The imitation of natural systems to produce effective antifouling materials is often referred to as “biomimetics”. The world of biomimetics is a multidisciplinary one, needing careful understanding of “biological structures”, processes and principles of various organisms found in nature and based on this, designing nanodevices and nanomaterials that are of commercial interest to industry. Looking to the marine environment for bioinspired surfaces offers researchers a wealth of topographies to explore. Particular attention has been given to the evaluation of textures based on marine organisms tested in either the laboratory or the field. The findings of the review relate to the numbers of studies on textured surfaces demonstrating antifouling potential which are significant. However, many of these are only tested in the laboratory, where it is acknowledged a very different response to fouling is observed.
<p>In the Artificial intelligence (AI) sense, meta-learning is the ability of an artificially intelligent machine first to learn how to conduct different complex tasks, taking the principles it utilised to learn one task and applying them to other different tasks. Hence, the general concept of "learning how to learn". Machine learning provides capabilities to learn from past data and generates models for future prediction, which can be helpful for multiple catchment management tasks, such as water elevation monitoring and flood prediction.</p> <p>Our initial studies focused on predicting and evaluating the ML-based hydrologic time-series models based on their predictive performance. We used eight machine learning algorithms to predict river water levels, including Baseline, Linear, Dense, MultiDense, CNN, RNN, GRU and LSTM techniques. The eight models were employed for one hour ahead of river water level forecasting in 70 hydrometric stations in Ireland.&#160;The results show that the NN-based models generally performed well in predicting the water level, with some differences in each model's performance for different stations. These results suggest that a single machine learning model may be sufficient for forecasting river water levels in one location and perform poorly in another. Hence, there is no overall best model; and the selected model may significantly impact the desired results.</p> <p>This study's main goal was to investigate a meta-learning-based approach for water level prediction. The proposed Meta-learning approach comprises two phases; Learning and meta-learning. The meta-learning process uses the outcomes of the previous experiments to accomplish the Learning Training and Practising phases of the meta-learner. Later the outcome of the previous step will be the Databases to create the learner (learning about learning phase).&#160;</p> <p>Creating meta-learning models can help AI models to generalise learning methods and acquire new skills more quickly. We expect the meta-learning model to adjust well when generalising to previously unknown datasets and environments that have never been encountered during training.</p> <p>Keywords: Machine learning (ML), meta-learning,&#160; water-level prediction,&#160; hydrologic time-series forecasting.</p>
<p>Advanced technologies have proven to deliver significant outcomes in the water management sector. New technologies provide the capability to collect and correlate the information from remote devices, introducing smart tools that can leverage augmented intelligence for interpreting structured and unstructured, text-based or sensory data. However, most of the single feature or non-sequential prediction machine learning methods for understanding water quality achieve poor results due to the fact that water quality information exists in the form of multivariate time-series datasets.</p><p>At the catchment scale, there are many layers where relevant data needs to be measured and captured. For that, data warehouses play an essential role in decision support systems as they provide adequate information.&#160;</p><p>In this paper, we started by extracting, transforming, cleaning and consolidating data from several data sources into a data warehouse. Then, the data in the warehouse was used to develop a computer tool to predict river water level using Artificial Neural Networks (ANNs), in particular, Long Short-Term Memory networks (LSTM). As the prediction performance is significantly affected by the model inputs, the feature selection step, which considers the multivariate correlation of water quality information in terms of similarity and proximity, is particularly important. The features obtained from the previous steps are the inputs to the prediction model based on LSTM, which naturally takes the time sequence of water quality information into account.</p><p>The proposed method is applied to two different catchments in the island of Ireland. Experimental results indicate that our model provides accurate predictions for water levels and is a useful supportive tool for water quality management.&#160;</p><p>Ultimately, digitised representations of water environments will guarantee situational awareness of water flow and quality monitoring. The digitalisation of water is no longer optional but a necessity to solve many of the challenges faced by the water industry.</p><p><br><strong>Keywords:</strong> Water digitalisation, water quality, data warehouse, machine learning, predictive model, LSTM.</p><p><br><br></p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.