PROMETHEE II and fuzzy AHP: an enhanced GIS-based landslide susceptibility mapping

This paper describes the application of a well-known multi-criteria decision-making technique, called preference ranking organization method for enrichment evaluation (PROMETHEE II), in combination with fuzzy analytical hierarchy process (FAHP), as a weighting technique to explore landslide susceptibility mapping (LSM). To this end, eight landslide-related geodata layers of the Minoo Dasht located in the Gorgan province of Iran, involving slope, aspect, distance to river, drainage density, distance to fault, mean annual rainfall, distance to road and lithology have been integrated using the PROMETHEE II enhanced by FAHP technique. Afterward, the receiver operating characteristics (ROC) curves for the proposed LSM were drawn using an inventory of landslides containing 83 recent and historic landslide points, and the area under curve = 0.752 value was calculated accordingly. Additionally, to further verify the practicality of such susceptibility map, it was also evaluated against the landslide inventory using simple overlay. The outcome was that about 11 % of the occurred landslide points fall into the very high susceptibility class of the LSM, but approximately 52 % of them indeed fall into the high and very high susceptibility zones together. Also, it resulted that no recorded landslide occurred in the zone of very low susceptibility. According to the results of the ROC curves analysis and simple overlay evaluation, the produced map has exhibited good performance.     

Land assessment for flood spreading site selection using geospatial information system

Irregular patterns of precipitations from temporal as well as spatial perspectives not only cause destructions but also waste surface water resources. Hence, controlling surface water and leading the flood to underground stores improve the efficiency of water usage. Selecting appropriate sites for optimal use of water floods is one of the most important factors in recharging underground water tables in dry lands where the agricultural and rangelands are vulnerable. Traditional methods of site selections are, however, time consuming and error prone. This paper attempts to; analyze existing schemes of site selection; introduces an appropriate method of locating flood-spreading sites using Geospatial Information System; implements the strategy in a case study; and scientifically assesses its results. The study area of this research is Samal sub-basin covering 31571.7 ha of Ahrom basin in Boushehr province. In the present research, factors pertain to earth sciences (quaternary units, slope and landuse) and hydrology (runoff infiltration rate and aquifers’ depth) are considered. Information layers are weighted, classified and integrated through several models such as boolean logics, index overlay and fuzzy logics. The results are then checked against the existing sites to estimate their accuracy. The results of this research demonstrated that fuzzy logic operators including gamma=0.1, gamma=0.2 and products of fuzzy logics yield the best when compared to control fields and therefore, the models are introduced as the most suitable site selection strategies for flood spreading.     

Application of Remote Sensing in Monitoring Unsustainable Wetlands: Case Study Hamun Wetland

Monitoring wetland as one of the important parts of the global ecosystem is necessary for conservational programs. But, usually, collecting in situ data is restricted in these areas because of their remote locations, vast area and dynamic conditions. Remote sensing provides a cost effective tool to investigate hydrological patterns and the seasonal trend of changes in wetlands. In this paper, Land-use/land-cover change during water inundation period of Hamun wetland was investigated in order to determine change trend during this period. Hamun wetland is an unsustainable ecosystem, and monitoring this wetland is essential for conservation goals. This trend is critical for decision makers in order to plan the conservational scheme in all unsustainable ecosystems. To reach this objective, the land-use/land-cover maps during inundation period of Hamun were produced using Landsat 8 time series images. The results of accuracy assessment showed the classification of water and vegetation have the highest accuracy (94% and 93%, respectively). And the accuracy of plants in the water classes was the lowest (water–veg?=?89.9%, veg–water 1?=?88.8%, veg–water 2?=?87.6%). This means the higher misclassification is in determining the vegetation in the water. Then, the changes in the land-cover classes in relation to wetland inundation were investigated. Results of land-use/land-cover change illustrate the regions that were suitable for water birds but lost their suitability when the wetland dried out. These areas are crucial for water bird’s conservation. Satellite data determined these areas with acceptable accuracy.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

Influence of mid-latitude circulation on upper Indus basin precipitation: the explicit role of irrigation

Since much of the flow of the Indus River originates in the Himalayas, Karakoram and Hindu Kush Mountains, an understanding of weather characteristics leading to precipitation over the region is essential for water resources management. This study examines the influence of upper level mid-latitude circulation on the summer precipitation over upper Indus basin (UIB). Using reanalysis data, a geopotential height index (GH) is defined at 200 hPa over central Asia, which has a significant correlation with the precipitation over UIB. GH has also shown significant correlation with the heat low (over Iran and Afghanistan and adjoining Pakistan), easterly shear of zonal winds (associated with central Asian high) and evapotranspiration (over UIB). It is argued that the geopotential height index has the potential to serve as a precursor for the precipitation over UIB. In order to assess the influence of irrigation on precipitation over UIB, a simplified irrigation scheme has been developed and applied to the regional climate model REMO. It has been shown that both versions of REMO (with and without irrigation) show significant correlations of GH with easterly wind shear and heat low. However contrary to reanalysis and the REMO version with irrigation, the REMO version without irrigation does not show any correlation between GH index and evapotranspiration as well as between geopotential height and precipitation over UIB, which is further confirmed by the quantitative analysis of extreme precipitation events over UIB. It is concluded that although atmospheric moisture over coastal Arabian sea region, triggered by wind shear and advected northward due to heat low, also contribute to the UIB precipitation. However for the availability of necessary moisture for precipitation over UIB, the major role is played by the evapotranspiration of water from irrigation. From the results it may also be inferred that the representation of irrigated water in climate models is unavoidable for studying the impact of global warming over the region.     

A new class of bulk viscous universe with time dependent deceleration parameter and Λ-term

A new class of exact solutions of Einstein’s field equations with a bulk viscous fluid for an LRS Bianchi type-Ia obtained by using a time dependent deceleration parameter and cosmological term Λ. The coefficient of bulk viscosity is assumed to be a power function of mass density (ξ=ξ ₀ ρ ⁿ ). We have obtained a general solution of the field equations from which six models of the universe are derived: exponential, polynomial and sinusoidal form respectively. The behaviour of these models of the universe are also discussed in the frame of reference of recent supernovae Ia observations.      

A Comparative Study of Pollutant Removal Stability in Hybrid Wetland Columns

This study investigated the effects of two alternative substrates(wood mulch and zeolite) on the performance of three laboratory-scale hybrid wetland systems that had identical system components and configurations.Each system consisted of a vertical flow(VF) wetland column,followed by a horizontal flow(HF) column and a vertical flow column.The substrates employed were wood mulch,gravel and zeolite,and Phragmites australis were planted in each column.The systems received synthetic wastewater,with pollutant loadings in the range of 8.5-38.0 g/(m2·d) total nitrogen(TN) and 4.0-46.4 g/(m2·d) biological oxygen demand(BOD5).Wood mulch and zeolite substrates showed higher efficiencies in terms of removing nitrogenous compounds and biodegradable organics.The supply of organic carbon from the organic mulch substrates enhanced denitrification,while adsorption of influent ammoniacal nitrogen(NH4-N) in zeolite played a major role in the removal of nitrogenous species in the wetland columns.Overall,the average percentage removals of TN and BOD5 reached >66% and >96% respectively,indicating stable performances by the hybrid wetland systems under the experimental loading ranges.Mathematical models were developed,based on the combination of Monod kinetics and continuously-stirred tank reactor(CSTR) flow patterns to describe the degradation of nitrogenous compounds.Predictions by the models closely matched the experimental data,indicating the validity and potential application of Monod kinetics in the modelling and design of treatment wetlands.