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Objective: To reduce the danger for firefighters and ensure the safety of firefighters as much as possible, based on the back propagation neural network (BPNN) the fire sensor multi-sensor information fusion detection system is investigated. Method: According to previous studies, the information sources and information processing methods for the design of this study are first explained. Then, the basic structure and flowchart of the research object in this study are designed. Based on the structure diagram and flowchart, the BPNN is selected to fuse the feature layers in this study, and the fuzzy control is selected to fuse the decision layers in this study. The multi-sensor information fusion detection system collects information for the sensors first, processes the collected information, and sends it to the processor of the robot. The processor analyzes and processes the received signal, and transmits the obtained information to the control terminal through the wireless communication system. Results: Through the tests in this study, it is found that when the number of hidden layer nodes of the BPNN is 7, the optimal training result is obtained. On this basis, the test of BPNN in this study is performed. The test results show that after 127 iterations, the error of the BPNN reaches the lowest target value, indicating that the BPNN achieves an excellent level of accuracy. The trained BPNN has a running time of 0.0276 s and a mean square error of 0.0013. The smaller the mean square error value is, the higher the accuracy of the BPNN is, which shows that the BPNN meets the high precision requirements of this study. Conclusion: The research on the multi-sensor information fusion detection system of fire robots in this study can provide theoretical support for the research on forest fire detection in China. Since the proposed BPNN-based robot is applied to the inspection and processing of forest remaining fire, the results are applicable to the forests of various countries, with a wide range of applications. [ABSTRACT FROM AUTHOR]

Many coastal cities reclaim land from the sea to meet the rapidly growing demand for land caused by population growth and economic development. Settlement in reclaimed land may delay construction and even damage infrastructures, so accurately predicting the settlement over reclaimed areas is important. However, the limited settlement observation and ambiguous final settlement estimation affect accurate settlement prediction in traditional methods. This study proposes a new strategy to solve these problems by using the Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) method and takes the Xiamen Xiang'an International Airport, built on reclaimed land, as an example. The MT-InSAR is adopted to process the Sentinel-1 images to obtain the settlement history of the study area. The results show that settlement mainly occurs in the reclaimed areas, with the maximum average settlement rate exceeding 40 mm/y. We use the statistical properties of curve fitting to choose the best curve model from several candidate curve models to predict the settlement time series. The Asaoka method is used to identify the critical state between settlement and stability. We predict the consolidation time of the whole study area and reveal that the deformation rate is positively correlated with the consolidation time. The maximum remaining settlement time is over ten years since 24 December 2019. Therefore, manual compaction operations can be carried out to speed up settlement in the areas that need a long time to consolidate. The proposed method can be used to predict the settlement of similar reclaimed areas, and the predicted results can provide a reference for engineering construction. [ABSTRACT FROM AUTHOR]