Journal of Spatial Analysis

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Resilience are concepts that are finding increasing currency in several fields of research as well as in various policy and practitioner communities engaged in global environmental change science, climate change, sustainability science, disaster risk-reduction and famine interventions (Vogel, et.al, 2007). Where the risk is a probability of damage, injury, liability, loss, or any other negative occurrence that is caused by external or internal vulnerabilities, and that may be avoided through preemptive action (Benson, et.al, 2004). Among natural disasters, earthquakes, due to the unpredictable nature of these events, are one of the most destructive. Iran is one of the most earthquake-prone countries in the world that its cities most affected by this phenomenon. Among the cities of Iran, Tehran, as the country's first metropolis, due to dense population, poor physical development, structural density, and lack of standards, is potentially facing a serious threat. The purpose of this study is to investigate the spatial flexibility of Tehran over the region 12 after the earthquake incidence.

The present study is dealt with the data preparing and analysis using geospatial methods. The several geospatial data such as Peak Ground Acceleration (AGA) map, urban structure, infrastructure and population collected from Tehran Disaster Management Center were provided and analysis based some GIS known algorithms. In other to urban spatial resilience zonation the AHP (analytical Hierarchy Process) was implemented to generation risk map. Finally OWA (Ordered Weighted Average) method was implemented in order to Production spatial flexibility map of earthquake incidence over the District 12 of Tehran. AHP model uses of priorities straight experts, but OWA provides of control the level of compensation and risk-taking in a decision. Using the conceptual of fuzzy quantifier with OWA makes the qualitative data analysis enter to decision.

    According to flexibility of the final map with fuzzy operator (All) equivalent to the operator MIN, the worst result Was obtained and resulting the highest risk and lowest flexibility respectively (Districts Nos. 2,12,7,8 and 11).By taking all the criteria of a criterion without compensation by other criteria as "non-risk" is obtained .

Map obtained with fuzzy operator (Half) has the high potential to provide suitable options,  because in addition to integration criteria the importance of each parameter based on the weight given to the criteria are considered. In this map Districts Nos.2.6 and 8 (Baharestan, Emamzadeyahya and Sanglajedarkhangah) respectively were most Risk to earthquakes and therefore less flexibility to the earthquake. The map obtained with the fuzzy operator "Atleast one" is equivalent to MAX operator districts Nos. 2,12,7 and 8 (Baharestan ,DarvazehGhar of Shush,Abshardardar and Sanglajedarkhangah)  respectively were most Risk to earthquakes and therefore less flexibility to the earthquake.

The fuzzy conceptual map quantifier showed that districts Nos. 2 and 12 (Baharestan and DarvazehGhar of Shush) were most vulnerable and therefore less flexibility to the earthquake as final results.

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• What is the status of rural households' vulnerability to shocks?
• What is the status of rural households' vulnerability to trends?
• What is the status of rural households' vulnerability to seasonality?

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Evaluation and Evaluation of Resilience to Drought Hazards in Rural Areas Case Study: rural district Ghare Taghgan Neka
 
