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1.
The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.  相似文献
2.
Contaminations of groundwater by heavy metals due to agricultural activities are growing recently. The objective of this study was to evaluate and map regional patterns of heavy metals (Cd, Zn and Cu) in groundwater on a plain with high agricultural activities. The study was conducted to investigate the concentration of heavy metals and distribution in groundwater in regions of Shush Danial and Andimeshk aquifers in the southern part of Iran. Presently, groundwater is the only appropriate and widely used source of drinking water for rural and urban communities in this region. The region covers an area of 1,100 km2 between the Dez and Karkhe rivers, which lead to the Persian Gulf. For this study, the region was divided into four sub-regions A, B, C and D. Additionally, 168 groundwater samples were collected from 42 water wells during the earlier months of 2004. The flame atomic absorption spectrometry (AAS-Flame) was used to measure the concentration of heavy metals in water samples and the Surfer software was used for determination of the contour map of metal distribution. The results demonstrated that in all of the samples, Cd and Zn concentrations were below the EPA MCLG and EPA secondary standard, respectively. However, the Cu contents of 4.8 % of all samples were higher than EPA MCL. It is also indicated that the concentrations of metals were more pronounced at the southern part of the studied region than at the others. The analysis of fertilizers applied for agricultural activities at this region also indicated that a great majority of the above-mentioned heavy metals were discharged into the environment. Absence of confining layers, proximity to land surface, excess agricultural activities in the southern part and groundwater flow direction that is generally from the north to the southern parts in this area make the southern region of the Shush plain especially vulnerable to pollution by heavy metals than by other contaminants.  相似文献
3.
Contamination of heavy metals represents one of the most pressing threats to water and soil resources, as well as human health. Phytoremediation can be potentially used to remediate metal contaminated sites. In this study, concentrations of copper, zinc, iron, and magnesium accumulated by native plant species were determined in field conditions of Hame Kasi iron and copper mine in the central part of Iran in Hamadan province. The results showed that metal accumulation by plants differed among species and tissue bodies. Species grown in substrata with elevated metals contained significantly higher metals in plants. Metals accumulated by plants were mostly distributed in root tissues, suggesting that an exclusion strategy for metal tolerance exists widely amongst them. The mentioned species could accumulate relatively higher metal concentrations far above the toxic concentration in the plant shoots. With high translocation factor, metal concentration ratio of plant shoots to roots indicates internal detoxification metal tolerance mechanism; thus, they have potential for phytoextraction. The factors affecting metal accumulation by plant species including metal concentrations, pH, electrical conductivity, and nutrient status in substrata were measured. Mostly, concentrations of zinc and copper in both aboveground and underground tissues of the plants were significantly, positively related to their total in substrata, while iron, zinc, and copper were negatively correlated to soil phosphorus.  相似文献
4.
The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.  相似文献
5.
Environmental management of coastal regions in the Caspian Sea   总被引:13,自引:13,他引:0  
Considering rapid population growth and migration, higher accumulation of communities is noticed in coastal areas. This is especially true with the coastal areas of the Caspian Sea. In the present investigation coastal areas between Jouybar to Behshahr region is selected for their special geographical and ecological locations. Further, adverse impacts of human, agriculture and industrial activities was examined along side the above mentioned coasts. It should be pointed out that protected Miankaleh Wildlife zone which is an internationally recognized wetland, falls within area of study. In the present study strengths, weaknesses, opportunities and threats method is used for the evaluation of environmental management status. In this regard, internal and external factors gained 2.28 and 2.58 scores. This is indicative of the abundance of weaknesses over strengths and it also shows that opportunities are more than threats. Subsequently 27 strategies were developed and quantitative strategic planning matrix method was also used to score each strategies. The results of quantitative strategic planning matrix method analysis was programmed in strategic position and action evaluation matrix. The present situation falls within “competitive” classification. This is indicative of weakness in coordinating development and environmental strategic plans. The result of present investigation strongly emphasis on compilation of strategic environmental plans for the control of population, pollution emission and land use planning changes. The most important strategies include development of environmental regulations and better supervision on enforcement of laws.  相似文献
6.
Flood spreading is an inexpensive method for flood mitigation and artificial recharge of aquifers that results in a large budget return for relatively small investment.It is necessary to study some regional characteristics in order to determine the appropriate areas for artificial groundwater recharge by flood spreading in Meimeh Basin, Isfahan Province, Iran. Necessary regional characteristics to be studied are: slope, infiltration rate, sediment thickness, transmissivity, and water quality. In this research to identify suitable areas for artificial recharge several thematic layers were prepared, assigning each layer to one of the mentioned characteristics. The thematic layers were classified to several classes based on the existing criteria. All of the classes of the thematic layers were integrated and analyzed using a decision support system (DSS) in a geographical information system (GIS) environment. Finally suitability of the integrated classes for artificial recharge was identified in which the following classes were separated:(i) Very suitable, (ii) suitable, (iii) moderate suitability, and (iv) unsuitable.The validity of the generated model was verified by applying the model to a number of successful floodwater spreading stations throughout Iran. The verified model showed satisfactory results for all of the stations. The results for Meimeh Basin showed that about 70% of the Quaternary sediments in the studied area are suitable and moderately suitable for artificial recharge by flood spreading.  相似文献
7.
This study presents a landslide susceptibility assessment for the Caspian forest using frequency ratio and index of entropy models within geographical information system. First, the landslide locations were identified in the study area from interpretation of aerial photographs and multiple field surveys. 72 cases (70 %) out of 103 detected landslides were randomly selected for modeling, and the remaining 31 (30 %) cases were used for the model validation. The landslide-conditioning factors, including slope degree, slope aspect, altitude, lithology, rainfall, distance to faults, distance to streams, plan curvature, topographic wetness index, stream power index, sediment transport index, normalized difference vegetation index (NDVI), forest plant community, crown density, and timber volume, were extracted from the spatial database. Using these factors, landslide susceptibility and weights of each factor were analyzed by frequency ratio and index of entropy models. Results showed that the high and very high susceptibility classes cover nearly 50 % of the study area. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results revealed that the index of entropy model (AUC = 75.59 %) is slightly better in prediction than frequency ratio model (AUC = 72.68 %). The interpretation of the susceptibility map indicated that NDVI, altitude, and rainfall play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction and timber harvesting operations.  相似文献
8.
The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.  相似文献
9.
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.  相似文献
10.
The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.  相似文献
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