The present disclosure relates generally to methods, systems and computer program storage devices for generating a response to flooding. In one specific example, the present disclosure relates to methods, systems and computer program storage devices for generating one or more operational responses to flooding.

Moisture content information in soil at a specified site is acquired. Moisture content information a ground surface within a given range that includes the specified site is acquired. An amount of runoff or storage volume of water on the ground surface or in a ground at a freely-selected site in the given range is calculated based on the moisture content information in soil and the moisture content information at the ground. A degree of risk of flood disaster at the freely-selected site within the given range is calculated based on the amount of runoff or storage volume of water on the ground surface. A point where there is the risk of flood disaster within the given range is determined and highlighted based on the degree of risk of flood disaster.

According to an exemplary embodiment of the present disclosure, a method of predicting the amount of precipitation based on deep learning performed by a computing device is disclosed. The method may include: receiving meteorological data measured in a weather observation system; and predicting the amount of precipitation of a region of interest based on the meteorological data by using a deep learning model. In this case, the deep learning model may be pre-trained based on a combination of a first loss function for an error calculation between a prediction value and Ground Truth (GT), and a second loss function for an error calculation different from the first loss function.

According to an exemplary embodiment of the present disclosure, a method of predicting the amount of precipitation based on deep learning performed by a computing device is disclosed. The method may include: receiving meteorological data measured in a weather observation system; and predicting the amount of precipitation of a region of interest based on the meteorological data by using a deep learning model. In this case, the deep learning model may be pre-trained based on a combination of a first loss function for an error calculation between a prediction value and Ground Truth (GT), and a second loss function for an error calculation different from the first loss function.

A device for detecting rain includes a camera and a lighting source for emitting visible light onto a window. The camera and the lighting source are configured and arranged in such a way that the camera can detect a signal of the visible light which the lighting source emits onto the window. The signal which is detected by the camera correlates with visible light of the lighting source, which visible light is reflected or scattered at the inner face of the window or outer face of the window and/or at the raindrop. The visible light passes through a shutter device which causes the light to be blocked or highly attenuated in a predefined direction perpendicular to the illumination direction of structures of the shutter device. In contrast, the light in the direction perpendicular to the predefined direction and to the illumination direction can propagate virtually unimpeded through the shutter device.

A device for detecting rain includes a camera and a lighting source for emitting visible light onto a window. The camera and the lighting source are configured and arranged in such a way that the camera can detect a signal of the visible light which the lighting source emits onto the window. The signal which is detected by the camera correlates with visible light of the lighting source, which visible light is reflected or scattered at the inner face of the window or outer face of the window and/or at the raindrop. The visible light passes through a shutter device which causes the light to be blocked or highly attenuated in a predefined direction perpendicular to the illumination direction of structures of the shutter device. In contrast, the light in the direction perpendicular to the predefined direction and to the illumination direction can propagate virtually unimpeded through the shutter device.

The present application relates to a self-correcting multi-model numerical rainfall ensemble forecasting method, comprising the following steps: step 1, selecting various numerical weather prediction models; step 2, simulating forecasting and outputting rainfall data for every T hours; step 3, evaluating rainfall forecast results; step 4, determining a forecast weight coefficient of each model; and step 5, releasing a forecast result. The present application can more objectively evaluate the rainfall forecast results of all numerical weather prediction models on the basis of existing multi-model ensemble rainfall forecast, so that the final ensemble rainfall forecast result does not depend too much on man-made decisions and thus the released rainfall forecast result is more objective.

The present application relates to a self-correcting multi-model numerical rainfall ensemble forecasting method, comprising the following steps: step 1, selecting various numerical weather prediction models; step 2, simulating forecasting and outputting rainfall data for every T hours; step 3, evaluating rainfall forecast results; step 4, determining a forecast weight coefficient of each model; and step 5, releasing a forecast result. The present application can more objectively evaluate the rainfall forecast results of all numerical weather prediction models on the basis of existing multi-model ensemble rainfall forecast, so that the final ensemble rainfall forecast result does not depend too much on man-made decisions and thus the released rainfall forecast result is more objective.

An acoustic disdrometer is provided for measuring precipitation. The acoustic disdrometer has an acoustic transducer positioned within an acoustic chamber defined by an acoustic shell. Precipitation impacting the acoustic shell generates sound waves that are collected by the acoustic transducer for processing.

An acoustic disdrometer is provided for measuring precipitation. The acoustic disdrometer has an acoustic transducer positioned within an acoustic chamber defined by an acoustic shell. Precipitation impacting the acoustic shell generates sound waves that are collected by the acoustic transducer for processing.