Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Wong, Wen Yee; Al-Ani, Ayman; Hasikin‬, Khairunnisa; Khairuddin, Anis Salwa Mohd; Razak, Sarah Abdul; Hizaddin, Hanee F.; Mokhtar, Mohd Istajib; Azizan, Muhammad Mokhzaini

doi:10.1109/access.2021.3099107

Cited by 18 publications

(9 citation statements)

References 186 publications

(207 reference statements)

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“…These unique ecosystems are home to a diverse variety of flora and fauna, with many species exhibiting specialized adaptations to survive in the challenging conditions of the intertidal zone [5]. Mangroves are vital habitats for many marine organisms, fish, crabs, and other aquatic creatures [6]. In addition, the complex root systems of mangroves play a crucial role in stabilizing coastal sediments, mitigating the effects of erosion and storm surges [7].…”

Section: Introductionmentioning

confidence: 99%

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Panuntun,

Jamaluddin,

Chen

et al. 2024

Remote Sensing

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Mangroves grow in intertidal zones in tropical and subtropical regions, offering numerous advantages to humans and ecosystems. Mangrove monitoring is one of the important tasks to understand the current status of mangrove forests regarding their loss issues, including deforestation and degradation. Currently, satellite imagery is widely employed to monitor mangrove ecosystems. Sentinel-2 is an optical satellite imagery whose data are available for free, and which provides satellite imagery at a 5-day temporal resolution. Analyzing satellite images before and after loss can enhance our ability to detect mangrove loss. This paper introduces a LSST-Former model that considers the situation before and after mangrove loss to categorize non-mangrove areas, intact mangroves, and mangrove loss categories using Sentinel-2 images for a limited number of labels. The LSST-Former model was developed by integrating a fully convolutional network (FCN) and a transformer base with few-shot learning algorithms to extract information from spectral-spatial-temporal Sentinel-2 images. The attention mechanism in the transformer algorithm may effectively mitigate the issue of limited labeled samples and enhance the accuracy of learning correlations between samples, resulting in more successful classification. The experimental findings demonstrate that the LSST-Former model achieves an overall accuracy of 99.59% and an Intersection-over-Union (IoU) score of 98.84% for detecting mangrove loss, and the validation of universal applicability achieves an overall accuracy of more than 92% and a kappa accuracy of more than 89%. LSST-Former demonstrates superior performance compared to state-of-the-art deep-learning models such as random forest, Support Vector Machine, U-Net, LinkNet, Vision Transformer, SpectralFormer, MDPrePost-Net, and SST-Former, as evidenced by the experimental results and accuracy metrics.

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Section: Introductionmentioning

confidence: 99%

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Panuntun,

Jamaluddin,

Chen

et al. 2024

Remote Sensing

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“…In the realm of physicochemical properties, numerous parameters come into play, including pH, Electrical Conductivity (EC), TDS, chloride levels, turbidity, phosphate content, hardness, nitrate concentrations, and more [12,13]. Evaluating the suitability of drinking water involves comparing these parameters to established standards, as delineated by the Environmental Protection Agency (EPA) and World Health Organization (WHO) [14,15]. These standards serve as crucial benchmarks for determining the acceptability and safety of drinking water quality.…”

Section: Introductionmentioning

confidence: 99%

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Ramesh,

Vanitha

2024

Water

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The assessment of water quality assumes a position of utmost significance as it plays a critical role in upholding ecological balance and safeguarding the well-being of human populations. To achieve these goals, an in-depth consideration of water quality trends is essential, as it offers comprehension into the intricate interplay between various elements within aquatic ecosystems. As a consequence, the proposed work investigates the water quality trends specifically within the Valliyar sub-basin, which encompasses the geographical areas of Kattathurai, Colachal, Thuckalay, and Villukuri. The temporal scope of investigation spans from the year 2000 to 2018 using the dependent variable of water quality parameters with dependent variables of climate data. Recognizing the need for advanced methodologies to tackle the multifaceted nature of water quality dynamics, this research harnesses the power of pioneering machine learning techniques. Two notable approaches, the Radial Bias Function Neural Network (RBFNN) and the DenseNet-121-based Convolutional Neural Network (CNN), are brought into performance. The primary objective is to leverage these techniques to forecast water quality trends for the next twenty-two years. The effectiveness of various machine learning models in predicting water quality is evaluated using the following key performance metrics: the Mean-Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE). Notably, the DenseNet CNN model exhibits accurate prediction among the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning (DL) models. This research underscores the significance of machine learning techniques, with DenseNet CNN model emerging as a particularly promising tool in this domain.

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“…Marine waste poses severe and far-reaching threats to marine life. Of particular concern is the pervasive distribution of microplastics in oceans, presenting an insidious danger to marine ecosystems and the delicate balance of our planet [1][2][3]. For example, the sudden increase in ocean plastic waste poses multifaceted threats, i.e., imperiling marine ecosystems, human health, crucial ecosystem services, and so forth [4].…”

Section: Introductionmentioning

confidence: 99%

An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques

Nunkhaw,

Miyamoto

2024

Water

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Plastic pollution in the ocean is a severe environmental problem worldwide because rivers carry plastic waste from human activities, harming the ocean’s health, ecosystems, and people. Therefore, monitoring the amount of plastic waste flowing from rivers and streams worldwide is crucial. In response to this issue of river-floating waste, our present research aimed to develop an automated waste measurement method tailored for real rivers. To achieve this, we considered three scenarios: clear visibility, partially submerged waste, and collective mass. We proposed the use of object detection and tracking techniques based on deep learning architectures, specifically the You Only Look Once (YOLOv5) and Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT). The types of waste classified in this research included cans, cartons, plastic bottles, foams, glasses, papers, and plastics in laboratory flume experiments. Our results demonstrated that the refined YOLOv5, when applied to river-floating waste images, achieved high classification accuracy, with 88% or more for the mean average precision. The floating waste tracking using DeepSORT also attained F1 scores high enough for accurate waste counting. Furthermore, we evaluated the proposed method across the three different scenarios, each achieving an 80% accuracy rate, suggesting its potential applicability in real river environments. These results strongly support the effectiveness of our proposed method, leveraging the two deep learning architectures for detecting and tracking river-floating waste with high accuracy.

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Water, Soil and Air Pollutants’ Interaction on Mangrove Ecosystem and Corresponding Artificial Intelligence Techniques Used in Decision Support Systems - A Review

Cited by 18 publications

References 186 publications

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

An Image Analysis of River-Floating Waste Materials by Using Deep Learning Techniques

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