Rainfall Damage Prediction via Historical Index Matching
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Solution Overview
Problem
People with little knowledge about weather find it difficult to perceive the danger from numerical values like analyzed precipitation and short-term precipitation forecasts, leading to biased awareness and potential misinterpretation of disaster prevention information.
Innovation Solution
An information presentation method that computes an index value for current rainfall using spot and forecast values, identifies similar past rainfall events, and presents associated damage information to provide contextually relevant data for effective disaster management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If numerical values such as analyzed precipitation and short-term precipitation forecasts are used to present disaster prevention information, then the information can be provided in a standardized format, but people with little knowledge about weather find it difficult to perceive the danger
Solution Approach 1:
The patent transforms weather parameters from abstract numerical values (precipitation amounts, forecast values) into a concrete hazard level classification system with five tiers (very low, low, moderate, high, very high). This parameter transformation makes the information both standardized for processing and intuitively perceivable for decision-makers, directly resolving the contradiction between standardized format and danger perceptibility
Solution Approach 2:
The patent introduces hazard level classification as an intermediary layer between raw numerical weather data and human perception. This intermediary categorization system translates complex numerical forecasts into simplified, universally understood risk levels, enabling both standardized information handling and immediate comprehension of danger without requiring weather expertise
2Ease of operation
If disaster prevention information is represented by levels of hazard, then the information can be simplified for understanding, but bias arises in the awareness depending on the recipient of the information
Solution Approach 1:
The patent incorporates feedback mechanisms by comparing current hazard levels with historical data from the same period in previous years. The system provides feedback information showing whether current conditions exceed historical averages, which objectively validates the hazard level assessment and reduces recipient bias by anchoring perceptions in historical context rather than subjective interpretation
Solution Approach 2:
The patent performs preliminary classification of weather forecast data into standardized hazard levels before presentation to users. By pre-processing the data through objective classification criteria and historical comparison, the system eliminates the need for individual recipients to subjectively interpret raw data, ensuring consistent and unbiased hazard perception across all users
3Reliability
If organizations manually predict damage based on experience and knowledge, then the predictions can be contextually informed, but the process is time-consuming and may lack consistency
Solution Approach 1:
The patent creates a digital copy of historical damage data and weather conditions, storing them in a database for rapid retrieval. By copying and structuring historical information digitally, the system enables quick comparison with current conditions without requiring manual review of past experiences, thus maintaining contextual accuracy while dramatically improving prediction speed and consistency
Solution Approach 2:
The patent replaces the manual mechanical process of reviewing historical experience and knowledge with an automated information processing system. The system automatically retrieves historical data, compares it with current weather forecasts, and generates damage predictions, substituting human manual analysis with computational processes that are both faster and more consistent while preserving contextual information
Data Source
AI summary
A storage unit stores ranking information including an index value relating to past rainfall and a corresponding date-time and damage information including details of damage that occurred due to past rainfall and a corresponding date-time. A real-time data reception unit acquires a spot value and a forecast value of current precipitation. Also, a comparison operation unit computes an index value relating to current rainfall using the acquired spot value and forecast value. The comparison operation unit specifies, with reference to the ranking information, the date-time of an index value relating to past rainfall whose similarity to the computed index value relating to current rainfall is not less than a threshold value. An information output unit presents, with reference to the damage information, details of damage that occurred in a period whose difference from the specified date-time of the index value relating to past rainfall is in a predetermined range.