Iran is one of the countries exposed to many natural disasters, of which the flood phenomenon is one of the most serious. According to official statistics, more than 50% of casualties due to natural disasters in Iran are due to floods. Accurate and timely information is needed to plan any flood management measures. Currently, many researchers have examined the methods of obtaining water area information. Among these methods, land use classification and change analysis are the most important applications of remote sensing techniques. One of the most widely practical classifications is the separation of water class from other classes. Also, Landsat images are one of the most common data sources in this field. In this regard researches have been done in the field of identifying water areas, due to its high importance in flood crisis management with the advancement of satellite technology in remote sensing. In this study, an automated method has been developed to prepare flood change maps and use different capabilities of different water and humidity indicators to provide automated training data. This can reduce the challenge of operator interferences. Besides, the efficiency of the high level of automation in the extraction of flooded areas in accelerating and facilitating the management of this crisis is indisputable. Therefore, when manual training data is not available, applying a fully automated approach will be helpful. The integration of indicators can also be effective in improving the accuracy of existing water indicators. Floods are one of the most important natural disasters in the world that occurred due to different factors such as human encroachment on rivers and vegetation damages. The purpose of this study is to develop an automated method for preparing training data for supervised classification of images and using the capabilities of integrated indicators to identify flooded areas. Therefore, several approaches for flood in Golestan province, 2019, were implemented by Landsat-8 data and the results of each were examined. The Kmeans clustering algorithm, Otsu, Multi and Adaptive thresholds were used to generate automated training data based on different water indices (NDWI, AWEI and MNDWI; Then the ANN, SVM, ML, MD and BE classifications) was implemented to monitor flood changes. The results compared with the manual classification method indicated that the proposed approach, in addition to providing a high-level of automation in monitoring changes, also had high efficiency and accuracy. In another development approach, different water and moisture indices were combined with the aim of improving the production process of automated training data. Among the 85 tests performed, the combined approaches had the highest accuracy. Due to the nature of different water indicators, their selection and appropriate combination, in addition to reducing the noise in the water indicator image was also effective in increasing the ability to identify impure water areas. Finally, a comprehensive deductive analysis of automated methods and hybrid indicators was carried out to help to manage flood crisis, facilitate and accelerate the process. It can be said that with the advancement of technology and the emergence of various aerial / satellite images, many opportunities and possibilities have been created for effective and sustainable management. One of the best ways to extract information from digital images is to classify the image. One of the challenges in image classification is to reduce the interference of users using algorithms that have high level of automation. This issue has high importance due to the sensitivity and sudden occurrence of the flood crisis. Therefore, to promote this challenge, in this research, an automated and efficient approach for supervised classifications with emphasis on automatic production of training data to prepare a map of flood changes is presented. In total, 85 different studies were performed to obtain flood change maps, using hybrid and non-hybrid automated methods to prepare automated training data for supervised classifications. It is considered that the obtained results had acceptable accuracy. For example, in all approaches studied, ANN classification offered better accuracy, which could be seen from the diagrams provided. Also, due to the lack of significant difference in the accuracy of the maps, obtained from this classification with different indicators and thresholds, it can be said that this classification is also highly robust. The results indicate that ANN classification in combined approaches with Otsu and Adaptive thresholds has the highest accuracy and maximum robustness. In comparison with methods related to execution speed, ML and MD classifications also provided good performance. However, the difference in the accuracy of these classifications compared to the ANN classification is not significant in some cases and will be possible to replace due to higher execution speed. Finally, by analysis of the results, it can be stated that the automated approaches proposed in this research can be replaced by traditional classification methods.