Extensive abstract
Concerns over the social, economic, and ecological impacts of climate change on human habitat have increased over the last few decades (McGranahan et al, 2007). According to the UN report, water shortages will occur in the near future in 18 countries, and by 2025 more than two-thirds of the world's population is projected to be in serious water shortages (Pozzi et al, 2013, 191 112; World Bank, 2008, 124). On the other hand, the number of disasters has increased over the past 20 years, reaching 400 from 200 accidents (Pittman et al, 2011, 83,94; Molen et al, 2011, 765-773). In this regard, droughts, the most dangerous natural disasters, affect a wide range of climates and ecosystems, and the geographical areas affected by them have increased rapidly in the last four decades (Kamara et al., 2018, 2318). Drought is a major threat to households and community’s dependent on agriculture for livelihoods (Anthopoulou et al, 2017). Because livelihoods in agricultural-dependent societies are more dependent on climate change (Pittman et al., 2017. (
It is central to the city of Neka; it requires a holistic perspective. The type of applied research and methodology used is descriptive-analytical. Librarian and field method (observation, questionnaire) were used to collect the required data. In order to assess the resilience of rural areas to drought in two economic dimensions (ability to return to employment and income generation and compensation of costs and losses) and in social dimension (awareness, knowledge, skills and preparedness and participation and Collaboration) Designed and developed a Likert-type questionnaire (¬1 very low, 5 = very high). The statistical population of this study is based on census of 6947 households in 24 villages with error of 0.07, ¬191 questionnaire as sample size and based on the relationship of sharing ratio of the number of samples in each village is specified and in villages less than 7 samples, the number has been upgraded to 7. Finally, 233 samples were used as the sample size based on questionnaire completion and analysis and were randomly distributed and distributed among households. To assess the validity of the questionnaire, the experts were first provided with validity and the validity of the research instrument was measured and the final indices and statements were extracted. The questionnaire developed at this stage was pre-tested in the study area and after confirmation of trust or reliability (Cronbach's alpha value of 0.84) the questionnaire was finalized for field research. Descriptive (inferential) and inferential statistical methods (single sample t, Friedman, cluster analysis) and VASP, ARAS and VIKOR models were used to analyze the data. Therefore, in order to evaluate and measure resilience in rural areas, firstly, annual moisture status was investigated based on SPI, SDI and GRI indices. Therefore, the moisture status of the study area during the 13-year statistical period (2006-2007) using the data of 9 Standard Precipitation Index (SPI) rain gauge data, the data of the discharge rate of 6 rivers Surface Flow Index (SDI) and the values Water level level of 9 piezometric wells of GRI index was calculated in DrinC software environment.
 Iran's position on the dry belt and the persistence of droughts over the past two decades have led to the emergence of drought-related crises, especially for villagers who are heavily dependent on water for production, due to climate change. Droughts in the study area were also not exempt from this rule and resulted in adverse effects beyond the normal state and the risk of drought among rural farmers, which could be due to their low level of resilience to this risk. Be it. Therefore, the present study aimed to investigate the economic and social effects of this phenomenon on the status of rural resilience against drought hazards. The results showed that among the sample villages, in the economic dimension, the highest average belonged to Tavaza Abad village of Bostan Khel with average of 3.11 and the lowest average belonged to Plazhartesh village with average of 1.63. In the social aspect, the highest average belongs to the village of Dukhaelo with a mean of 3.54 and the lowest average belongs to the village of Plazhartesh with an average of 1.55. Also, the average real perception of the respondents is less than three and is moderately low, indicating that rural resilience to drought is low.
 
Keywords: Rural Areas, Resilience, Drought, Gharaghgan Village

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Comparison of the effectiveness of four artificial intelligence methods in predicting drought
Abstract
Problem statement:
Drought is a temporary disorder whose characteristics vary from region to region, therefore, it is not possible to define a complete and absolute definition of drought. Drought is one of the most important natural disasters that can occur in any climate regime. Since drought is unavoidable, it is important to know it in order to optimally manage water resources. Drought prediction can play an important role in managing this phenomenon. In other words, recognizing and predicting this phenomenon is one of the topics of interest for scientists who are interested in solving the problem of water shortage. More than 80% of Iran's area is covered by arid and semi-arid regions and lack of rain is a predominant phenomenon in this region. So far, several methods have been proposed to predict drought. Each method offers different results in specific conditions.  Therefore, identifying the best method for predicting drought in the climatic conditions of central Iran is essential.
 
