Oil Spill Prediction Model Using Numerical Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for predicting oil spill areas on sea surfaces from submarine oil pipeline leaks are labor-intensive, costly, and lack accuracy, failing to comprehensively consider influencing factors, which hampers effective emergency response and pollution control.
Innovation Solution
A method and system utilizing a machine learning model to predict oil spill areas by constructing a numerical model based on simulation data and training datasets, including influencing factors such as leakage time, aperture size, and water flow velocity, to determine the oil spill area on the sea surface, with a processor initializing the model and processing input data to estimate the spill area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data collection methods are used to identify influencing factors of submarine oil pipeline leaks, then comprehensive research on all factors can be achieved, but the process becomes labor-intensive, material-intensive, and financial-intensive with limited accuracy
Solution Approach 1:
The patent uses numerical simulation to create virtual copies of oil spill scenarios, replacing traditional physical data collection methods. The simulation model replicates the complex interactions between oil leakage, sea currents, wind, and waves, allowing comprehensive factor analysis without physical experiments. This copying approach achieves high accuracy in identifying influencing factors while eliminating the labor, material, and financial intensity of traditional methods.
Solution Approach 2:
The patent replaces traditional mechanical data collection systems (physical measurements, field surveys) with a computational numerical simulation system. The simulation uses mathematical models to calculate oil spill area based on input parameters such as leakage rate, current velocity, and wind speed, substituting physical measurement processes with computational algorithms that provide both comprehensive coverage and high accuracy.
2Loss of information
If numerical simulation studies are conducted to study the diffusion pattern of oil spills, then insights into transportation process and size of oil film area can be obtained, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the numerical simulation process into distinct functional modules: a simulation model for calculating oil spill diffusion, a data processing module for handling simulation results, and a prediction model for estimating oil film area. Each module handles specific aspects of the diffusion process, making the overall complex system manageable and efficient. This segmentation allows comprehensive information capture while organizing computational complexity into manageable components.
Solution Approach 2:
The patent introduces an intermediary data processing layer between the numerical simulation and final prediction results. This intermediary module processes raw simulation data, extracts relevant features, and transforms them into inputs for the prediction model. This intermediary approach manages the complexity by creating a structured bridge between the complex simulation system and the simpler prediction output, maintaining information completeness while reducing computational burden.
3Measurement precision
If a machine learning predictive model is constructed based on simulation data, then prediction accuracy of oil spill area can be improved, but the model initialization and training complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing simulation data and pre-training the machine learning model with comprehensive numerical simulation results before actual deployment. The simulation data is used to initialize the model's parameter space, and preliminary training establishes baseline performance. This preliminary action reduces the complexity of subsequent real-time prediction tasks, as the model is already adapted to the specific characteristics of oil spill diffusion patterns.
Solution Approach 2:
The patent employs parameter changes by adjusting the machine learning model's architecture and training parameters based on the specific characteristics of the simulation data. The model structure, learning rate, and other hyperparameters are optimized to match the data distribution and relationships found in the numerical simulations. This parameter tuning achieves high prediction accuracy while managing construction complexity through systematic optimization rather than trial-and-error.
4Reliability
If comprehensive factors are considered in predicting oil spill area, then prediction accuracy improves, but the amount of input data and processing requirements increase
Solution Approach 1:
The patent extracts only the most critical influencing factors from the comprehensive set of possible parameters. The numerical simulation identifies key factors such as leakage rate, current velocity, wind speed, and wave height that have the greatest impact on oil spill area. By extracting and focusing on these dominant factors, the model achieves high reliability in predictions while managing input data volume, as fewer but more significant parameters are used rather than all possible variables.
Data Source
AI summary
Embodiments of the present disclosure provide a method, system, and storage medium for predicting an area of oil spill on sea surface, the method for predicting the area of oil spill on sea surface includes: S1, obtaining a training dataset; S2, constructing an oil spill numerical model, and determining a predictive model by performing a predetermined processing on an initial predictive model based on simulation result data and the training dataset; S3, initializing the predictive model, determining a count of nodes of an input layer, an output layer, and a hidden layer; and S4, obtaining input data and inputting the input data into the predictive model in S2 to obtain an oil spill area on sea surface to be measured. The method, when used, has a small error and high accuracy, can save a lot of material and financial resources, and can be more widely used in real life.