). Finally, total four LSMs were obtained by using these four methods. For verification, the results of LSM analyses were confirmed using the landslide inventory map containing 190 active landslide zones. The validation process showed that the prediction accuracy of LSMs, produced by the FR, LR, ANN, and GP, was 87.57, 89.42, 92.37, and 93.27 %, respectively. The obtained results indicated that the use of GP for generating LSMs provides more accurate prediction in comparison with FR, LR, and ANN. Furthermore; GP model is superior to the ANN model because it can present an explicit formulation instead of weights and biases matrices.