Material and methods:
In this research, in order to introduce a suitable method for predicting drought for the next month, four methods of artificial intelligence including Deeplearning (using the Alexnet network, one of the convoluted networks), K nearest neighbor algorithm (KNN), multi-class Support vector machines (SVM-MultiClass) and decision tree have been used. Monthly rainfall data from 11 syntactic stations of Yazd province during the 29-year statistical period (1988 to 2017) were used as experimental data. Standardized precipitation index (SPI) was calculated to indicate drought status in terms of severity and duration on 1, 3, 6, 9, 12 and 24 month time scales. Precipitation data was used as neural network input and SPI classification as network output and 80 percent of the data was used for training and 20 percent for testing the networks.
In this study, the Recurrence Plot method was used to interpret the time series to convert these series into images and RG and B pages were created. To convert rainfall data into photos at 1, 3, 6, 9, 12 and 24 month time scales, photo layers were created using a standardized rainfall formula, and by merging these three output layers into colored photos and black and white photos were obtained. Using three pages created in MATLAB software and merging them, the output was in the form of a photo, which was placed as the input of the Alexnet network. Combination of Recurrence Plot to create images and deep learning network for classification of drought data has been used for the first time in this research. To evaluate the effectiveness of the classification strategy, standard criteria of accuracy, micro-F1 and macro-F1 were used.
 
Results Description and interpretation:
 The results showed that all networks were able to predict drought. However, on short time scales such as 3 and 9 months, the accuracy assessment criteria for some classes are zero and the methods of learning from these classes are practically ignored due to their lack of data. But on a larger time scale, this issue has been addressed and the data of those classes are well categorized. Deep learning network with image input could not predict well in the short term due to lack of data, but in the long term due to increased data has improved its performance and had the best performance. The SVM method at different time scales has shown unreliable and variable behaviors that can not be said to be a suitable method for predicting drought at different time scales. Decision Tree and KNN methods have been able to predict drought better in the short term than in the long term. The two methods have been closely related. .Based on the Deeplearning network macro-f1 evaluation criterion, the one-month time scale with 22.71% was the most inefficient method and the Decision Tree with 64.65% was the most efficient method, But with the increase in time scale, the Deeplearning network improved its performance, so that at the 24-month time scale with 65.35%, the best performance for the Deeplearning network followed by the SVM-MultiClass network with 57.40%. For future research, it is suggested that Decision Tree and KNN methods be used to predict short-term drought. In this study, with increasing the time scale and increasing the data used, these two methods have lost their effectiveness compared to the short term.
 
key words: Drought, Standardized Precipitation Index, Artificial Intelligence, Deep Learning, Alexent, Recarence Plot
 

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Risk assessment of Maroun gas and oil pipelines due to land sliding hazard

based on D-InSAR technique

Mohammad Sharifikia, @ Associate professor, Tarbiat Modares University, Department of Remote Sensing-

