Enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques

Abstract

Abstract: The present invention is an enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques, an enhance prediction accuracy, an improved Long Short-Term Memory (LSTM) model is proposed, a hybrid approach is utilized to optimize the LSTM model's parameters, addressing the non-stationary unpredictability and nonlinearity of water quality data. This hybrid model incorporates the COOT method, inspired by the behavior of coot bird flocks, along with elements of the cuckoo bird’s reproductive strategy and the model was validated using actual weekly water quality data. By combining LSTM with this novel optimization technique, the proposed model could serve as an alternative framework for predicting water quality, supporting broader basin-wide initiatives to monitor water quality and control pollutants.

Specification

Description:Title of the invention:
Enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques

Field of the invention:

The present invention relates to the field of water quality prediction and particular relates to field of an enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques.

Prior art to the invention:

1. A patent document with application number "CN117808143" titled "Deep learning-based aeration quantity prediction method for water quality purification plant" is described here, "A deep learning -based aeration quantity prediction method for a water quality purification plant is characterized by comprising the following steps: collecting data related to operation of the water quality purification plant, and ensuring data quality and integrity; performing cleaning, normalization and feature engineering processing on the data; constructing a model, inputting data into the model, training the model by using a historical data set, and measuring the accuracy of the model by using a mean square error; deploying the trained model into a water quality purification plant for predicting the aeration rate in real time and regularly monitoring the performance of the model; and analyzing the output of the deep learning model, understanding factors which have important influences on prediction of the aeration rate, and further optimizing operation and maintenance strategies of the water quality purification plant according to an interpretive analysis result. According to the method, the deep learning model is utilized, so that the prediction accuracy of the aeration rate is improved, and the model can more accurately adjust the aeration rate to meet actual requirements."

wherein, the present invention is an enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques.


Objects of the invention:

It is a primary object of the present invention is an enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques.
Summary of the invention:
An aspect of the present invention of an enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques.


























Detailed description:
The following specification particularly describes the invention and the manner in which it is to be performed.

The present invention will be further described in detail below through specific embodiments.

An embodiment of the present invention is a predicting water quality using deep learning algorithms has gained significant attention due to its ability to handle complex, non-linear, and time-dependent data patterns. Water resource management depends on accurate water quality predictions. This study focuses on forecasting water quality metrics within the watershed system, particularly dissolved oxygen (DO) levels. To enhance prediction accuracy, an improved Long Short-Term Memory (LSTM) model is proposed. Additionally, a hybrid approach is utilized to optimize the LSTM model's parameters, addressing the non-stationary unpredictability and nonlinearity of water quality data. This hybrid model incorporates the COOT method, inspired by the behavior of coot bird flocks, along with elements of the cuckoo bird's reproductive strategy. The model was validated using actual weekly water quality data. By combining LSTM with this novel optimization technique, the proposed model could serve as an alternative framework for predicting water quality, supporting broader basin-wide initiatives to monitor water quality and control pollutants.
, Claims:Claims:

I claim,
1. An enhancing water quality prediction with hybrid deep learning and metaheuristic optimization techniques, a method claim, an enhance prediction accuracy, an improved Long Short-Term Memory (LSTM) model is proposed, a hybrid approach is utilized to optimize the LSTM model's parameters, addressing the non-stationary unpredictability and nonlinearity of water quality data,
wherein, this hybrid model incorporates the COOT method, inspired by the behavior of coot bird flocks, along with elements of the cuckoo bird's reproductive strategy and the model was validated using actual weekly water quality data, and
wherein, by combining LSTM with this novel optimization technique, the proposed model could serve as an alternative framework for predicting water quality, supporting broader basin-wide initiatives to monitor water quality and control pollutants.

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