Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm

Zakhrouf, Mousaab; Bouchelkia, Hamid; Kim, Sungwon; Stamboul, Madani

doi:10.1080/02723646.2021.1943126

Cited by 17 publications

(5 citation statements)

References 49 publications

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“…This relationship can be understood as follows: artificial intelligence is the end, machine learning is the means, and deep learning is the best way to achieve this goal [26,27]. Owing to its strong ability to fit functions, deep learning has been widely researched as an effective machine-learning algorithm [28,29], with a large number of successful cases in the fields of medicine, agriculture, and hydrology [30][31][32].…”

Section: Deep Learning-based Intelligent Biosensorsmentioning

confidence: 99%

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Chen,

Wang,

Zhang

et al. 2024

Foods

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Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety.

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Section: Deep Learning-based Intelligent Biosensorsmentioning

confidence: 99%

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Chen,

Wang,

Zhang

et al. 2024

Foods

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“…Among them, the task of the worker ant colony is only responsible for feeding back from the confirmed optimal path, and the author did not consider it when designing the cloud database path optimization algorithm [4]; Reconnaissance ants are mainly responsible for local reconnaissance, searching around each node in the cloud database and leaving reconnaissance results (reconnaissance elements) to provide assistance for searching ants; Search ants 1) Reconnaissance ants place m reconnaissance ants on n nodes, with each reconnaissance ant scouting n-1 other nodes centered around its node. The reconnaissance results are combined with existing MAXPC (prior knowledge) to form reconnaissance elements, denoted as s [i] [j], marked on the path from node i to j, the search ant colony can calculate the state transition probability P k ij and adjust the amount of information on each path based on the marked detection elements and existing pheromones [5]. The calculation formula for s…”

Section: Basic Polymorphic Ant Colony Algorithm Modelmentioning

confidence: 99%

Dynamic Security Rule Optimization Based on Deep Learning and Adaptive Algorithms

Hang,

Xie,

Zhang

et al. 2024

SCPE

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Compared with traditional computer network security identification techniques, deep learning algorithms are based on their own distributed network structure for storage, processing, classification, comparison, and automatic identification functions. The distribution and dynamism of cloud databases increase the difficulty of route prediction and recognition in the cloud, affecting the efficiency of cloud computing. In response to the above issues, the author proposes a dynamic path optimization process for cloud databases based on adaptive immune grouping polymorphic ant colony algorithm. By setting up two states of ant colony, reconnaissance ant and search ant, and introducing an adaptive polymorphic ant colony competition strategy, the defect of general ant colony algorithms being prone to falling into local optima is improved; On this basis, an artificial immune algorithm with fast global search capability is further integrated to improve the search ant path optimization process, improving search speed and accuracy. Simulation experiments show that the IPANT algorithm outperforms the other three algorithms, maintaining a throughput of 1000 kbps and relatively stable; The data of OSPF, SPF, and FR are not significantly different, significantly lower than IPANT. The immune polymorphic ant colony algorithm (IPANT) has the lowest time delay for packet routing and performs better than the other three algorithms, with FR and SPF having higher latency. It has been proven that this algorithm can better solve convergence speed and global optimization problems, and can quickly and reasonably find the database to be accessed in the cloud.

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“…Zakhrouf et al [128] integrated three deep learning techniques (i.e., ERNN, LSTM, and GRU) and one machine learning method (i.e., FFNN) by combining each model with the PSO algorithm to simulate the daily streamflow data in Sidi Aich and Ponteba Defluent stations, Algeria over six years. The results of the testing stage indicate that the GRUII two-stage combined technique delivers the best results compared with the rest of the models.…”

Section: Hybridisation Of Parameter Optimisation-based and Pre Proces...mentioning

confidence: 99%

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Abdul Kareem,

L. Zubaidi,

Al-Ansari

et al. 2024

Computer Modeling in Engineering &Amp; Sciences

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Forecasting river flow is crucial for optimal planning, management, and sustainability using freshwater resources. Many machine learning (ML) approaches have been enhanced to improve streamflow prediction. Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches. Current researchers have also emphasised using hybrid models to improve forecast accuracy. Accordingly, this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years, summarising data preprocessing, univariate machine learning modelling strategy, advantages and disadvantages of standalone ML techniques, hybrid models, and performance metrics. This study focuses on two types of hybrid models: parameter optimisation-based hybrid models (OBH) and hybridisation of parameter optimisation-based and preprocessing-based hybrid models (HOPH). Overall, this research supports the idea that meta-heuristic approaches precisely improve ML techniques. It's also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches (classified into four primary classes) hybridised with ML techniques. This study revealed that previous research applied swarm, evolutionary, physics, and hybrid metaheuristics with 77%, 61%, 12%, and 12%, respectively. Finally, there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

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Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm

Cited by 17 publications

References 49 publications

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0

Dynamic Security Rule Optimization Based on Deep Learning and Adaptive Algorithms

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

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