Deep learning detection of types of water-bodies using optical variables and ensembling

Nasir, Nida; Kansal, Afreen; Alshaltone, Omar; Barneih, Feras; Shanableh, Abdallah; Al-Shabi, Mohammad; Shammaa, Ahmed Al

doi:10.1016/j.iswa.2023.200222

Cited by 11 publications

(7 citation statements)

References 46 publications

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“…Knaeps et al [148] compare algorithms across different water quality classes, revealing performance variations based on water quality class. Nasir et al [149] investigate classifier performance across water classes, noting suboptimal performance for the stream class. The aforementioned studies collectively validate the hypothesized factors that have the potential to influence predictive models in the assessment of water quality based on satellite data.…”

Section: Factors Influencing Model Performance In Satellite-based Wat...mentioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

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Section: Factors Influencing Model Performance In Satellite-based Wat...mentioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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“…A seleção de um índice de água é de crucial importância para a extração de corpos hídricos por sensoriamento remoto. Neste trabalho, além das polarizações VV e VH, o corpo hídrico também foi extraído usando o índice Sentinel-1 Dual-Polarized Water Index (SDWI) (Nasir et al 2023). Dedicado às bandas duplamente polarizadas do Sentinel-1, esse índice pode efetivamente distinguir a diferença entre água e outros objetos em bandas de onda duplamente polarizadas e pode aprimorar as informações dos corpos d'água de superfície e eliminar perturbações de outros tipos de superfície, como vegetação e solo.…”

Section: Dual-polarized Water Index (Sdwi)unclassified

“…Os métodos atuais de extração de informações sobre água incluem os métodos de limite (Guo et al, 2022), aprendizado de máquina (Rajendiran;Kumar, 2022) e aprendizado profundo (Li et al, 2021), entre outros. O aprendizado profundo é um método popular no processamento de imagens que vem sendo muito usado nos últimos anos (Nasir et al, 2023). As Redes Neurais Artificais (RNAs) têm sido usadas na classificação de cenas (Tamiru;Dinka, 2021), e ainda são utilizados a segmentação semântica (Pinheiro, 2023) e a detecção de objetos (Guo et al, 2022).…”

Section: Introductionunclassified

Extração de Corpo Hídrico Usando uma Cena Sentinel-1 e Redes Neurais Artificiais: Estudo de Caso – Barragem de Carpina-PE

Silva Júnior,

Silva Junior

2023

S&N

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A água de superfície é o recurso e fator ambiental mais importante para manter a sobrevivência humana e a estabilidade dos ecossistemas, portanto, informações precisas e oportunas sobre águas superficiais são urgentemente necessárias. Neste estudo foi proposto uma abordagem de classificação de imagens por Redes Neurais Artificiais para o mapeamento da extensão da água de superfície da Barragem de Carpina-PE usando dados (SAR) do satélite Sentinel-1, bem como suas polarizações (VH e VV) e os índices polarimétricos de água gerados (SDWI e SWI). Todos os conjuntos de dados apresentaram limitações na detecção de pequenos corpos hídricos, como rios estreitos, e superestimação em áreas de pastagem, gerando erros de comissão variando de 16,5 % a 28,9% e erros de omissão variando entre 1,47% e 3,5%, com destaque para as polarizações VH e VV. A precisão geral da classificação variou de 96% a 98% e valores de R² chegaram próximo de 1, onde o melhor desempenho foi visto para o SDWI e o SWI. Os experimentos comparativos indicaram que, as polarizações unitárias de radar com índices polarimétricos de água, foram úteis para melhorar a precisão da extração de corpos hídricos em locais com muitas nuvens, sem variações significativas, além de fornecer informações detalhadas, com potencial de monitoramento contínuo.

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“…Chlorophyll-a detection algorithms using combined machine learning and remote sensing techniques are new advanced tools used to assess the presence and concentration of chlorophyll-a in water bodies [24][25][26]. Machine learning consists of training a model with previously labeled sample data, which allows the algorithm to learn to recognize patterns and make accurate predictions about chlorophyll-a concentration in new images [27][28][29]. The combination of machine learning and remote sensing techniques allows for improved accuracy of chlorophyll-a detection compared to traditional approaches [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake

Rodríguez-López,

Alvarez,

Bustos Usta

et al. 2024

Remote Sensing

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In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.

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Deep learning detection of types of water-bodies using optical variables and ensembling

Cited by 11 publications

References 46 publications

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Extração de Corpo Hídrico Usando uma Cena Sentinel-1 e Redes Neurais Artificiais: Estudo de Caso – Barragem de Carpina-PE

Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake

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