Influencing Factors Analysis of Water Footprint Based on the Extended STIRPAT Model

Xu, Meng; Li, Chunhui

doi:10.1007/978-981-15-0234-7_10

Cited by 7 publications

(2 citation statements)

References 52 publications

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“…According to results of this study (threshold must be < 0.70), slope angle and lithology had higher coefficient value (> 0.70), therefore, these features were regarded as redundant and excluded from algorithm training. The studies by (Chen et al, 2020;Hong et al, 2018;Miles, 2014;Xu and Li, 2020) reported a similar observation and also eliminated the redundant features.…”

Section: Discussionsupporting

confidence: 53%

How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

et al. 2022

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With climate change, hydro-climatic hazards, i.e., floods in the Himalayas regions, are expected to worsen, thus, likely to affect humans and socio-economic growth. Precisely, the Koshi River basin (KRB) is often impacted by flooding over the year. However, studies on estimating and predicting floods still lack in this basin.This study aims at developing flood probability map using machine learning algorithms (MLAs): gaussian process regression (GPR) and support vector machine (SVM) with multiple kernel functions including Pearson VII function kernel (PUK), polynomial, normalized poly kernel, and radial basis kernel function (RBF). Historical flood locations with available topography, hydrogeology, and environmental datasets were further considered to build flood model.Two datasets were carefully chosen to measure the feasibility and robustness of MLAs: training dataset (location of floods between 2010 and 2019) and testing dataset (flood locations of 2020) with thirteen flood influencing factors. 2The validation of the MLAs was evaluated using a validation dataset and statistical indices such as the coefficient of determination (r 2 : 0.546~0.995), mean absolute error (MAE: 0.009~0.373), root mean square error (RMSE: 0.051~0.466), relative absolute error (RAE: 1.81~88.55%), and root-relative square error (RRSE: 10.19~91.00%).Results showed that the SVM-Pearson VII kernel (PUK) yielded better prediction than other algorithms. The resultant map from SVM-PUK revealed that 27.99% area with low, 39.91% area with medium, 31.00% with high, and 1.10% area with very high probabilities of flooding in the study area. The final flood probability map could add a greatt value to the effort of flood risk mitigation and planning processes in KRB.

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Section: Discussionsupporting

confidence: 53%

How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

et al. 2022

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“…The stochastic impacts by regression on population, affluence, and technology (STIR-PAT) model, which is derived from the IPAT (Impact, Population, Affluence, and Technology) equation, has been widely used to analyze the influence factors of CO 2 emission [41][42][43], energy consumption [44][45][46], and water ecological footprint WEF [47][48][49]. The original version of the STIRPAT model can be expressed as follows:…”

Section: Model Establishmentmentioning

confidence: 99%

Urban Spatial Structure and Water Ecological Footprint: Empirical Analysis of the Urban Agglomerations in China

Liu

Ri-zhao

et al. 2022

Sustainability

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Focusing on the water conservation of China’s urban agglomerations (UAs), panel data covering 92 cities in the top five agglomerations from 2006 to 2020 are used to study the relationship between the spatial structure of UAs and the water ecological footprint (WEF) of their cities. WEFs and spatial structures are measured by the ecological footprint models and the rank-size law, respectively. Furthermore, the effects of spatial structure on WEF are estimated through the fixed-effects (FE) model with instrumental variables (IVs). Results suggest that the concentricity of the spatial structure has a nonlinear impact on the WEF, in that as the spatial structure moves away from polycentricity, the WEF first declines and then rises. By reducing the WEF through concentrated development, cities with a large proportion of production WEF or a large population can enjoy more benefits. Therefore, promoting the balanced development of JJJ and PRD and enhancing the role of the growth pole in CY and YRMR can help the water conservation of most cities. Moreover, considering household water use and small-population cities in other water-saving policies can serve as a policy reference in the future.

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Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

Talukdar

Ghose

Shahfahad

et al. 2020

Stoch Environ Res Risk Assess

156

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Influencing Factors Analysis of Water Footprint Based on the Extended STIRPAT Model

Cited by 7 publications

References 52 publications

How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

How do multiple kernel functions in machine learning algorithms improve precision in flood probability mapping?

Urban Spatial Structure and Water Ecological Footprint: Empirical Analysis of the Urban Agglomerations in China

Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

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