Assessing the Water Pollution of the Brahmaputra River Using Water Quality Indexes

Water quality is continuously affected by anthropogenic and environmental conditions. A significant issue of the Indian rivers is the massive water pollution, leading to the spreading of different diseases due to its daily use. Therefore, this study investigates three aspects. The first one is testing the hypothesis of the existence of a monotonic trend of the series of eight water parameters of the Brahmaputra River recorded for 17 years at ten hydrological stations. When this hypothesis was rejected, a loess trend was fitted. The second aspect is to assess the water quality using three indicators (WQI)–CCME WQI, British Colombia, and a weighted index. The third aspect is to group the years and the stations in clusters used to determine the regional (spatial) and temporal trend of the WQI series, utilizing a new algorithm. A statistical analysis does not reject the hypothesis of a monotonic trend presence for the spatially distributed data but not for the temporal ones. Hierarchical clustering based on the computed WQIs detected two clusters for the spatially distributed data and two for the temporal-distributed data. The procedure proposed for determining the WQI temporal and regional evolution provided good results in terms of mean absolute error, root mean squared error (RMSE), and mean absolute percentage error (MAPE).

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Computer Architectures for Spatially Distributed Data

10.1007/978-3-642-82150-9 ◽

1985 ◽

Keyword(s):

Computer Architectures ◽

Distributed Data ◽

Spatially Distributed ◽

Spatially Distributed Data

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Tile2Vec: Unsupervised Representation Learning for Spatially Distributed Data

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33013967 ◽

2019 ◽

Vol 33 ◽

pp. 3967-3974 ◽

Cited By ~ 7

Author(s):

Neal Jean ◽

Sherrie Wang ◽

Anshul Samar ◽

George Azzari ◽

David Lobell ◽

...

Keyword(s):

Natural Language ◽

Learning Algorithm ◽

Representation Learning ◽

Distributed Data ◽

Simple Arithmetic ◽

Latent Space ◽

Wide Range ◽

Spatially Distributed ◽

Vector Representations ◽

Spatially Distributed Data

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language — words appearing in similar contexts tend to have similar meanings — to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations for both image and non-image datasets. Our learned representations significantly improve performance in downstream classification tasks and, similarly to word vectors, allow visual analogies to be obtained via simple arithmetic in the latent space.

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Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River

Applied Sciences ◽

10.3390/app9091883 ◽

2019 ◽

Vol 9 (9) ◽

pp. 1883

Author(s):

Przemysław Hawro ◽

Tadeusz Kwater ◽

Robert Pękala ◽

Bogusław Twaróg

Keyword(s):

Water Quality ◽

Adaptive Algorithm ◽

Mean Squared Error ◽

Oxygen Demand ◽

Gain Coefficient ◽

Soft Sensor ◽

Percentage Error ◽

Polluted River ◽

Online Measurements ◽

The Mathematical Model

This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Simulation studies of a biochemically polluted river, for which the water quality was represented by biochemical oxygen demand (BOD) indices and the dissolved oxygen (DO) deficit, were carried out. The algorithm concept uses only online measurements of the object, and adaptive changes in the gain coefficient are determined based on the adaptation error adopted for this purpose. Simulation results indicated the correct functioning of the soft sensor even for inaccurately identified parameters of the mathematical model and for unknown values and intensity of disturbances affecting the object. The quality of the signals monitored via a soft sensor implemented in this way was determined with the root-mean-squared error (RMSE) and mean percentage error (MPE) indicators and compared with the Kalman filter.

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