sajjad ebrahimzadeh; javad bazrafshan
Abstract
Drought can affects by reduced water resources, agricultural productivity, change in vegetation cover, and accelerate the desertification of areas. In order to drought monitoring, we need to quantify drought effects by using drought indices. These indices based on type of available data are divided ...
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Drought can affects by reduced water resources, agricultural productivity, change in vegetation cover, and accelerate the desertification of areas. In order to drought monitoring, we need to quantify drought effects by using drought indices. These indices based on type of available data are divided into two general categories of ground- and satellite- based indices. The aim of this study was to compare the capability of detection and classification of vegetation changes occurred due to the drought, between one ground-based drought index (Standardized Precipitation Index (SPI)) and four satellite drought indices derived from AVHRR-NOAA (normalized difference vegetation index (NDVI), temperature condition index (TCI), ratio vegetation index (RVI), standardized vegetation index (SVI) in the Kermanshah province. To do this, the change vector (CV) analysis was used as one of the important change detection algorithms. In this method, the change occurred in vegetation has been shown by two components, change magnitude and change direction. The results of implementation of the CVA on the maps of drought indices during the growing season (March to August) in selected years (two normal years, one wet year, and one drought year) showed the best response to the drought in the study years (except the wet year 1992), obtained by SVI. The lowest similarity was obtained between the SPI and TCI, for wet and normal years. Finally, the study suggests mostly the satellite indices based on the vegetation conditions, rather than the temperature indices, for assessing the effect of drought on vegetation cover.
A. Hezarjaribi; F. Nosrati Karizak; K. Abdollahnezhad
Abstract
Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose ...
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Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose of this research was to investigate CEC estimating using easily accessible parameters with Artificial Neural Network. In this study, the easily accessible parameters were sand, silt and clay contents, bulk density, particle density, organic matter (%OM), calcium carbonate equivalent (%CCE), pH, geometric mean diameter (dg) and geometric standard deviation of particle size (σg) in 69 points from a 1×2 km sampling grid. The results showed that Artificial Neural Network is a precise method to predict CEC that it can predict 82% of CEC variation. The most important influential factor on CEC was soil texture. The sensitivity analysis of the model developed by using of Artificial Neural Network represented that clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size, OM% and total porosity were the most sensitive parameters, respectively. The model with clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size as inputs data was selected as the base model to predict CEC at studied area.
Kh. Ghorbani
Abstract
So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation ...
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So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation spatial variation, this study was conducted in Gilan province which precipitation is distributed non-uniform due to different environmental conditions. The results of geographically weighting regression method were compared with another interpolation methods including global polynomial, local polynomial, inverse distance weighting (IDW), spiline, kriging and co-kriging and . In this study, average of 20 years annually precipitation data of 185 meteorological observations over Gilan Province and its neighboring stations was used for modeling of spatial distribution variations of mean annual precipitation by using other variables like elevation and position of points to the sea level. Cross validation technique was used to assessment accuracy of each interpolation methods. The result showed that geographically weighting regression method had minimum error with RMSE=147 and had significant difference with the kriging method which was in the second rank with RMSE=187. Finally the best method for mapping isohyets in Gilan province is geographically weighting regression method.
Kh. Ghorbani; A. Khalili; S.K. Alavinezhad; Gh. Nakhaezadeh
Abstract
Abstract
Precipitation is an important variable which is used in definition of drought. Based on precipitation amounts, some indices are devised for drought monitoring including, the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP). Each of these Drought indices ...
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Abstract
Precipitation is an important variable which is used in definition of drought. Based on precipitation amounts, some indices are devised for drought monitoring including, the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP). Each of these Drought indices are classified into some classes so that each class descripts a given severity of drought. Investigation of simultaneously occurrence situation for two drought indices can be an appropriate measure to evaluate the agreement of indices. Association Rules in DATA MINING is used to find rules and patterns in database. In this paper, two drought indices, SPI and SIAP, were computed at 11 meteorological stations belong to Ministry of Energy in Kermanshah province. Then, based on Association Rules, simultaneously occurrence situation of drought severity classes for both indices were determined in seasonally, half yearly and yearly time scales. Results showed that there is not any good agreement between most of drought category from these indices (less than 50 percent) and Shows different behavior of drought.
Keywords: Data Mining, Drought, Association Rules, Kermanshah