Iran

Meral Poorhamzah, postgraduate in Remote Sensing, Tarbiat Modares University

Abstract
It is importance to note that Iranian oil company have to transfer this valuable enrage from one side to other side of
country passing form several ridge and valley prone with several natural hazard. This is because the natural sources
of oil and gas generally lied in south west part of Iran locally calling Manathegh Nafte Khize Jonoub (south oil field
area). This area is closed to one of most active geological zone of Iran (Zakrose) covering thousands of kilometer
from south east to north west. Supplying natural enrages to central port of country need to crossing from this zone
which is suffering with several difficulties as well as neutral hazard. Out of neutral hazards can found to excite in
this area, the landslide hazard is a main restriction for pipeline crossing over.
The present research is dale with radar interferometry techniques applying for risk assessment and mapping over the
oil and gas pipelines suffering to landslides hazard in the part of Central Zagros (Maroun-Esfahan). For this purpose,
two individual radar dataset in C (ASAR) and L (PALSAR) band with deferent time were collected. Furthermore,
the D-InSAR technique was applied for land surface movement and land displacement detection. The outcome map
was showed the maximum rate of land displacement in this region is about 7.4 cm uplifted and 3.9 cm subsidence
with duration of almost one year. this is due to shape of landslide over the area’s slop. Overlying the landslide map
with the pipeline crossing route shown at lies three active landslides over the Maroun-Esfahan gas and oil pipelines.
For investigation about this three landslide and damage estimation over the pipeline the field study has been done
for accuracy assessment and land movement rat measuring and evaluation. Which, successfully identified and
mapped 3 landslides were located across the pipeline and damage it. Furthermore, map surveying by DGPS in RTK
method over the one of landslide shown that sliding transfer 20 m with falling 10 m over the length of 45 m of gas
pipeline. moreover, the press of landslide made curvatures on straight pip hogging pipe 43 cm. continued this
landslide activation and more pressing in close further can make a fracture and pessimistic pipe expulsion. With can
a kind of disaster if the event be close to settlements are.
The outcome landslide map shown the active landslide points (small area) very well, but the main think need to
suffusion information about interred area. For this exigency have to convert points data map to area as prediction
hazard. For this proses and to understanding the amplitude of landslide hazard in area the information value model
was applied for hazard zonation and mapping. The landslide hazard map resulting from D-InSAR technique as
inventory map along with 8 data set maps namely, lito-logy, soil, land cover, lineaments, faults, roads, derange
pattern and slop, has been interred to model for zonation and hazard estimation over the area. Furthermore, this map
was reclass in 5 individual hazard and risk class from low to high risk. The hazard map analyses and calculation was
show about 20 percent of area study was marked as high and very high risk zone. This is mainly because of
morphological and lito-logical exclusivity of area resulting by active tectonics. Crooning and overlaying the
landslide hazard map with pipeline track has been shown 28.5 percent of line length crossing over the high and very
high risk zone, where the 52 percent was prone with low and very low risk zone. This mine that near 1/3 of mention
pipeline length suffering from hazardous area which can classified as high risk part of pipeline.
Interpreting the hazardous classes on the prediction map is an important concern in landslide prediction models. For
this purpose, the prediction-rate curve was generated using validation group of landslide locations to validate the
prediction map obtained. This rate curve explains how well the model and factors predict the landslide. Results from
the success-rate curve are very promising, since the 3% area predicted as the most hazardous, includes 42.35% of
the total area affected by landslides, and this value grows to 90%, when about 25% area of highest susceptibility is
considered. The prediction accuracy can be assessed qualitatively by calculation the area under cover. The total area

equal to one means perfect prediction accuracy. In this model ratio area was 0.633 that means the prediction
accuracy was 63.3%.
Keywords: Differential SAR Interferometry, PALSAR, ASAR, Landslide, Oil and Gas Pipeline risk

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Abstract
The aim of this study was to analyze the temporal and spatial nature of dust storms during the period 2016 to 2018 in Kermanshah Using the HYSPLIT routing model and the MCD19A2 product, the Modis sensor was performed in the Google Earth web engine.In order to route the origin of dust particles, the Lagrangian method of HYSPLIT model was used in 48 hours before the occurrence of dust phenomenon in Kermanshah at three altitude levels of 200, 1000 and 1500 meters.Findings from HYSPLIT model tracking maps indicate that the general route for dust transfer to the study area is the north-west-southeast route with the origin of the deserts of Iraq and Syria at three altitudes of 200, 1000 and 1500 meters. On June 17, 2016 and October 27, 2018, as well as the southwest-west route originating in Kuwait, Northern Saudi Arabia and part of Iraq on November 2, 2017.The results of the maps obtained from the MCD19A2 product of the Modis sensor, especially the maps of periodicity, cumulative concentration, spatial variation and the highest AOD map, show a high correlation with the routed maps extracted from the HYSPLIT model. In general, based on the findings of the maps extracted from the product MCD19A2, Modis sensor during the period 2016 to 2018 in Kermanshah, the central and eastern regions have always been more affected by dust storms than in other parts of the city. On average, they were more exposed to dust pollution than other parts of the city. In this regard, the final results show a high correlation between the actual PM10 data and the AOD values derived from the MODIS sensor.

Keyword: Dust, AOD, Modis, HYSPLIT, Kermanshah, Google Earth Engine