U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model

Yin, Lirong; Wang, Lei; Li, T; Lu, Siyu; Yin, Zhengtong; Liu, Xuan; Li, Xiaolu; Zheng, Wenfeng

doi:10.3390/land12081602

Cited by 127 publications

(32 citation statements)

References 33 publications

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“…as for the Sargodha, Rawalpindi, Lahore, and some parts of the Gujranwala, as shown in Figure 4l. The findings are strongly supported by [54,55] for different cities in Punjab. The primary sources of the contamination of the microbes across the study area are municipal effluents, improper solid waste management, and open septic tanks [56].…”

Section: Total Coliformssupporting

confidence: 73%

“…Heavy metal contamination, especially arsenic and iron, is a significant concern in various zones of the study area. Arsenic and iron contamination can occur due to industrial effluents, natural metal deposits [50,74], weathering of metal-bearing rocks, and leachate from landfills [55]. Control measures for heavy metal contamination include:…”

Section: Scenario Ii: Variations In Heavy Parametersmentioning

confidence: 99%

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Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Naz,

Ahmad,

Aslam

et al. 2023

Water

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This study undertook an assessment of 24 physiochemical parameters at over 1094 sites to compute the water quality index (WQI) across the upper and central Punjab regions of Pakistan. Prior to the WQI calculation, an analytical hierarchy process (AHP) was employed to assign specific weights to each water quality parameter. The categorization of WQI into distinct classes was achieved by constructing a pairwise matrix based on their relative importance utilizing Saaty’s scale. Additionally, the groundwater quality status for irrigation and drinking purposes across various zones in the study area was delineated through the integration of WQI and geostatistical methodologies. The findings revealed discernible heavy metal issues in the Lahore division, with emerging microbiological contamination across the entire study region, potentially attributed to untreated industrial effluent discharge and inadequately managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions fell within the marginal to unfit categories, indicating water quality concerns. In contrast, the indices for other divisions were in the medium class, suggesting suitability for drinking purposes. Scenario analysis for developing mitigation strategies indicated that primary treatment before wastewater disposal could rehabilitate 9% of the study area, followed by secondary (35%) and tertiary (41%) treatments. Microbiological contamination (27%) emerged as the predominant challenge for water supply agencies. Given the current trajectory of water quality deterioration, access to potable water is poised to become a significant public concern. Consequently, government agencies are urged to implement appropriate measures to enhance overall groundwater quality for sustainable development.

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Section: Total Coliformssupporting

confidence: 73%

Section: Scenario Ii: Variations In Heavy Parametersmentioning

confidence: 99%

Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Naz,

Ahmad,

Aslam

et al. 2023

Water

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“…Performance assessment is one of the most importance steps in scientific works (Chen et al, 2023;Li & Liu, 2022;Yang et al, 2023;Yin, Wang, Ge, et al, 2023;Yin, Wang, Li, et al, 2023).…”

Section: Validating the Performance Of Machine Learning Modelsmentioning

confidence: 99%

Landslide risk assessment and management using hybrid machine learning‐based empirical models

