Modified Principal Component Analysis for Identifying Key Environmental Indicators and Application to a Large-Scale Tidal Flat Reclamation

Chu, Kejian; Liu, Wenjuan; She, Yuntong; Hua, Zulin; Tan, Min; Liu, Xiaodong; Li, Gu; Jia, Yi

doi:10.3390/w10010069

Cited by 19 publications

(8 citation statements)

References 41 publications

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“…Pearson correlation among the predictor variables was checked to avoid unusual spatial collinearity. In case of a high correlation value of coefficient r =|0.7|, variables were processed for principal component analysis; otherwise, provided variables were retained (Chu et al 2018).…”

Section: Variable Identificationmentioning

confidence: 99%

Hunting of hunted: an ensemble modeling approach to evaluate suitable habitats for caracals in India

et al. 2022

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Background Large-scale hunting and various anthropogenic pressures in the recent past have pushed the Asiatic caracal (Caracal caracal schmitzi), an elusive medium-sized and locally threatened felid species towards local extinction in India. Though widely distributed historically, it has been sparsely reported from several regions of central and northern states in India till twentieth century. Later, the species distribution became confined only to the states of Rajasthan, Gujarat and Madhya Pradesh, which have had reported sightings in the twenty-first century. In order to highlight the potentially suitable habitats for Asiatic caracals in India, we targeted forth-filtering of the spatial model ensemble by creating and utilizing the validated and spatially thinned species presence information (n = 69) and related ecological variables (aridity, NDVI, precipitation seasonality, temperature seasonality, terrain ruggedness), filtered with anthropological variable (nightlight). Results Out of eight spatial prediction models, the two most parsimonious models, Random Forest (AUC 0.91) and MaxEnt (AUC 0.89) were weighted and ensembled. The ensemble model indicated several clustered habitats, covering 1207.83 km2 areas in Kachchh (Gujarat), Aravalli mountains (Rajasthan), Malwa plateau (Rajasthan and Madhya Pradesh), and Bundelkhand region (Madhya Pradesh) as potentially suitable habitats for caracals. Output probabilities of pixels were further regressed with converted vegetation height data within selected highly potential habitats, i.e., Ranthambore Kuno Landscape (RKL) (suitability ~ 0.44 + 0.03(vegetation height) **, R2 = 0.27). The regression model inferred a significant positive relation between vegetation height and habitat suitability, hence the lowest ordinal class out of three classes of converted vegetation height was masked out from the RKL, which yielded in an area of 567 km2 as potentially highly suitable habitats for caracals, which can be further proposed as survey areas and conservation priority areas for caracals. Conclusion The study charts out the small pockets of landscape in and around dryland protected areas, suitable for caracal in the Indian context, which need attention for landscape conservation.

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Section: Variable Identificationmentioning

confidence: 99%

Hunting of hunted: an ensemble modeling approach to evaluate suitable habitats for caracals in India

et al. 2022

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“…In 2010, Chau et al [18] applied modular artificial neural networks technique to predict the rainfall time series. In 2018, Chu et al [31] proposes a modified principal component analysis (MPCA) method for assessing environmental variables to track environmental changes in coastal recovery ares. Ghadim et al [32] discuss the use of the Holt-Winters time series model's additive and multiplicative types of to forecast environmental variables for one year in advance.…”

Section: Meterological Data Analyticsmentioning

confidence: 99%

Energy efficient IoT-based cloud framework for early flood prediction

Kaur

Sood

2021

Nat Hazards

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Flood is a recurrent and crucial natural phenomenon affecting almost the entire planet. It is a critical problem that causes crop destruction, destruction to the population, loss of infrastructure, and demolition of several public utilities. An effective way to deal with this is to alert the community from incoming inundation and provide ample time to evacuate and protect property. In this article, we suggest an IoT-based energy efficient flood prediction and forecasting system. IoT sensor nodes are constrained in terms of battery and memory, so the fog layer uses an energy-saving approach based on data heterogeneity to preserve the system's power consumption. cloud storage is used for efficient storage. The environmental conditions such as temperature, humidity, rainfall, and water body parameters, i.e. water flow and water level, are being investigated for India's Kerala region to calibrate the flood phases. PCA (Principal Component Analysis) approach is used at the fog layer for attribute dimensionality reduction. To forecast the flood, the ANN (Artificial Neural Network) algorithm is used, and the simulation technique of Holt Winter is used to forecast the future flood. Data is obtained from the Indian government meteorological database and experimental assessment is carried out. The findings showed the feasibility of the proposed architecture.

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“…Thus, the identification of parameters that allow a WQI to be determined for High Andean basins can be established through the application of this methodology. In comparison to multivariate methods, which allow the identification of water-quality parameters, which result just from the statistical decision [44][45][46]. However, the Delphi method collects the expert experience in water quality, for specific uses, who include within the selection criteria, the perception of the water body and its surroundings [38,43,[47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Proposal of a Water-Quality Index for High Andean Basins: Application to the Chumbao River, Andahuaylas, Peru

Choque-Quispe

Froehner

Palomino-Rincón

et al. 2022

Water

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The water from the high Andean rivers is peculiar due to its composition and the geomorphology of its sources, and naturally or anthropogenically contamination is not discarded along its course. This water is used for agriculture and human consumption, therefore knowing its quality is important. This research aimed to proposing and formulate a water-quality index for high Andean basins through the Delphi method, and its application in the Chumbao River located in Andahuaylas-Peru. Forty-three water-quality parameters were evaluated through the Delphi method, and the water-quality index (WQIHA) was formulated with a weighted average of the weights of the selected parameters, it was compared with the WQI Dinius. For this purpose, ten sampling points were considered along the Chumbao River located between 4274 and 2572 m of altitude and the WQIHA was applied. In addition, field and laboratory analyses were carried out in 2018, 2019, and 2021, in dry and rainy seasons. Twenty parameters were grouped in the physicochemical sub-index (SIPC), heavy metals sub-index (SIHM), and organic matter sub-index (SIOM). Each group contributed with weights of 0.30, 0.30, and 0.40, respectively, for the WQIHA formulation. The SIPC and SIOM showed that the areas near the head of the basin presented excellent and good quality, while the urbanized areas were qualified as marginal to poor; SIHM reported good quality in all points and seasons. Regarding the WQIHA, the index shows good quality in the zones above 3184 m of altitude, contrasting with poor quality downstream, decreasing notably in both seasons, suggesting continuous degradation of the water body.

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Modified Principal Component Analysis for Identifying Key Environmental Indicators and Application to a Large-Scale Tidal Flat Reclamation

Cited by 19 publications

References 41 publications

Hunting of hunted: an ensemble modeling approach to evaluate suitable habitats for caracals in India

Hunting of hunted: an ensemble modeling approach to evaluate suitable habitats for caracals in India

Energy efficient IoT-based cloud framework for early flood prediction

Proposal of a Water-Quality Index for High Andean Basins: Application to the Chumbao River, Andahuaylas, Peru

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