Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean

Abstract

In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O2 and CO2 respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (MhWNN) refined by the efficient Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS), i.e., MhWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the MhWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm.

Modeling Surface Runoff in Al-Mohammadi Valley: Influence of Climate and Soil Parameters

“…In the present study, we use an MLP trained with a backpropagation algorithm to predict the drainage basin runoff. The MLP consists of an input layer consisting of node (s) representing various input variable(s), the hidden layer consisting of many hidden nodes, and an output layer consisting of output variable(s) [25]. The input nodes pass on the input signal values to the nodes in the hidden layer unprocessed.…”

“…The input nodes pass on the input signal values to the nodes in the hidden layer unprocessed. The values are distributed to all the nodes in the hidden layer depending on the connection weights Wij and Wjk (24)(25)(26) between the input node and the hidden nodes. Connection weights are the interconnecting links between the neurons in successive layers.…”

Quantifying Plastic Waste and Microplastic Contamination in African Aquatic Systems: An Imperative for Sustainable Waste Management

“…In the environment, plastics find their way into aquatic, terrestrial, and atmospheric compartments, propelled by factors like wind and water currents [3]. Roads, wastewater pathways, and inadequate waste management systems are known conduits for plastics into marine environments [4].…”

Pricing Decision of Closed-Loop Supply Chain to Improve Service Level under Patent Protection

“…Majumder and Groenevelt [10] assumed that new products and remanufactured products could substitute each other, and explored the optimal decision on product pricing. Sahoo and Chakraverty [11] illustrate the Mexican Hat Wavelet Neural Network with L-BFGS optimization algorithm for simulating the recycling procedure of waste plastic in the Ocean. Cao et al [12] classified consumers according to their preferences for traditional or direct sales channels when studying product price setting and coordination.…”