Water Care: Water Surface Cleaning Bot and Water Body Surveillance
System
- URL: http://arxiv.org/abs/2111.12579v1
- Date: Wed, 24 Nov 2021 15:59:41 GMT
- Title: Water Care: Water Surface Cleaning Bot and Water Body Surveillance
System
- Authors: Harsh Sankar Naicker, Yash Srivastava, Akshara Pramod, Niket Paresh
Ganatra, Deepakshi Sood, Saumya Singh, Velmathi Guruviah
- Abstract summary: There is a lot of plastic waste on the surface of rivers and lakes.
The Ganga river is one of the 10 rivers which account for 90 percent of the plastic that ends up in the sea.
From 2001 to 2012, in the city of Hyderabad, 3245 hectares of lakes dissipated.
- Score: 0.5387709967970504
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Whenever a person hears about pollution, more often than not, the first
thought that comes to their mind is air pollution. One of the most
under-mentioned and under-discussed pollution globally is that caused by the
non-biodegradable waste in our water bodies. In the case of India, there is a
lot of plastic waste on the surface of rivers and lakes. The Ganga river is one
of the 10 rivers which account for 90 percent of the plastic that ends up in
the sea and there are major cases of local nalaas and lakes being contaminated
due to this waste. This limits the source of clean water which leads to major
depletion in water sources. From 2001 to 2012, in the city of Hyderabad, 3245
hectares of lakes dissipated. The water recedes by nine feet a year on average
in southern New Delhi. Thus, cleaning of these local water bodies and rivers is
of utmost importance. Our aim is to develop a water surface cleaning bot that
is deployed across the shore. The bot will detect garbage patches on its way
and collect the garbage thus making the water bodies clean. This solution
employs a surveillance mechanism in order to alert the authorities in case
anyone is found polluting the water bodies. A more sustainable system by using
solar energy to power the system has been developed. Computer vision algorithms
are used for detecting trash on the surface of the water. This trash is
collected by the bot and is disposed of at a designated location. In addition
to cleaning the water bodies, preventive measures have been also implemented
with the help of a virtual fencing algorithm that alerts the authorities if
anyone tries to pollute the water premises. A web application and a mobile app
is deployed to keep a check on the movement of the bot and shore surveillance
respectively. This complete solution involves both preventive and curative
measures that are required for water care.
Related papers
- AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment [46.56288727659417]
Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization.
We introduce AirCast, a novel multi-variable air pollution forecasting model.
AirCast employs a multi-task head architecture that simultaneously forecasts atmospheric conditions and pollutant concentrations.
arXiv Detail & Related papers (2025-02-25T07:34:18Z) - Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring [68.41400824104953]
This paper presents a vehicle prototype that addresses the use of Artificial Intelligence algorithms and enhanced sensing techniques for water quality monitoring.
The vehicle is fully equipped with high-quality sensors to measure water quality parameters and water depth.
By means of a stereo-camera, it also can detect and locate macro-plastics in real environments.
arXiv Detail & Related papers (2024-10-08T10:35:32Z) - Making AI Less "Thirsty": Uncovering and Addressing the Secret Water
Footprint of AI Models [34.93600962447119]
Training GPT-3 in Microsoft's state-of-the-art U.S. data centers can directly evaporate 700,000 liters of clean freshwater.
The global AI demand may be accountable for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027.
To respond to the global water challenges, AI models can, and also must, take social responsibility and lead by example.
arXiv Detail & Related papers (2023-04-06T17:55:27Z) - IoT-Based Water Quality Assessment System for Industrial Waste
WaterHealthcare Perspective [1.1318749736230347]
polluted water can cause food poisoning, diarrhea, short-term gastrointestinal problems, respiratory diseases, skin problems, and other serious health complications.
In a developing country like Bangladesh, where ready-made garments sector is one of the major sources of the total Gross Domestic Product (GDP), most of the wastes released from the garment factories are dumped into the nearest rivers or canals.
To address this issue, we developed an Internet of Things (IoT)-based real-time water quality monitoring system, integrated with a mobile application.
arXiv Detail & Related papers (2023-03-26T07:17:18Z) - Waste Detection and Change Analysis based on Multispectral Satellite
Imagery [0.0]
We analyze two possible forms of waste detection: identification of hot-spots (i.e. illegal waste dumps) and identification of water-surface river blockages.
We found that using satellite imagery and machine learning are viable to locate and to monitor the change of the previously detected waste.
arXiv Detail & Related papers (2023-03-25T17:12:22Z) - In-situ Water quality monitoring in Oil and Gas operations [1.9857559596234144]
Many existing satellite-based monitoring studies utilize index-based methods to monitor large water bodies such as rivers and oceans.
We propose a new Water Quality Enhanced Index (WQEI) Model, which is designed to enable users to determine contamination levels in water bodies with weak reflectance patterns.
Our results show that 1) WQEI is a good indicator of water turbidity validated with 1200 water samples measured in the laboratory, and 2) by applying our method to commonly available satellite data (e.g. LandSat8), one can achieve high accuracy water quality monitoring efficiently in large regions.
arXiv Detail & Related papers (2023-01-20T20:56:52Z) - Deep object detection for waterbird monitoring using aerial imagery [56.1262568293658]
In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone.
By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast.
arXiv Detail & Related papers (2022-10-10T17:37:56Z) - Water Surface Patch Classification Using Mixture Augmentation for River
Scum Index [0.0]
Urban rivers provide a water environment that influences residential living.
We focus on the organic mud, or "scum" that accumulates on the river's surface and gives it its peculiar odor and external economic effects on the landscape.
We propose a patch classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers.
arXiv Detail & Related papers (2022-07-13T17:45:25Z) - Climate Change & Computer Audition: A Call to Action and Overview on
Audio Intelligence to Help Save the Planet [98.97255654573662]
This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges.
We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
arXiv Detail & Related papers (2022-03-10T13:32:31Z) - ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste.
This dataset contains over1800fully segmented video frames collected from a real waste sorting plant.
We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
arXiv Detail & Related papers (2021-06-04T22:17:09Z) - Underwater image filtering: methods, datasets and evaluation [44.933577173776705]
We review the design principles of underwater image filtering methods.
We discuss image formation models and the results of restoration methods in various water types.
We present task-dependent enhancement methods and datasets for training neural networks and for method evaluation.
arXiv Detail & Related papers (2020-12-22T18:56:39Z) - Attention Neural Network for Trash Detection on Water Channels [2.4660652494309936]
Rivers and canals flowing through cities are often used illegally for dumping the trash.
This contaminates freshwater channels as well as causes blockage in sewerage resulting in urban flooding.
This paper proposes a method for the detection of visible trash floating on the water surface of the canals in urban areas.
arXiv Detail & Related papers (2020-07-09T08:41:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.