Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests,Ilam province,Iran)

Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the Zagros region. Yet, areas vulnerable to forest conversion are unknown. This study aims to predict the spatial distribution of deforestation in western Iran. Landsat images dated 1988, 2001, and 2007 are classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. Meanwhile, in order to examine deforestation factors’ investigation, deforestation maps with physiographic and human spatial variables are entered into the model. Areas vulnerable to forest changes in the Zagros forest region are predicted by a multilayer perceptron neural network (MLPNN) with a Markov chain model. The results show that about 19,294 ha forest areas are deforested in the last 19 years. The predictive performance of the model appears successful, which is validated using the actual land cover map of the same year from Landsat data. The validated map is found to be 94 % accurate. The validation is also tested using the relative operating characteristic approach which yielded a value of 0.96. The model is then further extended to predict forest cover losses for 2020. The MLPNN approach was found to have a great potential to predict land use/land cover changes because it permits developing complex, nonlinear models.     

Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

Analyzing drought history using Fuzzy Integrated Drought Index (FIDI): a case study in the Neyshabour basin,Iran

In arid to semi-arid climates, monitoring drought is very complicated because of different hydrometeorology variables effect on it. It is proposed in this paper to develop Fuzzy Integrated Drought Index (FIDI) which combines most important effective factors in developing drought. At first, Variable Infiltration Capacity (VIC) model calibrated simulated runoff to outlet basin runoff data for years 1993–1995. Results represent high performance of model in simulating runoff of outlet basin. Then, Precipitation Anomaly Percentage Index (PAPI), actual Evapotranspiration Anomaly Percentage Index (EAPI), Runoff Anomaly Percentage Index (RAPI), and Soil Moisture Anomaly Percentage Index (SMAPI) were constructed. FIDI was compared with the PAPI, RAPI and SMAPI for the period of 1985 to 2014. The results indicate that (1) the FIDI has more ability in determining start and persistence of drought event compared with PAPI, RAPI, and SMAPI; (2) in the low time scales, PAPI and SMAPI have high correlation with FIDI, and in the higher time scales, RAPI has the high correlation with FIDI; (3) spatially, the middle, west, and portion of north have higher drought risk in the Neyshabour basin.     

Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran)

Investigations of failures of soil masses are subjects touching both geology and engineering. These investigations call the joint efforts of engineering geologists and geotechnical engineers. Geotechnical engineers have to pay particular attention to geology, ground water, and shear strength of soils in assessing slope stability. Artificial neural networks (ANNs) are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are nonlinear. In this research, with respect to the above advantages, ANN systems consisting of multilayer perceptron networks are developed to predict slope stability in a specified location, based on the available site investigation data from Noabad, Mazandaran, Iran. Several important parameters, including total stress, effective stress, angle of slope, coefficient of cohesion, internal friction angle, and horizontal coefficient of earthquake, were used as the input parameters, while the slope stability was the output parameter. The results are compared with the classical methods of limit equilibrium to check the ANN model’s validity.     

Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India

Drought is a serious climatic condition that affects nearly all climatic zones worldwide, with semi-arid regions being especially susceptible to drought conditions because of their low annual precipitation and sensitivity to climate changes. Drought indices such as the standardized precipitation index (SPI) using meteorological data and vegetation indices from satellite data were developed for quantifying drought conditions. Remote sensing of semi-arid vegetation can provide vegetation indices which can be used to link drought conditions when correlated with various meteorological data based drought indices. The present study was carried out for drought monitoring for three districts namely Bhilwara, Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI) derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data. The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations, the correlation coefficient was high for different time scales. Bhilwara district having the best correlation for the 9-month time scale shows late response while Kota district having the best correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that the area was worst affected by drought in the year 2002. This was validated on the basis of NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured data and satellite data has a great potential in drought monitoring.     

Scale matching of multiscale digital elevation model (DEM) data and the Weather Research and Forecasting (WRF) model: a case study of meteorological simulation in Hong Kong

It is becoming easier to combine geographical data and dynamic models to provide information for problem solving and geographical cognition. However, the scale dependencies of the data, model, and process can confuse the results. This study extends traditional scale research in static geographical patterns to dynamic processes and focuses on the combined scale effect of multiscale geographical data and dynamic models. The capacity for topographical expression under the combined scale effect was investigated by taking multiscale topographical data and meteorological processes in Hong Kong as a case study. A meteorological simulation of the combined scale effect was evaluated against data from Hong Kong Observatory stations. The experiments showed that (1) a digital elevation model (DEM) using 3 arc sec data with a 1 km resolution Weather Research and Forecasting (WRF) model gives better topographical expression and meteorological reproduction in Hong Kong; (2) a fine-scale model is sensitive to the resolution of the DEM data, whereas a coarse-scale model is less sensitive to it; (3) better topographical expression alone does not improve weather process simulation; and (4) uncertainty arising from a scale mismatch between the DEM data and the dynamic model may account for 38 % of the variance in certain meteorological variables (e.g., temperature). This case study gives a clear explanation of the significance and implementation of scale matching for multiscale geographical data and dynamic models.     

