Water-induced disasters are harmful and can cause drastic damage. The response to these disasters is critical, and this task continues to gain global attention. A quick and accurate assessment of the damage is vital to mitigate the effects of the water-induced disasters. This paper begins with exploring the S-shaped curve that is widely observed with many types of damage resulting from the various water-induced disasters. Following that, we propose a conceptual model using the grade of a disaster as the crucial factor and recommend a general function in the form of a hyperbolic tangent function. The damage assessment of the flood and drought events that occurred in the Yangtze River Delta was conducted to explain the processes involved in the function configuration. Flood season precipitation and soil moisture were incorporated into the flood and drought models, respectively, to simulate the functions in agricultural areas hit by drought or flood events. Comparing the calculated result and field survey shows the effectiveness of the proposed damaged estimate function. This proposed methodology can be used to quickly assess the flood and drought damage, and it can also be extended to other types of disasters.

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With the increasing abundance of network teaching resources, the recommendation technology based on network is becoming more and more mature. There are differences in the effect of recommendation, which leads to great differences in the effect of recommendation algorithms for teaching resources. The existing teaching resource recommendation algorithm either takes insufficient consideration of the students’ personality characteristics, cannot well distinguish the students’ users through the students’ personality, and pushes the same teaching resources or considers the student user personality not sufficient and cannot well meet the individualized learning needs of students. Therefore, in view of the above problem, combining TDINA model by the user for the students to build cognitive diagnosis model, we put forward a model based on convolution (CUPMF) joint probability matrix decomposition method of teaching resources to recommend the method combined with the history of the students answer, cognitive ability, knowledge to master the situation, and forgetting effect factors. At the same time, CNN is used to deeply excavate the test question resources in the teaching resources, and the nonlinear transformation of the test question resources output by CNN is carried out to integrate them into the joint probability matrix decomposition model to predict students’ performance on the resources. Finally, the students’ knowledge mastery matrix obtained by TDINA model is combined to recommend corresponding teaching resources to students, so as to improve learning efficiency and help students improve their performance.

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