A CNN-BiLSTM-SVR based Deep Hybrid Model for Water Quality Forecasting of the River Ganga

Kogekar, Aishwarya Premlal; Nayak, Rashmiranjan; Pati, Umesh Chandra

doi:10.1109/indicon52576.2021.9691532

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…On the other hand, ensemble techniques form a separate class of competitive models, where an ensemble is structured by merging different models' observations, outperforming other classical approaches by engrossing different models' rich features. Subsequently, ConvLSTM+SVM+RF, LR+RF, CNN+LSTM, and other model combinations have been asserted for use in such tasks [25]- [27]. Interestingly, numerous ingenious DLoriented techniques have been applied recently for RS-based applications, among which a successful paradigm is originated by the consolidation of Bidirectional ConvLSTM with the UNet (BiConvLSTM-UNet) [28]- [30].…”

Section: A Related Workmentioning

confidence: 99%

Deep Learning-Oriented c-GAN Models for Vegetative Drought Prediction on Peninsular India

Shukla,

Pandya

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, the vegetative drought prediction employing Deep Learning (DL) models is designed, incorporating rainfall data and NOAA satellite-data derived Vegetation Health Index (VHI) values spanning 1981-2022. Correspondingly, two DL-oriented models based on Generative Adversarial Networks (GANs): 1) Pix2Pix GAN (P2P) and 2) Bidirectional Convolutional LSTM (BiConvLSTM)-P2P GAN (BiCP2P) are developed over the targeted Region of Interest (ROIs). The assimilation of generative DL models for the application of drought forecasting constitutes a novel investigation and a stateof-art approach targeted in this work. Subsequently, the primary ROI designated is peninsular India, and the models' efficacy is further validated by implementing it on two more ROIs: the Karnataka and Rajasthan states of India. The proposed models' outcomes are compared with several preferred methodologies, quantitatively through Coefficient of Determination (R2 score), Mean Squared Error (MSE), and Mean Absolute Error (MAE) and qualitatively employing drought maps denoting the VHIbased drought severity levels over the ROI. Remarkably, excellent performance is demonstrated by the proposed models over peninsular India, with earned R2 score, MSE, and MAE values of 0.971, 0.0016, and 0.020 for P2P and 0.963, 0.0021, and 0.0239 for BiCP2P, respectively. Moreover, generated drought maps efficiently portrayed the drought severities across the land cover and could potentially be extended further for rapid drought risk assessments. The proposed models functioned outstandingly for the developed datasets on the ROIs, corroborating their potential for similar forecasting applications on other climatic zones, which can aid in better planning and preparedness to tackle natural predicaments such as drought calamity.

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Section: A Related Workmentioning

confidence: 99%

Deep Learning-Oriented c-GAN Models for Vegetative Drought Prediction on Peninsular India

Shukla,

Pandya

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…C. Calculation of Water Quality Index [3] WQI have been calculated to assign a unique value to overall water quality & it is calculated using different water quality parameters that represent the actual quality of water individually [7]. In our study four parameters namely temperature, pH, DO and BOD are used to calculate WQI.…”

Section: B Water Quality Data Collection and Preprocessingmentioning

confidence: 99%

WaterQualityPrediction of Ganga River usingTime-seriesModels

Lal,

Kumar

2023

Preprint

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Life of Living organism have present on the earth depends on Water. Water Quality is also equally important as Water. Ganga river is fulfilling the needs of water of a large population of India. Being a citizen of India it’s our responsibility to keep the Ganga River neat & clean. A large number of governments funded base stations available for forecasting the Water Quality of ganga river. But there is a need of low-cost prediction techniques of water quality based on data available from these base stations. It can help the government to take the necessary decisions to cure the water quality of Ganga River & save the lives of many livings’ organism depends on Ganga River. Monitoring & forecasting of water quality of Ganga River is most important because ganga river is the main source of drinking water of a large population of India. In this paper two time series-based models such as Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) have been used to predict the water quality of Ganga River. The models are developed on water quality data available of 10 base stations on the Uttarakhand Pollution Control Board’s official website. Four water quality parameters-Temp, pH, DO, BOD data is used for models training & calculating WQI (Water Quality Index). The result of experiment shows that SARIMA model predict the water quality parameters as well as Water Quality Index (WQI) more accurately.

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“…Bi-LSTM has better performance than LSTM and RNN because it uses both preceding and subsequent information. CNN is a deep learning model that has predominantly been used in image recognition and is increasingly being used in time series data prediction [20], [36] and [37]. CNN is usually used in modelling complex nonlinear systems that handle multi-dimensionality aspects.…”

Section: Hosted Filementioning

confidence: 99%

A HYBRID CNN & BiLSTM WITH AM MODEL TO PREDICT AND ESTIMATE LOAD HARMONICS AT NESTLE EAST LONDON SOUTH AFRICA.

Kuyumani

Shongwe

Hasan

2022

Preprint

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Prediction of electrical power harmonics promotes development and provision of clean power supply. However, the elevated frequency combined with noise of harmonics make precise forecasting challenging and demanding. Due to the stochastic nature of harmonics occurrence and the challenge of attaining a dependable, and efficient working model, to this date research efforts have not been sufficient. Although various statistical and machine learning algorithms have shown very interesting and promising results, but work is still being done on algorithms that would produce the least possible error. This research uses hybrid convolutional neural network (CNN) and bidirectional long short term memory (BiLSTM) with Attention Mechanism (AM) model, to predict load harmonics at Nestle, a confectionary manufacturing plant in South Africa. Historical load harmonics data is used as the dataset. The results show that the hybrid deep machine learning method CNN – BiLSTM – AM has better performance compared to other five prediction models when detecting and forecasting load harmonics. The hybrid algorithm has a prediction accuracy of 92.3569 % and the lowest RMSE of 0.0000002215.

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A CNN-BiLSTM-SVR based Deep Hybrid Model for Water Quality Forecasting of the River Ganga

Cited by 20 publications

References 25 publications

Deep Learning-Oriented c-GAN Models for Vegetative Drought Prediction on Peninsular India

Deep Learning-Oriented c-GAN Models for Vegetative Drought Prediction on Peninsular India

WaterQualityPrediction of Ganga River usingTime-seriesModels

A HYBRID CNN & BiLSTM WITH AM MODEL TO PREDICT AND ESTIMATE LOAD HARMONICS AT NESTLE EAST LONDON SOUTH AFRICA.

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