Identification of hazardous waste landfill site: a case study from Zanjan province, Iran

The study of landfill sites is one of the most important studies in landfill engineering, and the landfill site selection involves combination of engineering, science, and politics. This paper describes a comprehensive hazardous waste landfill site selection methodology with the combined utilization of geographic information system and multiple criteria analysis methods, as applied to the Zanjan province in Iran. The six main data categories that were used are geological/engineering geological, geomorphological, hydrological/hydrogeological, climatological, pedological, and social/economical criteria, which included 31 input layers in total. A suitability map for hazardous waste landfilling was prepared for study area with five classes from most suitable to completely unsuitable. Finally, out of the three sites, one site was selected which was chosen by the local authorities. Our work offers a comprehensive methodology and provides essential support for decision-makers in the assessment of hazardous waste management problems in Zanjan province in I.R. Iran and other developing cities in other countries.     

Bivariate frequency analysis of low flow using copula functions (case study: Dez River Basin,Iran)

Accurate estimation of low flow as a criterion for different objectives in water resource management, including drought is of crucial importance. Despite the complex nature of water deficits, univariate methods have often been used to analyze the frequency of low flows. In this study, low flows of Dez River basin were examined during period of 1956–2012 using copula functions at the upstream of headbranches’ junction. For this purpose, at first 7-day series of low flow was extracted at the studied stations, then their homogeneity was examined by Mann–Kendall test. The results indicated that 7-day low flow series of Dez basin were homogenous. In the next stage, 12 different distribution functions were fitted onto the low flow data. Finally, for Sepid Dasht Sezar (SDS), Sepid Dasht Zaz (SDZ), and Tang Panj Bakhtiyari (TPB) stations, logistic distribution had the best fit, while for Tang Panj Sezar (TPS) station, GEV distribution enjoyed the best fit. After specifying the best fitted marginal distributions, seven different copula functions including Ali–Mikhail–Haq (AMH), Frank, Clayton, Galambos, Farlie–Gumbel–Morgenstern (FGM), Gumbel–Hougaard (GH), and Plackett were used for bivariate frequency analysis of the 7-day low flow series. The results revealed that the GH copula had the best fitness on paired data of SDS and SDZ stations. For TPS and TPB stations, Frank copula has had the best correspondence with empirical copula values. Next, joint and conditional return periods were calculated for the low flow series at the upstream of branches’ junction. The results of this study indicated that the risk of incidence of severe drought is higher in upstream stations (SDZ and SDS) when compared with downstream stations (TPB and TPS) in Dez basin. Generally, application of multivariate analysis allows researchers to investigate hydrological events with a more comprehensive view by considering the simultaneous effect of the influencing factors on the phenomenon of interest. It also enables them to evaluate different combinations of required scenarios for integrated management of basin and planning to cope with the damages caused by natural phenomena.     

A proposed geotechnical-based method for evaluation of liquefaction potential analysis subjected to earthquake provocations (case study: Korzan earth dam, Hamedan province, Iran)

During the earthquakes, a number of earth dams have had severe damages or suffered major displacements as a result of liquefaction, thus modeling by computer codes can provide a reliable tool to predict the response of the dam foundation against earthquakes. These modeling can be used in the design of new dams or safety assessments of existing ones. In this paper, on the basis of the field and laboratory tests and by combination of several software packages a seismic geotechnical-based analysis procedure is proposed and verified by comparison with computer model tests and field and laboratory experiences. Verification or validation of the analyses relies to the ability of the applied computer codes. By using the Silakhor earthquake (2006, M _s 6.1) as a basis in order to check the efficiency of the proposed framework, the procedure is applied to the Korzan earth dam of Iran which is located in Hamedan Province to analyze and estimate the liquefaction and safety factor. Design and development of a computer code by the authors which was named as the ??Abbas Converter?? with graphical user interface which operates as logic connecter function that can compute and model the soil profiles is the critical point of this study. The results confirmed and proved the ability of the generated computer code on the evaluation of soil behavior during earthquake excitations. Also, this code was able to facilitate this study better than previous ones have, taking over the encountered problem.     

