Intellectual Property India Publishes Patent Application for Community-Centric Landslide Risk Assessment Mobile Application"

MUMBAI, India, July 31 -- Intellectual Property India has published a patent application (202411095475 A) filed by Lovely Professional University on December 04, 2024, for Community-Centric Landslide Risk Assessment Mobile Application".

Inventor includes Rohan Kumar.

The application for the patent was published on July 24, 2026, under issue no. 30/2026.

Abstract: The following specification particularly describes the invention and the manner in which it is to be performed. FIELD OF THE INVENTION The present invention generally relates to the field of disaster risk management and mitigation, and more particularly to a community-centric landslide risk assessment mobile application designed to provide user-friendly, personalized risk evaluations and enhance awareness and preparedness in landslide-prone areas. BACKGROUND OF THE INVENTION Landslides pose a significant threat to communities residing in mountainous regions, exacerbated by changing climatic patterns and increased infrastructure development. Climate change has led to altered weather patterns, resulting in heightened precipitation and extreme weather events, which increase the frequency and severity of landslides. Concurrently, infrastructure projects in these regions often destabilize slopes and disrupt natural drainage, further elevating landslide risks. Despite the existence of guidelines such as those from the Bureau of Indian Standards (BIS) for landslide hazard zoning, local communities frequently lack access to simplified, actionable risk information, leaving them vulnerable and unprepared. Current solutions for landslide risk assessment are often broad and regional, lacking the specificity required for individual properties. These assessments typically involve complex technical data that is not easily accessible or understandable by the general public, particularly those in landslide-prone areas. Traditional methods are also costly and require expert knowledge, making them impractical for widespread community use. Furthermore, these methods do not usually incorporate real-time data, resulting in outdated risk assessments that fail to reflect current environmental conditions. The limitations of existing solutions are evident in their complexity, lack of personalization, and limited accessibility. The technical nature of BIS guidelines and similar methodologies restricts their utility for non-experts, while the absence of personalized assessments means individuals cannot accurately gauge the risk to their specific properties. Additionally, the dissemination of critical risk information to vulnerable communities is often inadequate, leaving a significant gap in awareness and preparedness. The resource-intensive nature of traditional methods further compounds these issues, as they require substantial financial and expert resources that may not be available to local communities or authorities. Given these deficiencies, there is a clear need for a more accessible, user-friendly, and personalized approach to landslide risk assessment. The "Community-Centric Landslide Risk Assessment Mobile Application" addresses the above challenges by providing a tool that simplifies complex data, offers personalized risk assessments, integrates real-time data, and would be cost-effective. Such an invention would empower communities to better understand and manage their landslide risk, enhancing their safety and preparedness in the face of increasing landslide hazards. OBJECTS OF THE INVENTION The object of the invention is to provide a mobile application that integrates the Bureau of Indian Standards (BIS) guidelines for landslide hazard zoning, translating complex technical data into a user-friendly format accessible to non-experts, thereby enhancing community accessibility to landslide risk information. Another object of the invention is to enable personalized risk assessments by allowing users to input location-specific geo-environmental data, such as slope angle, soil conditions, and local rainfall, resulting in more accurate risk profiles for individual properties. Yet another object of the invention is to facilitate real-time risk mapping by integrating Geographic Information System (GIS) and Global Positioning System (GPS) technologies, allowing non-experts to produce real-time landslide risk maps and enhancing awareness and preparedness among local communities. A further object of the invention is to incorporate BIS codes for landslide hazard zoning, ensuring that the app's risk assessments are based on established standards and methodologies, thereby providing reliable and standardized risk evaluations. An additional object of the invention is to offer advanced machine learning algorithms for landslide risk prediction, providing users with multiple assessment options and enhancing the understanding of risk zones through dynamic and adaptive risk assessments. Another object of the invention is to feature an intuitive interface with interactive maps and data input options, making the app accessible and easy to use for individuals without technical expertise, thereby improving usability and engagement. A further object of the invention is to serve as a cost-effective tool for identifying landslide risk, enabling users to conduct their own assessments and share data with local authorities, thus contributing to community safety efforts and reducing the need for costly traditional assessments. Yet another object of the invention is to provide personalized recommendations for mitigating assessed risks, such as bioremediation methods, drainage improvements, and terrace design, empowering users to take proactive measures in risk management. Finally, an object of the invention is to empower local communities residing in hilly areas by equipping them with the necessary tools and information to make informed decisions regarding landslide hazards, thereby strengthening their ability to tackle such challenges effectively. SUMMARY OF THE INVENTION The present invention is described in the following sections by various embodiments. However, it should be understood that the invention can be implemented in various forms and is not limited to the specific embodiment provided herein. Embodiments of the present invention provide a community-centric landslide risk assessment mobile application that comprises a user interface (UI) configured to allow