Smart City Waste Management System with Location-Based Features

Abstract

This article presents the use of automated machine learning technique for solving a socio-economic problem of waste management specifically focused on India. We design a machine learning model to already existing system of smart waste management to tackle the drawbacks of the system. Usage of sensors in waste level indication poses practical problems of real life where the sensors need to be maintained regularly, if not done properly may result in less accurate depications. In order to improve the efficiency and accuracy we deployed data-driven models to predict and forecast future data based on historical data. The usage of the machine learning models helped in boosting the manually engineered model to (86:8%,99:1%) from (47:9%,98.2%) of classification accuracy and recall respectively. We deployed a Random Forest Classifier on a set of features 4. based on the filling level at different given time spans in our model. Finally compared to existing manually engineered model, our upgraded model enhances the quality of forecasts for emptying time of recycling containers.

Specification

Description:Description

1. Title: Smart City Waste Management System with Location-Based Features
2. Field of Invention: automated machine learning (AutoML) techniques and data-driven models
3. Abstract:This article presents the use of automated machine learning technique for solving a socio-economic problem of waste management specifically focused on India. We design a machine learning model to already existing system of smart waste management to tackle the drawbacks of the system. Usage of sensors in waste level indication poses practical problems of real life where the sensors need to be maintained regularly, if not done properly may result in less accurate depications. In order to improve the efficiency and accuracy we deployed data-driven models to predict and forecast future data based on historical data. The usage of the machine learning models helped in boosting the manually engineered model to (86:8%,99:1%) from (47:9%,98.2%) of classification accuracy and recall respectively. We deployed a Random Forest Classifier on a set of features
4. based on the filling level at different given time spans in our model. Finally compared to existing manually engineered model, our upgraded model enhances the quality of forecasts for emptying time of recycling containers.

5. Background: The Smart City Waste Management System with Location-Based Features addresses the growing challenge of efficient waste management in urban areas, particularly focusing on India. Traditional systems relying on sensors for waste level detection face issues such as maintenance and accuracy. To improve the efficiency of waste management, this system leverages machine learning to predict waste levels and optimize the timing for emptying bins.The integration of machine learning and automation ensures that the waste collection process is more timely and efficient, reducing operational costs and enhancing environmental sustainability

Objective of Invention: The primary objective of this invention is to develop an optimized Smart City Waste Management System that enhances the efficiency and accuracy of waste collection by leveraging data-driven models and machine learning techniques. The system aims to address limitations in existing sensor-based waste monitoring systems by accurately predicting waste bin fill levels and emptying times. This is achieved through the integration of automated machine learning models that forecast future data based on historical patterns, improving overall waste management operations, reducing costs, and promoting environmental sustainability.

6. Summary of the invention:
The invention described in the document proposes a "Smart City Waste Management System" enhanced with machine learning techniques for greater accuracy and efficiency. It addresses the limitations of traditional sensor-based systems, which require regular maintenance and may provide inaccurate readings. The system incorporates automated machine learning (AutoML) and data-driven models to predict waste levels in containers and improve the timing for emptying recycling bins.
The core innovation lies in the use of an ensemble model, which integrates multiple deep learning algorithms, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. This ensemble approach leverages the strengths of each individual model, enhancing overall detection accuracy and robustness. By combining these diverse methodologies, the system can more effectively analyze and classify textual content, distinguishing between genuine news and fabricated stories with higher precision.

A key innovation lies in the use of the Random Forest Classifier to analyze waste fill levels, improving the forecast accuracy from 47.9% to 86.8%. This is a significant improvement over manually engineered models. The invention also involves a feature selection method called Recursive Feature Elimination (RFE) to identify optimal features for classification while maintaining simplicity in operation. The proposed system enhances efficiency by optimizing waste collection routes, saving operational costs, and reducing environmental impacts.

The invention's contribution is primarily focused on improving waste management logistics and emptying detection, helping maintain cleanliness in smart cities through a sustainable and data-driven approach.
7. Information about drawing: None
a. Best Methods for Coming out the Invention: To bring an invention to life, it's essential to combine creativity, research, and strategic implementation. Start by thoroughly understanding the problem the invention aims to solve, analyzing existing solutions, and identifying gaps. After conceptualizing the invention, conduct in-depth research and feasibility studies to assess its practicality. Prototyping is crucial for testing the invention's functionality and performance. It's important to continuously gather feedback, refine the design, and address any potential issues. Protecting the invention through patents or intellectual property rights is a key step, ensuring ownership and market advantage. Lastly, effective promotion and collaboration with industry experts or investors can facilitate successful commercialization and broader adoption.Lastly, involving end-users and stakeholders, such as city authorities and citizens, can ensure that the system is both practical and sustainable in the long term. This combined approach leads to innovative solutions that can effectively transform waste management practices.
b. PYTHON LIBRARIES:

Here are some Python libraries you can use for a Smart City Waste Management system with machine learning and location-based features, as described in the document:

Pandas - For data manipulation and analysis, particularly useful when working with historical waste management data.
- `pip install pandas`

NumPy- For numerical computing and handling large arrays of data, essential for preprocessing data and model inputs.
- `pip install numpy`

SciKit-Learn - For implementing machine learning models like Random Forest Classifier,
Web browser:It provides interface for displaying web-based documents to users.
PyTorch:Anotherdeeplearninglibrarythatoffersflexibilityanddynamic computation graphs, also suitable for building various neural network architectures.
SpaCy: Anotherlibrary foradvanced natural languageprocessing, useful fornamed entity recognition, dependency parsing, and more.
8. Industrial Applicability: The "Smart City Waste Management System with Location-Based Features" invention has significant industrial applications across various sectors. We design a machine learning model to already existing system of smart waste management to tackle the drawbacks of the system. Usage of sensors in waste level indication poses practical problems of real life where the sensors need to be maintained regularly, if not done properly may result in less accurate depications. In order to improve the efficiency and accuracy we deployed data-driven models to predict and forecast future data based on historical data.
, Claims:CLAIMS
What is claimed is:
The"Smart City Waste Management System with Location-Based Features" project presents a comprehensive solution to the pervasive issue of misinformation in digital media. The following claims encapsulate the innovative contributions and potential impact of this endeavor:

Machine Learning Enhances Waste Management: The proposed system improves the accuracy of waste management by deploying machine learning models, which boost classification accuracy and recall significantly compared to manually engineered models
Automated Detection of Emptying Events: The system focuses on accurately detecting when waste containers are emptied, which is crucial for predicting future waste levels and optimizing collection routes
Random Forest Classifier for Prediction: A Random Forest Classifier was used to predict waste bin fill levels, improving the efficiency of the system compared to traditional models
Optimization through Feature Selection: The proposed system uses Recursive Feature Elimination (RFE) to balance between using more features for better accuracy and fewer features for operational simplicity
Integration of IoT and GIS: The system utilizes Internet of Things (IoT) devices and Geographic Information Systems (GIS) to enhance real-time waste bin monitoring and optimize collection routes.
Sustainability Goals: The smart waste management system aims to promote environmental sustainability by improving waste segregation, recycling programs, and reducing environmental impacts through technology
Improvement in Prediction Accuracy: The best-performing solution improved waste bin emptying time predictions by 14.2%, demonstrating the effectiveness of data-driven engineering for smart waste management systems

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