الهام محمودزاده

 The accurate estimation of actual evapotranspiration (ETA), i.e. crop water requirements, is an important issue for irrigation water allocation at fields and improving water efficiency. In the present study, ETA for Sorghum crop was estimated using surface energy balance algorithm for land (SEBAL) and by employing Landsat8 satellite images; meteorological data and DEM map and then compared with the measured values of lysimeter.

 For carrying out the sensitivity analysis, the key parameters of SEBAL algorithm was altered at the range of ±10 , ±20 , ±30 , ±40  , ±50  and resultantly the variation of ETA corresponding to decrease and increase of these parameters was investigated. The changes of key parameters on the days of 206, 238 and 254, based on Julian days calendar, were evaluated at eight points with different vegetation densities. Albedo parameters have moderate to high sensitivity, so that the albedo parameter has medium and high sensitivity in the areas with ET> 10 and ET <10, respectively.

 The results of SEBAL showed the value of accuracy indices were acceptable at a significant level of 95%, while compared with those of lysimeter measurements. The results of the sensitivity analysis also showed the surface temperature and input shortwave radiation are more sensitive especially at the areas having low ETA values. The wind speed and

In general, in addition to high economic efficiency compared to other conventional methods, the SEBAL algorithm has a good efficiency in estimating ET. Also, performing sensitivity analysis and determining the key input parameters, while increasing the accuracy of measuring those parameters, can greatly improve the modeling results.

The accurate estimation of daily Evapotranspiration (ET) improves the efficiency of water resources management especially in areas where suffers from water scarcity. In the present study, ET was estimated using surface energy balance algorithm for land (SEBAL) and the experimental model of FAO-Penman-Monteith (FPM) and finally compared and verified with those calculated from pan evaporation method. Since many climatic factors affect the ET values, the sensitivity analysis of SEBAL inputs variables was finally cerried out to determine the key affecting parameters. In this regard, by SEBAL model and emplying the satellite data of Landsat 8 (OLI and TIRS sensors), the ET values were estimated on a daily scale for the time period 2018/07/25 to 2018/09/11. Results of SEBAL model showed that the values of SEE, RMSE and R2 indices were equal to 1.27, 0.76 and 0.77 mm /day and 0.91, 0.6 and 0.92 mm /day, while compared with those of FPM and pan evaporation methods, respectively.

In any educational system, the optimal output of educational approach is of particular importance. Therefore, considering the personality characters of individuals and providing educational services in accordance with their characteristics are effective factors in learning and educational efficiency improvement. Analyzing the data related to learner’s behavior in an educational environment and implicitly discovering the learner’s personality based on their behavior is a well-noticed study in recent years. Over the last few years, using learners’ information such as number of friends, the level of activities in educational forum, writing style of learner, study duration, the difficulty of solved problem, the difficulty of presented example by learners, number of clicks, number of signs in sentences, the time spent doing homework are items that has been used to personal characteristic identification. This study is aimed at using teammates’ changing / not changing data in order to learners’ personality identification. For this purpose the teammates’ changing/ not changing data extracted from a dynamic collaborative learning environment that allows leaners to change their teammate during the different sessions of learning, are used. The design and implementation of mentioned dynamic collaborative learning environment is based on game theory. Game theory provides mathematical models of conflict and collaboration between intelligent rational decision-makers.

In this paper, we collect teammates’ changing/not changing information of 119 randomly selected computer engineering students from a game theoretical dynamic collaborative learning environment. At the next step, using frequent pattern mining, as a tools of data mining, some aspects of the neo big 5 personality traits of learners are identified. In this survey, in order to evaluate the results, the extracted patterns from frequent pattern mining are compared with the neo big 5 personality questionnaire that have been filled by learners. In another part of research, using the Laplace’s rule of succession, valuable predictions were made about the probability of teammate’s changing of learners during the learning process.

In this study, using frequent pattern mining in learners’ behaviour, we identified some neo big 5 personality traits such as those in the first (neuroticism), second (extraversion), and third (openness to experience) dimensions, with an acceptable support value. The results of this part of research can be used in any adaptive learning environment that adapt learning process for individual learners with different personality. At the next step of our study, we predicted the probability of the teammate changing in the sessions after. At this step, we had a prediction accuracy of up to 67.44%. Using the results of this part, teammate suggestion can be made to learner based on likelihood of their teammates’ changing. That is, higher teammate changing probability, more appropriate teammate suggestion to learner.

The results of the present study can be used in any adaptive system that requires predicting group change behaviour or identifying personality dimensions based on behaviour.