Groundwater quality degradation of urban areas (case study: Tehran city,Iran)

Groundwater quality of Tehran city is considered in this study. Nine sampling stations were selected, and composite sampling campaign was performed in summer 2012. Groundwater sampled from northern stations appeared to have acceptable characteristics for agricultural and drinking uses. The southern station samples did not meet the required guidelines. Concentration of SO₄ ^2?, Na⁺ and Cl^? obey a sharp ascending trend southwards. Accordingly, the electrical conductivity of the last station at the very southern areas is more than fifteen times greater than that of the first northern station. Tehran city is located in a semi-arid climate and experiences long hot summers. High rates of evapo-transpiration within urban green spaces and agricultural lands facilitate the salinization phenomenon in root zones. As a result, excess irrigation water eases the consequent percolation into aquifers. Furthermore, saline water intrusion from salt marshes located down south of the city is an expected consequence of wells overpumping. Such case is especially remarkable in hot seasons when an increased urban water demand is observed. Remarkable sulfate concentrations in saline water are mainly justified by percolation of sulfate containing fertilizers which are broadly used by local farmers in an uncontrolled manner. Surface run-offs and municipal wastewater leakage may also trigger the salinization process.     

Assessing building vulnerability to tsunami using the PTVA-3 model: A case study of Chabahar Bay,Iran

Chabahar Bay, in southeastern Iran, lies at the north of the Gulf of Oman and close to the Makran Subduction Zone, which makes it a region that is susceptible to tsunamis. This bay has an increasingly important role in Iran’s international trade, and therefore the assessment of the regional vulnerability to the effects of a tsunami is vital. Based on both the details of historical events and the results of numerical modeling of the propagation pattern of a tsunami in this region, this study assessed the vulnerability of buildings within the Chabahar Bay region to a tsunami event. The Papathoma Tsunami Vulnerability Assessment (PTVA) model was used to calculate a relative vulnerability index (RVI) for the affected buildings based on their physical and structural characteristics. The results showed that in a postulated worst-case-scenario tsunami event in the Chabahar Bay area, approximately 60 % of the residential buildings would be affected, a level of damage that is categorized as “Average” in the RVI classification. Overall, the economic losses related to the damage of residential buildings due to a tsunami in the Chabahar Bay area are anticipated to be the equivalent of US$ 16.5 million.     

Eco-environmental change detection by using remote sensing and GIS techniques: a case study Basrah province, south part of Iraq

This study examines the temporal and spatial dynamics of eco-environment degradation processes and change detection as evident from land use survey datasets (1990–2003) for the Basrah province, Iraq was taken as a case. Using a combination of techniques of RS, GIS, and GPS, the study identifies five prominent eco-environment degradation processes, namely: desertification, secondary salinization, urbanization, vegetation degradation, and loss of wetlands. Rates of conversion were calculated and distribution patterns were mapped with the aid of GIS. The results suggest that wind erosion was the dominant cause of eco-environment degradation in more than half of the study area. Coupled with this were increases in salinization processes, affecting 17.6% of the land area in 2003. Overall, severe eco-environment degradation was noticed to be the dominant eco-environment degradation grade (61.9% of total area), followed by moderate eco-environment degradation (18.9% of total area) in 2003. Incorporation of both natural and anthropogenic factors in the analysis provided realistic assessment of the risk of land degradation. The study area, in general, is exposed to a high-risk of eco-environment degradation.     

Nonlinear site response evaluation of earthquakes: A case study from Sirdjan Balvavard Bridge,Kerman province,Iran

The aim of this study is to illustrate the soil conditions encountered at the proposed bridge sites to analyze and evaluate the test conducted, submit recommendations regarding foundation design. At first by field investigation, the required data was collected and after primary processing the acceptable data was selected. For nonlinear analysis of elastic and rigid half space bed rock, standard hyperbolic model was selected and performed, and the results were compared. The study clearly showed that the effect of bed rock on soil behavior during earthquake is one of the main factors controlling prediction of ground response. A critical aspect of this work was to develop and use a computer code “Abbas Converter” developed by the authors that has several advantages, such as quick installation, acting as a connecter function between the used softwares which can generate the input data corresponding to a defined format and finally, the results of this computer code can be easily exported to the other softwares used in this study. Moreover this code can make it easy to solve the problems encountered.     

PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations.     