users to input location-specific geo-environmental data, including slope angle, soil conditions, and local rainfall. The application includes a data input module integrated with the UI for collecting and transmitting the geo-environmental data. A Geographic Information System (GIS) component is utilized for base map integration and visualization of user location and surrounding terrain, while a Global Positioning System (GPS) component provides real-time location data. The application employs landslide hazard/risk simulation algorithms configured to process the user-inputted data and apply Bureau of Indian Standards (BIS) guidelines IS 14496 (Part 1 and 2): 1998 to generate risk maps. Additionally, machine learning algorithms are integrated with the landslide hazard/risk simulation algorithms for analyzing patterns in the data and improving risk prediction accuracy over time. A recommendation engine is configured to provide personalized recommendations based on the assessed risk, including bioremediation methods, drainage improvements, and terrace designs. The GIS and GPS components are configured to capture real-time terrain information and integrate base maps for accurate location data. The landslide hazard/risk simulation algorithms ensure compliance with established standards through BIS code integration, and the machine learning algorithms enhance the app's predictive capabilities by learning from historical data. The advantages of this embodiment include the ability to provide precise, location-specific risk assessments by integrating real-time data capture through GIS and GPS technologies. The use of BIS guidelines ensures that the risk assessments are standardized and reliable, while the incorporation of machine learning algorithms allows for continuous improvement in prediction accuracy. This combination of features empowers users to make informed decisions about landslide risk mitigation, offering a comprehensive and user-friendly tool for communities in mountainous regions. In accordance with an embodiment of the present invention, the user interface (UI) further comprises interactive maps and data input options. This feature enhances the user experience by providing a visual representation of risk zones, making it easier for users to understand the potential hazards in their area. Additionally, the interactive maps allow users to navigate the app seamlessly, facilitating efficient data entry and risk assessment interpretation. In accordance with an embodiment of the present invention, the data input module is configured to provide fields and options for users to enter data related to slope, soil conditions, and rainfall. This module plays a crucial role in ensuring that the app receives accurate and relevant data for risk assessment, contributing to the precision of the generated risk maps. In accordance with an embodiment of the present invention, the GIS component is configured to provide spatial data crucial for generating accurate risk maps. By integrating base maps and visualizing user locations, the GIS component enables the app to offer detailed and location-specific risk assessments, which are essential for effective landslide risk management. In accordance with an embodiment of the present invention, the GPS component ensures that the user's location is accurately captured for precise, location-specific risk assessments. This real-time data capture capability is vital for the app's functionality, as it allows for the generation of risk maps that reflect the current conditions of the terrain. In accordance with an embodiment of the present invention, the landslide hazard/risk simulation algorithms are configured to process data from the data input module and GIS/GPS components to generate personalized risk assessments. These algorithms form the core computational engine of the app, applying BIS criteria to evaluate the stability of the terrain and predict potential landslide risks. In accordance with an embodiment of the present invention, the machine learning algorithms are configured to offer advanced analysis and comparison of results. By learning from historical data, these algorithms enhance the app's predictive capabilities, allowing it to adapt to new data and conditions, thereby improving the accuracy of risk assessments over time. In accordance with an embodiment of the present invention, the recommendation engine is configured to generate actionable advice for users based on the results from the simulation algorithms. This feature provides users with practical recommendations for mitigating landslide risks, such as bioremediation methods and drainage improvements, empowering them to take proactive measures to protect their properties and communities. DETAILED DESCRIPTION OF THE INVENTION The present invention is subsequently described herein using various embodiments. Throughout this description, the term 'may' is used in a permissive sense, indicating the potential to, rather than in a mandatory sense, indicating a requirement. Additionally, the words 'a' or 'an' signify at least one, and the word 'plurality' signifies 'one or more' unless otherwise specified. Moreover, the terminology and phraseology employed herein are solely for descriptive purposes and should not be construed as limiting in scope. Terms such as 'including', 'comprising', 'having', 'containing', or 'involving', and their variations, are intended to be broad and encompass the listed subject matter thereafter, as well as equivalents and additional subject matter not explicitly mentioned, and should not be interpreted as excluding other additives, components, integers, or steps. Similarly, the term 'comprising' is considered synonymous with the terms 'including' or 'containing' for applicable legal purposes. In the context of the present disclosure, it should be understood that the described embodiments in this section are put forth for illustrative purposes only. Those skilled in the art will appreciate that various modifications, adaptations, and alternative designs may be employed without departing from the scope and spirit of the invention. Accordingly, the present invention should not be limited to the specific embodiments illustrated herein, but rather should be construed according to the claims and description that follow. The present invention relates to a mobile application designed to assess landslide risk for communities residing in mountainous regions. The application leverages a combination of