Water Quality Prediction Using Artificial Intelligence Algorithms

Aldhyani, Theyazn H. H.; Al-Yaari, Mohammed; Alkahtani, Hasan; Maashi, Mashael

doi:10.1155/2020/6659314

Cited by 232 publications

(91 citation statements)

References 38 publications

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“…Deep learning algorithms use many deeper hidden layers to surpass classical ANN methods. [46,47]. A convolutional neural network is one of the most popular deep neural network algorithms, and it is named convolution by using mathematical linear operation between matrices.…”

Section: Correlation Analysismentioning

confidence: 99%

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

Alkahtani

Aldhyani

2021

Complexity

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Smart grids, advanced information technology, have become the favored intrusion targets due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid environment. These data are sent to the cloud, which is a huge network of super servers that provides different services to different smart infrastructures, such as smart homes and smart buildings. These can provide a large space for attackers to launch destructive cyberattacks. The novelty of this proposed research is the development of a robust framework system for detecting intrusions based on the IoT environment. An IoTID20 dataset attack was employed to develop the proposed system; it is a newly generated dataset from the IoT infrastructure. In this framework, three advanced deep learning algorithms were applied to classify the intrusion: a convolution neural network (CNN), a long short-term memory (LSTM), and a hybrid convolution neural network with the long short-term memory (CNN-LSTM) model. The complexity of the network dataset was dimensionality reduced, and to improve the proposed system, the particle swarm optimization method (PSO) was used to select relevant features from the network dataset. The obtained features were processed using deep learning algorithms. The experimental results showed that the proposed systems achieved accuracy as follows: CNN = 96.60%, LSTM = 99.82%, and CNN-LSTM = 98.80%. The proposed framework attained the desired performance on a new variable dataset, and the system will be implemented in our university IoT environment. The results of comparative predictions between the proposed framework and existing systems showed that the proposed system more efficiently and effectively enhanced the security of the IoT environment from attacks. The experimental results confirmed that the proposed framework based on deep learning algorithms for an intrusion detection system can effectively detect real-world attacks and is capable of enhancing the security of the IoT environment.

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Section: Correlation Analysismentioning

confidence: 99%

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

Alkahtani

Aldhyani

2021

Complexity

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“…It has the power to be adjusted in order to lower its error without being sure that the error could not be lower still [10]. e authors in [11] considered ANN among the most powerful machine learning algorithms for time-series predictions. A large-scale empirical comparison between ten supervised learning methods demonstrated that neural networks are more competitive and efficient than boosting, random forests, bagging, and support vector machines [12].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

Alla

Moumoun

Balouki

2021

Scientific Programming

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Flight delay is the most common preoccupation of aviation stakeholders around the world. Airlines, which suffer from a monetary and customer loyalty loss, are the most affected. Various studies have attempted to analyze and solve flight delays using machine learning algorithms. This research aims to predict flights’ arrival delay using Artificial Neural Network (ANN). We applied a MultiLayer Perceptron (MLP) to train and test our data. Two approaches have been adopted in our work. In the first one, we used historical flight data extracted from Bureau of Transportation Statistics (BTS). The second approach improves the efficiency of the model by applying selective-data training. It consists of selecting only most relevant instances from the training dataset which are delayed flights. According to BTS, a flight whose difference between scheduled and actual arrival times is 15 minutes or greater is considered delayed. Departure delays and flight distance proved to be very contributive to flight delays. An adjusted and optimized hyperparameters using grid search technique helped us choose the right architecture of the network and have a better accuracy and less error than the existing literature. The results of both traditional and selective training were compared. The efficiency and time complexity of the second method are compared against those of the traditional training procedure. The neural network MLP was able to predict flight arrival delay with a coefficient of determination R 2 of 0.9048, and the selective procedure achieved a time saving and a better R 2 score of 0.9560. To enhance the reliability of the proposed method, the performance of the MLP was compared with that of Gradient Boosting (GB) and Decision Trees (DT). The result is that the MLP outperformed all existing benchmark methods.

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“…However, this model only considers single‐dimensional input, while there are more complex datasets with many different dimensions for water quality monitoring. Aldhyani et al (2020) proposed a model by using advanced artificial intelligence algorithms to measure the future water quality. The SVM algorithm has achieved the highest accuracy; however, the analysis should be extended to different types of water.…”

Section: Motivationmentioning

confidence: 99%

Chronological Harris hawks‐based deep LSTM classifier in wireless sensor network for aqua status prediction

Chandanapalli¹,

Rao²,

Srinivas³

et al. 2021

Ecohydrology

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Aquaculture becomes very popular in economic where aquatic organisms, like fishes and prawns, are mainly dependent on the quality of water in aquaculture pond. Also, the water quality constraints, which include turbidity, carbon dioxide, temperature, pH level, dissolved oxygen and phosphorus, are considered for achieving better performance. Hence, this paper presents an approach for aqua status prediction based on Deep Long Short‐Term Memory (Deep LSTM) classifier. The sensor nodes are placed in the aqua pond for measuring the parameters of water quality, and then the cell network transformation is done using the Voronoi partition. After that, the Cluster Head (CH) selection is carried out using Piecewise Fuzzy C‐means clustering (piFCM). Once the clusters are selected, the Chronological Harris Hawks (Chronological HH) optimization algorithm is introduced for optimal sink placement where the constraints for enabling the optimal sink placement are the distance and energy of the nodes. Finally, the aqua status is predicted using Deep LSTM. The performance of the Chronological HH‐based Deep LSTM is computed in terms of accuracy, energy and the number of dead nodes. The proposed Chronological HH‐based Deep LSTM outperformed other methods with maximal accuracy of 92.65%, maximal energy of 0.976 and the minimal dead nodes of 32.

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Water Quality Prediction Using Artificial Intelligence Algorithms

Cited by 232 publications

References 38 publications

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms

A Multilayer Perceptron Neural Network with Selective-Data Training for Flight Arrival Delay Prediction

Chronological Harris hawks‐based deep LSTM classifier in wireless sensor network for aqua status prediction

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