An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources

Katrina, Yu, K. Daniel, Haeusler, M. Hank, Fabbri, Alessandra and Simons,; Liu, Huan; Chen, Zhe

doi:10.3390/math9121316

Cited by 25 publications

(13 citation statements)

References 34 publications

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“…Through the control of government policies, the application of science and technology, and the reuse of water, industrial water consumption can be reduced, thus water resources can be saved to support the water shortage areas. [ 48 ]…”

Section: Resultsmentioning

confidence: 99%

Prediction of Water Consumption in 31 Provinces of China Based on FGM(1,1) Model

Qiao

Yang

2022

CLEAN Soil Air Water

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Human production activities and social development cannot be separated from the indispensable water resources. In recent years, with the increasing speed of China's economic development, the consumption of water resources has gradually become the focus of attention of the Chinese government and the public. Based on the original data of water use of 31 provinces (municipalities and autonomous regions) in China from 2014 to 2018, a fractional order accumulative gray prediction model (FGM(1,1)) is established to predict the water use of 31 provinces (municipalities and autonomous regions) in China in the short term, and the applicability of the model is verified by the mean absolute percentage error (MAPE) value. According to the MAPE value, the data obtained by the FGM(1,1) model is relatively accurate. The prediction results show that in case of little change in total water consumption, agricultural water and industrial water use show a downward trend, while domestic water use shows an upward trend. The government's water management policy for urban residents needs to be adjusted, and the publicity work to improve the water-saving awareness of urban residents needs to be improved.

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Section: Resultsmentioning

confidence: 99%

Prediction of Water Consumption in 31 Provinces of China Based on FGM(1,1) Model

Qiao

Yang

2022

CLEAN Soil Air Water

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“…The new variant, MSMA, successfully moved out of local optima based on the balance in the search procedure. Another enhanced SMA (ESMA) was reported in 64 and is like the preceding variant with respect to the use of chaotic theory but differs from its use of the opposition learning method. ESMA was applied to solve the estimation of water needs in a region using data on historical water consumption and local economic structure.…”

Section: Related Workmentioning

confidence: 99%

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Oyelade

Ezugwu

2022

Sci Rep

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Feature classification in digital medical images like mammography presents an optimization problem which researchers often neglect. The use of a convolutional neural network (CNN) in feature extraction and classification has been widely reported in the literature to have achieved outstanding performance and acceptance in the disease detection procedure. However, little emphasis is placed on ensuring that only discriminant features extracted by the convolutional operations are passed on to the classifier, to avoid bottlenecking the classification operation. Unfortunately, since this has been left unaddressed, a subtle performance impairment has resulted from this omission. Therefore, this study is devoted to addressing these drawbacks using a metaheuristic algorithm to optimize the number of features extracted by the CNN, so that suggestive features are applied for the classification process. To achieve this, a new variant of the Ebola-based optimization algorithm is proposed, based on the population immunity concept and the use of a chaos mapping initialization strategy. The resulting algorithm, called the immunity-based Ebola optimization search algorithm (IEOSA), is applied to the optimization problem addressed in the study. The optimized features represent the output from the IEOSA, which receives the noisy and unfiltered detected features from the convolutional process as input. An exhaustive evaluation of the IEOSA was carried out using classical and IEEE CEC benchmarked functions. A comparative analysis of the performance of IEOSA is presented, with some recent optimization algorithms. The experimental result showed that IEOSA performed well on all the tested benchmark functions. Furthermore, IEOSA was then applied to solve the feature enhancement and selection problem in CNN for better prediction of breast cancer in digital mammography. The classification accuracy returned by the IEOSA method showed that the new approach improved the classification process on detected features when using CNN models.

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“…This phase mimics the contraction of venous tissues of the SM to search food. It alters the searching pattern based on the quality of food [26]. It is arithmetically formulated in the following equation,…”

Section: Smo Based Hyperparameter Optimizationmentioning

confidence: 99%

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Hamza¹,

Alsolai²,

Alamgeer³

et al. 2022

Computers, Materials &Amp; Continua

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Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning (DL) is a kind of ML technique which yields effective performance on data classification and prediction tasks. With this motivation, the current study introduces a novel Slime Mould Optimization (SMO) model with Bidirectional Gated Recurrent Unit (BiGRU) model for Traffic Prediction (SMOBGRU-TP) in smart cities. Initially, data preprocessing is performed to normalize the input data in the range of [0, 1] using minmax normalization approach. Besides, BiGRU model is employed for effective forecasting of traffic in smart cities. Moreover, the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method. The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model's superior performance in terms of prediction compared to existing techniques.

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An Improved Slime Mould Algorithm for Demand Estimation of Urban Water Resources

Cited by 25 publications

References 34 publications

Prediction of Water Consumption in 31 Provinces of China Based on FGM(1,1) Model

Prediction of Water Consumption in 31 Provinces of China Based on FGM(1,1) Model

Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

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