Geographic Information System (GIS), Global Positioning System (GPS), and machine learning technologies to provide real-time, location-specific risk assessments. The app is based on the Bureau of Indian Standards (BIS) guidelines IS 14496 (Part 1 and 2): 1998, which provide a framework for landslide hazard zoning in India. By integrating these guidelines, the app translates complex technical data into a user-friendly format, making landslide risk information accessible to non-experts. The invention comprises several key components, including a user interface (UI), data input module, GIS and GPS integration, landslide hazard/risk simulation algorithms, machine learning algorithms, and a recommendation engine. These components work together to provide personalized risk assessments and recommendations for mitigating landslide risks. The app's novel aspects include its integration with BIS guidelines, user-friendly interface, real-time data capture, and advanced machine learning capabilities, which collectively enhance the precision and accessibility of landslide risk assessments. In one embodiment, the user interface (UI) is designed to be simple and intuitive, allowing users to easily navigate the app and input necessary data. The UI includes interactive maps and data input options, enabling users to visualize their location and surrounding terrain. The UI is the front-end component of the app, directly interacting with the user and integrated with the back-end systems to display results and receive user inputs. The UI collects data from users, such as slope angle, land use, and precipitation, and displays the risk assessment results. It interacts with the GIS and GPS components to provide real-time data visualization. The data input module allows users to input location-specific data relevant to landslide risk, such as slope, soil conditions, and rainfall. This module is integrated within the UI, providing fields and options for users to enter data. The data input module sends the collected data to the processing algorithms for analysis. It works closely with the UI to ensure data is entered correctly and efficiently. The GIS and GPS integration captures real-time terrain information and integrates base maps for accurate location data. These components are backend elements that work in conjunction with the data input module and UI. They provide spatial data that is crucial for generating accurate risk maps. The GPS component ensures that the user's location is accurately captured, while GIS provides the necessary mapping capabilities. The landslide hazard/risk simulation algorithms process the input data to simulate landslide risk based on BIS criteria and advanced machine learning models. These algorithms are backend components that perform the core computational tasks of the app. They receive data from the data input module and GIS/GPS components, process it, and send the results back to the UI for display. The algorithms are central to the app's functionality, enabling personalized risk assessments. The BIS code integration is embedded within the simulation algorithms to ensure compliance with established standards. This component provides a framework for the algorithms to assess risk, ensuring that the app's outputs are reliable and standardized. The machine learning algorithms offer advanced analysis and comparison of results for better understanding of risk zones. They are integrated with the landslide hazard/risk simulation algorithms. These algorithms enhance the app's predictive capabilities by learning from historical data and improving risk assessment accuracy over time. The recommendation engine provides personalized recommendations based on the assessed risk, such as bioremediation methods and drainage improvements. It is a backend component that works after the risk assessment is completed. The engine uses the results from the simulation algorithms to generate actionable advice for users, which is then displayed through the UI. In an embodiment of the present invention, the app may be implemented to allow users to input specific data about their property, such as slope, soil conditions, and local rainfall. The app processes this data using the integrated algorithms to generate personalized landslide risk assessments. Users can visualize potential landslide scenarios based on their input data and produce real-time risk maps that are easy to interpret for non-experts. Based on the risk assessment, the app provides users with personalized recommendations for mitigating landslide risks, such as bioremediation methods, drainage improvements, and terrace design. The invention may also include variations where the machine learning algorithms are configured to offer advanced analysis and comparison of results, providing users with multiple perspectives on potential hazards. Another embodiment may involve the app's deployment on popular mobile platforms to ensure wide accessibility, with training resources or tutorials to help users understand how to effectively use the app and interpret the results. The invention's benefits include providing communities in landslide-prone areas with easy access to landslide risk information through a user-friendly mobile app, simplifying complex technical data from BIS guidelines. The app enables users to input location-specific data to receive personalized landslide risk assessments, offering more accurate risk profiles for individual properties. The integration of BIS guidelines and machine learning algorithms ensures that the app's risk assessments are based on established standards and methodologies, while also providing advanced predictive capabilities. These technical features empower local communities to tackle landslide hazards by equipping them with the necessary tools and information to make informed decisions. Various modifications to these embodiments are evident to those skilled in the art based on the description. The principles associated with the various embodiments described herein can be applied to additional embodiments. Consequently, the description is not intended to be limited to the embodiments but aims to provide the broadest scope consistent with the principles and the innovative and inventive features disclosed or suggested herein. Therefore, the invention is expected to encompass all other such alternatives, modifications, and variations falling within the scope of the present invention and appended claims.

Disclaimer: Curated by HT Syndication.