Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management

Latif, Sarmad Dashti; Ahmed, Ali Najah

doi:10.1007/s11269-023-03499-9

Cited by 22 publications

(2 citation statements)

References 27 publications

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“…The rapid and accurate mapping of water bodies, which are essential for the sustainability of the ecosystems and welfare of human life, is of high importance for land, water, and biodiversity management at local and global scales [8,10]. The detailed mapping and understanding of water bodies, including lakes, rivers, and wetlands, are crucial for various aspects of hydrology and water resource management [6,[9][10][11][12][13]. Current techniques using the SAM, several GitHub repositories have made significant contributions, showcasing the practical implementations and enhancements of the SAM in various applications.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Ozdemir,

Akbulut,

Karsli

et al. 2024

Sustainability

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Water, indispensable for life and central to ecosystems, human activities, and climate dynamics, requires rapid and accurate monitoring. This is vital for sustaining ecosystems, enhancing human welfare, and effectively managing land, water, and biodiversity on both the local and global level. In the rapidly evolving domain of remote sensing and deep learning, this study focuses on water body extraction and classification through the use of recent deep learning models of visual foundation models (VFMs). Specifically, the Segment Anything Model (SAM) and Contrastive Language-Image Pre-training (CLIP) models have shown promise in semantic segmentation, dataset creation, change detection, and instance segmentation tasks. A novel two-step approach involving segmenting images via the Automatic Mask Generator method of the SAM and the zero-shot classification of segments using CLIP is proposed, and its effectiveness is tested on water body extraction problems. The proposed methodology was applied to both remote sensing imagery acquired from LANDSAT 8 OLI and very high-resolution aerial imagery. Results revealed that the proposed methodology accurately delineated water bodies across complex environmental conditions, achieving a mean intersection over union (IoU) of 94.41% and an F1 score of 96.97% for satellite imagery. Similarly, for the aerial imagery dataset, the proposed methodology achieved a mean IoU of 90.83% and an F1 score exceeding 94.56%. The high accuracy achieved in selecting segments predominantly classified as water highlights the effectiveness of the proposed model in intricate environmental image analysis.

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Section: Introductionmentioning

confidence: 99%

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Ozdemir,

Akbulut,

Karsli

et al. 2024

Sustainability

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show abstract

“…Streamflow prediction is of critical importance for water resources management in the Western United States (Pagano et al., 2004), informing municipal water supply planning (Anghileri et al., 2016; Chiew et al., 2003; Latif & Ahmed, 2023), natural hazard planning and mitigation (Alfieri et al., 2013; Badrzadeh et al., 2015; Zhang et al., 2016), water quality management (Chien & Mackay, 2014; Guo et al., 2020; Kurunç et al., 2005), and aquatic ecosystem management (Huang et al., 2011; King et al., 2023; Nelson et al., 2009). However, high intra‐annual and interannual streamflow variability makes streamflow prediction challenging, in addition to causing substantial economic, ecological, and public health impacts (Christian‐Smith et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

Streamflow Prediction at the Intersection of Physics and Machine Learning: A Case Study of Two Mediterranean‐Climate Watersheds

Adera,

Bellugi,

Dhakal

et al. 2024

Water Resources Research

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Accurate streamflow predictions are essential for water resources management. Recent studies have examined the use of hybrid models that integrate machine learning models with process‐based (PB) hydrologic models to improve streamflow predictions. Yet, there are many open questions regarding optimal hybrid model construction, especially in Mediterranean‐climate watersheds that experience pronounced wet and dry seasons. In this study, we performed model benchmarking to (a) compare hybrid model performance to PB and machine learning models and (b) examine the sensitivity of hybrid model performance to PB model parameter calibration, structural complexity, and variable selection. Hybrid models were generated by post‐processing process‐based models using Long Short‐Term Memory neural networks. Models were benchmarked within two northern California watersheds that are managed for both municipal water supplies and aquatic habitat. Though model performance varied substantially by watershed and error metric, calibrated hybrid models frequently outperformed both the machine learning model (for 72% of watershed‐model‐metric combinations) and the calibrated process‐based models (for 79% of combinations). Furthermore, hybrid models were relatively insensitive to PB model calibration and structural complexity, but sensitive to PB model variable selection. Our results demonstrate that hybrid models can improve streamflow prediction in Mediterranean‐climate watersheds. Additionally, hybrid model insensitivity to PB model parameter calibration and structural complexity suggests that uncalibrated or less complex PB models could be used in hybrid models without any loss of streamflow prediction accuracy, improving model construction efficiency. Moreover, hybrid model sensitivity to the selection of PB model variables suggests a strategy for diagnosing poorly performing PB model components.

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Computational Optimization of Water Resources Management Through Evolutionary Computing: An Approach Based on the Transportation Problem

Calzada-Ledesma,

Zúñiga-Núñez,

Cornejo-Acosta

2024

Studies in Computational Intelligence

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Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management

Cited by 22 publications

References 27 publications

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Extraction of Water Bodies from High-Resolution Aerial and Satellite Images Using Visual Foundation Models

Streamflow Prediction at the Intersection of Physics and Machine Learning: A Case Study of Two Mediterranean‐Climate Watersheds

Computational Optimization of Water Resources Management Through Evolutionary Computing: An Approach Based on the Transportation Problem

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