Yang,

Jiang,

Arabameri

et al. 2023

Geological Journal

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Landslides are a prevalent geologic phenomenon that substantially threatens human life and infrastructure, resulting in considerable loss and destruction. The practice of landslide susceptibility mapping is crucial for the mitigation of risks connected with this natural disaster. This work aims at investigating the influence of varying sample sizes on the precision of landslide susceptibility modelling using a case study conducted in the Alamout basin, Iran. The researchers used a machine learning methodology based on tree algorithms to construct a model for predicting the likelihood of landslides. Additionally, they adopted a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The integration of the naive Bayes tree (NBTree), random forest (RF), logistic model tree (LMT) and J48 algorithms was performed. The modelling process included using 20 predictive parameters across four distinct scenarios. Four models, labelled S1, S2, S3 and S4, were used in this study. These models utilized 25%, 50%, 75% and 100% of the available inventory data. The research presented in this study is distinguished by using a tree‐based methodology for landslide susceptibility modelling and incorporating a multi‐scenario strategy to address the inherent uncertainty associated with the input data. The findings indicated that the augmentation of the sample size improved the precision of the models. The efficacy of using a multi‐scenario strategy in enhancing the dependability of the model is also underscored. Among the 20 input elements used in the modelling process, it was seen that slope angle accounted for the highest relative significance, constituting 25.60% of the overall influence. Following more closely, distance to fault contributed significantly, with a relative importance of 23.40%. Additionally, rainfall and elevation exhibited notable contributions, with relative volumes of 7.91% and 5.50%, respectively. All four landslide models showed adequate learning and forecasting ability throughout the training and testing phases. During the testing phase, the true skill score (TSS) values exhibited a range of 0.631–0.804, while the area under the receiver operating characteristic curve values showed a range of 0.745–0.921. The susceptibility maps indicated that a significant portion of the region exhibits moderate to very high susceptibility zones, with the northern and eastern sectors displaying greater landslide values than the western region. The model's performance showed improvement from S1 to S4 in both the training and testing phases. The performance of the models exhibited the following trend: in scenario 1, the RF model outperformed the J48, LMT and NBTree models; in scenario 2, the RF model surpassed the NBTree and LMT models, while being on par with the J48 model; in scenarios 3 and 4, the RF model showed superior performance compared to the NBTree, J48 and LMT models. Therefore, the RF model proved to be the most effective among the models evaluated. The findings derived from this research have the potential to serve as valuable references for the purposes of land‐use planning and catastrophe risk management.

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“…With the development of machine learning, nonparametric and nonlinear models represented by random forests (Shen et al, 2020), support vector machines (SVM) (Sheng et al, 2021), and neural networks (Zhu et al, 2017;Pahlevan et al, 2020) have the characteristics of broad applicability, high accuracy, and strong reliability and are favored by many scholars. Based on measured hyperspectral data, Liu manually selected sensitive bands and used SVM models to invert the surface TNC of rivers in arid areas (Liu et al, 2020) and so on (Dong et al, 2023;Yin et al, 2023a;Yin et al, 2023b;Wen et al, 2024). Amiri and Liu fitted the distribution of TNC in the study area using artificial neural networks and multiple linear regression methods (Amiri and Nakane, 2009;Liu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Utilizing residual networks for remote sensing estimation of total nitrogen concentration in Shandong offshore areas

Zheng,

Wu,

Han

et al. 2024

Front. Mar. Sci.

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Nitrogen is one of the critical factors in water pollution and eutrophication, so applying the deep learning method in remote sensing inversion of nitrogen can provide basic information for environmental management. This paper proposes a two-step feature extraction method to solve the problem that the number of bands in water quality inversion is insufficient and the deep learning method cannot be fully exploited. Firstly, manual feature extraction is completed through the fusion between bands to obtain a set of high-latitude shallow factors, which make the features rich and diverse. Then, a one-dimensional convolutional residual network (ResNet-1D) is constructed, and the deep features are automatically extracted through convolution operations of the model, where the residual learning is used to reduce the training difficulty. The full connection is established through depth features. The comparison of models shows that the Mean Relative Error (MRE) is decreased by at least 10% in both test and validation datasets. Finally, the spatiotemporal distribution of total nitrogen concentration (TNC) in the coastal waters of Shandong is explored. In general, the spatial distribution is that the concentration near the coast is higher than the far. The temporal variation is that the monthly mean of the TNC is low in March, moderate in May and August, and high in October; the annual average value of TNC is 0.3mg/L, which has decreased slightly year by year since 2014.

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U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model

Cited by 127 publications

References 33 publications

Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices

Landslide risk assessment and management using hybrid machine learning‐based empirical models

Utilizing residual networks for remote sensing estimation of total nitrogen concentration in Shandong offshore areas

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