Harmful algal blooms (HAB) open issues: A review of ecological data challenges, factor analysis and prediction approaches using data-driven method

Nur Aqilah Paskhal Rostam; Nurul Hashimah Ahamed Hassain Malim; Nur Afzalina Azmee; Renato J. Figueiredo; Mohd Azam Osman; Rosni Abdullah

doi:10.59400/cai.v1i1.100

Harmful algal blooms (HAB) open issues: A review of ecological data challenges, factor analysis and prediction approaches using data-driven method

Nur Aqilah Paskhal Rostam School of Computer Sciences Universiti Sains Malaysia
Nurul Hashimah Ahamed Hassain Malim School of Computer Sciences Universiti Sains Malaysia
Nur Afzalina Azmee Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris
Renato J. Figueiredo Department of Electrical and Computer Engineering of the University of Florida
Mohd Azam Osman School of Computer Sciences Universiti Sains Malaysia
Rosni Abdullah School of Computer Sciences Universiti Sains Malaysia

Ariticle ID: 100

661 Views, 237 PDF Downloads

DOI: https://doi.org/10.59400/cai.v1i1.100

Keywords: data-driven prediction method, harmful algal bloom, time series forecasting, machine learning, deep learning

Abstract

Ongoing research on the temporal and spatial distribution of algae ecological data has caused intricacies entailing incomprehensible data, model overfit, and inaccurate algal bloom prediction. Relevant scholars have integrated past historical data with machine learning (ML) and deep learning (DL) approaches to forecast the advent of harmful algal blooms (HAB) following successful data-driven techniques. As potential HAB outbreaks could be predicted through time-series forecasting (TSF) to gauge future events of interest, this research aimed to holistically review field-based complexities, influencing factors, and algal growth prediction trends and analyses with or without the time-series approach. It is deemed pivotal to examine algal growth factors for useful insights into the growth of algal blooms. Multiple open issues concerning indicator types and numbers, feature selection (FS) methods, ML and DL forms, and the time series-DL integration were duly highlighted. This algal growth prediction review corresponded to various (chronologically-sequenced) past studies with the algal ecology domain established as a reference directory. As a valuable resource for beginners to internalize the algae ecological informatics research patterns and scholars to optimize current prediction techniques, this study outlined the (i) aforementioned open issues with an end-to-end (E2E) evaluation process ranging from FS to predictive model performance and (ii) potential alternatives to bridge the literature gaps.

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Published

2023-08-12

How to Cite

Rostam, N. A. P., Ahamed Hassain Malim, N. H., Azmee, N. A., Figueiredo, R. J., Osman, M. A., & Abdullah, R. (2023). Harmful algal blooms (HAB) open issues: A review of ecological data challenges, factor analysis and prediction approaches using data-driven method. Computing and Artificial Intelligence, 1(1), 100. https://doi.org/10.59400/cai.v1i1.100

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This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Application of computer vision in livestock and crop production—A review

Nowadays, it is a challenge for farmers to produce healthier food for the world population and save land resources. Recently, the integration of computer vision technology in field and crop production ushered in a new era of innovation and efficiency. Computer vision, a subfield of artificial intelligence, leverages image and video analysis to extract meaningful information from visual data. In agriculture, this technology is being utilized for tasks ranging from disease detection and yield prediction to animal health monitoring and quality control. By employing various imaging techniques, such as drones, satellites, and specialized cameras, computer vision systems are able to assess the health and growth of crops and livestock with unprecedented accuracy. The review is divided into two parts: Livestock and Crop Production giving the overview of the application of computer vision applications within agriculture, highlighting its role in optimizing farming practices and enhancing agricultural productivity.