فهرست مطالب javad javdan

Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful launch of Landsat 8 with two thermal bands has provided a good opportunity for retrieving land surface temperature usingthermal remote sensing technology. Many studies had been performedwith the aim of retrieving land surface temperature, but available evidencesshow a significant calibration uncertainty inThermal Infrared Sensor (TIRS) of Landsat 8 band 11 and thus development of new studies based on onethermal band seems to be necessary. However, calibration documents issued by the United States Geological Survey (USGS) indicated uncertainty ofdata received from Band 11 Thermal Infrared Sensor (TIRS) of Landsat 8 and suggested using Band 10 data as a single spectral band for LST estimation.

In this study, mono-window algorithm with its three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature)has been developedunderan automated algorithmin MATLABand was used for Landsat 8 data.Thermal band 10 was used to estimate brightness temperature. Bands 4 and 5 were also used to calculate the NDVI. Retrieval of LST from Landsat 8 TIRS data is performed based on the premise that brightness temperature (Ti)can be computed for any pixel of Band 10 using the mono-window algorithm.Since the observed thermal radiance for Band 10 of Landsat 8 TIRS is stored and transferredasa digital number (DNs) with 16 digits between 0 and 65,535, it is possible toconvertthe DN value into thermal radiance and then convert radiance into brightness temperature.Ground emissivity is calculatedusing land cover patterns received from other bands of Landsat 8, and the other two parameters are estimated based on the local meteorologicaldata. Usually, obtaining an accurate estimate of ground emissivity is very difficult, and the atmospheric water vapor content is considered to be a sensitive parameter in traditional LST retrieval methods.

The algorithm has been successfully applied to Tabriz city in north west of Iran with the aim of analyzing spatial distribution of LST. After running the algorithm on the satellite images of the study area on July 18,2016, a lower land surface temperature was observed in green spaces with 1.2°C accuracy as compared to urban areas and wastelands. The lowest temperature in the study area was 20°C and the highest temperature was 53°C and mean temperature was 38.78°C.Results indicate that the algorithm candiscover natural urban heat islands accurately. Moreover, spatial distribution of LST in the region is quite well matched with the land covers. Successful application of the algorithm proves the efficiency of improved mono-window algorithm as a method used for retrieving LST from Landsat 8 data.

Compared to common methods,the proposed algorithm estimates land surface temperature with minimum requirement for user intervention, least possible time and an acceptable accuracy. Itgives researches an opportunity to easily compute LST and apply it in other studies, and thus it is a significant tool.

 Groundwater, its quantitative/qualitative variables, and variability directly affect human life, and thus has always been one of the major topics in scientific and academic research. Due to geographical, climatic and hydrological conditions, and specific patterns of surface water and subsurface water resources exploitation, this country has always faced water scarcity. As a result of global and regional changes in temporal and spatial patterns of rainfall, this has intensified in recent years. Therefore, exploitation of groundwater resources has been considered as an option for supplying agricultural, industrial and drinking water. However, excessive exploitation of these resources will result in their destruction. In recent years, excessive removal of groundwater and reduction of groundwater levels have resulted in some problems like subsidence in some plains. This makes it necessary to study the quantitative and qualitative changes of these resources more clearly. Due to the complex nature of aquifers’ hydrogeological systems, accurate investigation of these resources seems costly and even impossible. Thus in order to achieve a better understanding, it is necessary to use different methods for estimation and evaluation of such variables.

Most environmental features are completely continuous in nature, which makes it impossible to measure these features in every part of these environments. Thus, we can generalize measured samples to other areas lacking accurate measurements, and in this way estimate these variables in unmeasured areas. This is also true about quantitative and qualitative variables of groundwater, i.e. by collecting samples from some sections, we can measure different characteristics in these samples. This surface modelling -or in other words, generalization of points to surface- can be achieved with mathematical and statistical relationships and rules. Due to the spatial structure of the measured specimens, geo statistics is used in this regard. In recent years, artificial intelligence models, inspired by the natural nervous system and simulating its function, have yielded a very satisfactory result in groundwater estimation and studies. In order to evaluate the accuracy of geo statistical methods and artificial neural networks, the present study takes advantage of statistics and measurements collected from groundwater level of 46 wells in Shabestar-Sufiyan plain in 2014. Kriging method (geo statistics) and multilayer perceptron neural network method (MLP) were used along with error propagation pattern (BP) to estimate unmeasured features in the study area. MATLAB 2016B was used to perform the neural network modeling and ARCGIS10.5 was used to perform Kriging method and prepare the final maps. In both neural network and kriging models, geographical coordinates of observed wells was used as input and measured water table was introduced as the study goal. Primary data reduces the accuracy of models. Thus, data was normalized before being introduced to the neural network model. After the initial analysis of data dispersion and normalization, logarithmic transfer function was used due to the relative improvement of data in Kriging estimator model.  

Results indicate that at the training and testing stage (with Sigmoid tangent activation function (Tansig) and 9 neurons in the middle layer), neural network method (MLP) with a high correlation coefficient (0.96) and root mean square error of 13.18 is more accurate than Kriging method with J-shaped Variogram model, a correlation coefficient of 0.90 and root mean square error of 20.10. Due to realistic results provided by neural network method, it is considered to be a more efficient method in estimation of water table in Shabestar-Sufiyan Plain. This is also consistent with earlier hydrogeological studies (regarding aquifers) performed on the ability and flexibility of Artificial Intelligence models.

Results obtained from the current research, and previous studies conducted in this field indicate that most artificial intelligence computing models are capable of evaluating and estimating continuous environmental variables. On the other hand, understanding groundwater resources’ conditions is considered to be crucial. Thus, new methods, such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference methods (ANFIS) and fuzzy inference systems (FIS), which provide greater accuracy can help decision makers and researchers in maintenance and improvement of the groundwater status.