Mapping of the soil texture using geostatistical method (a case study of the Shahrekord plain, central Iran)

Soil texture is a key variable that reflect a number of soil properties such as soil permeability, water holding capacity, nutrient storage and availability, and soil erosion. The main objective of this study was to produce the kriged maps of soils of the Shahrekord region, central Iran. One hundred four soil samples were collected on a 375-m² sampling grid from the depths of 0–30, 30–60, and 60–100 centimeter, and their particle sizes were determined using hydrometer method. The results showed a moderately spatial correlation in the soil particles among sampling soil layers and across the study area. Moreover, increasing clay and therewith observation of heavier soil textures is evident from surface to subsurface layers of the soils in the studied area due to rainfall and/or irrigation agriculture. These findings indicated that study of the soil texture variation with depth can be used as a clue for site-specific management and precision agriculture. Moreover, we suggest further analysis by using other data layers like topographical parameters, land use, parent material, soil erosion, and any other information which might influence the spatial distribution of soil texture.     

The effects of removing bed materials on flow velocity (case study: Zaremrood River, Iran)

Nowadays, in parts of Iran, rivers and flood plains are being used as sand and silt mines, and the removal of river bed materials is performed without studying its effects on hydraulic behavior. On the other hand, the flood plain lands are in danger of floods and bank erosion. Zaremrood River in Tajan watershed due to removal of river bed material, two planes of before and after removal with scale of 1:1,000, has been used as basic data. The field investigation was emphasized on the end part of Zaremrood with a length of 5 km and starts from Ghandikola village to Ahoodasht Bridge. Using total station and field observations, the characteristics of reaches and cross sections of right bank, left bank, and main bed of river are written separately. Using software of HEC-RAS, ArcView 3.2, and extension HEC-GeoRAS, the flood zoning with different return periods to investigate water velocity and its changes, geometrical simulation of the bed, sides and flood way of rivers, and then by entering the results of HEC-GeoRAS into hydraulic software HEC-RAS for two before and after planes have been performed, and flow velocity was analyzed for three return periods of 10, 50, and 100 years. The results of this research showed that the velocities due to removal for floods with different return periods have increased, whereas water height and level during removal period have decreased.     

Integration multisource data for mineral exploration by using fuzzy logic,case study: Taknar deposit,NE of Iran

The Taknar Zone is located at the northern margin of the eastern Iranian continental microplate, and it is host to the Taknar massive sulfide deposit. This study was conducted to find new exploration targets. We used multiple data sources (e.g., litho-geochemical and magnetic surveys) to produce more effective predictive maps. Principal component analysis and hierarchical cluster analysis methods were used to organize the new information into favorability maps and to determine multi-element correlations. We then employed fuzzy logic modeling to create favorability maps from geochemical and magnetic data. A concentration–area multifractal method was used to evaluate the final integrated favorability map for massive sulfide exploration. Our new map identifies previously unexploited sites in the eastern part of the study area, near the boundary of the Taknar formation, with intrusive and subvolcanic rocks, with potential for mineral exploration. The newly defined targets are attractive because old mined ore bodies are also identified in the favorability map.     

Earth dam site selection using the analytic hierarchy process (AHP): a case study in the west of Iran

Earth dam site selection is one of the most important problems in water resources management. It depends on a set of qualitative and quantitative criteria, and they may even be in conflict with each other. This study aims to develop a multicriteria decision-making approach to locate the dam site and construct a multipurpose earth dam in Harsin City at the western part of Iran. For this purpose, firstly, the influential criteria for locating the earth dam site were determined using a comprehensive literature review and the experts’ opinions. Then, some watersheds in the surrounding areas of Iran’s Harsin City were studied and four feasible sites proposed. In the final stage, these sites, in order to construct a multipurpose earth dam, were prioritized using the analytic hierarchy process approach and the most optimal site was selected.     

The role of blocking system in heavy precipitation of Iran (a case study: southeast of Iran January 2008)

This article concerns the analysis of the heavy precipitation, which allows investigating the effect of the blocking system on the unusual precipitation and temperature occurrence in Iran. The days of January 2008 have been the coldest days during the history of recorded data in Iran. Variation of precipitation during January 2008 compared with long-term data (30 years) shows the maximum positive anomaly in the stations located in southeast of Iran. However, the precipitation in consecutive days, 14–15 and 15–16 of January, produce a more important proportion of the heavy precipitation in this region. In order to study the role of the blocking system related to heavy precipitation in January 2008, the position and movement of the atmospheric systems including cyclones, anticyclones, fronts, and wind fields have been analyzed by the use of synoptic maps by the environment to circulation approach. Consequently, the weather maps indicated that the blocking system over the north of the Caspian Sea has caused the relatively deep low trough on January 5, 7, 14, 15, and 16, 2008, while the thermal and moisture gradients in the warm section of air masses have produced heavy precipitation. As a result, wind field of low levels (850 hPa) provided remarkable moisture fed by the Arabian Sea, Oman Sea, and Persian Gulf in the study area. Furthermore, the speed of wavelength and the position of the blocking system associated with the heavy precipitation can be clearly identified.