Correction: Gu et al. Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine. Land 2021, 10, 173

Gu, Changjun; Zhang, Yili; Liu, Linshan; Li, Lanhui; Li, Shicheng; Zhang, Binghua; Cui, Bohao; Kumar, Mohan

doi:10.3390/land10050506

Cited by 2 publications

(5 citation statements)

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“…In ESV coefficient, showed an increasing trend of ESV from 2010 to 2020 due to an increase in forest cover through the agriculture area decreases by 1.99%, which implies that ESV is mostly dependent upon the forest because it provides a wide variety and high-value ecosystem services. The other studies also revealed a decrease in ESV due to a decrease in forest cover (Sharma et al, 2019) (Gu et al, 2021).…”

Section: Results and Discussion Land Use Land Cover Change Analysismentioning

confidence: 88%

Impact of Land cover Dynamics on Ecosystem services value of Siwalik range of Madhesh Province Nepal

Shah¹,

Gautam²,

Dhakal³

et al. 2022

IJAAS

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The Siwalik region is the area that lies between the hills in the north and Terai in the south. It provides a wide range of services and is of particular interest from a land use land cover (LULC) change perspective as it is naturally fragile and prone to degradation. The study was conducted in Madhesh Province Siwalik to assess the LULC change over three time periods and to quantify the change in ecosystem services value (ESV) due to LULC change. Remote sensing, Google Earth Engine and Arc GIS were used to prepare the land cover map. In 2000, 2010 and 2020 total area covered by the forest was 68.46%, 65.58% and 71.17%, agriculture was 16.50%, 18.21% and 16.21% and waterbodies were 4.40%, 3.89% and 3.79% respectively. From 2000-2010 forest and waterbodies decreased by 3.87%, and 0.54% respectively whereas agriculture and other land increased by 1.7% and 2.71% respectively. Similarly, from 2010 to 2020, agriculture, water bodies, and other land decreased by 1.99%, 0.10%, and 4.48% whereas forests increased by 6.58% respectively. The overall accuracy of the map is 90%, 89% and 88% for the years 2000, 2010 and 2020. The ESV was estimated using the value transfer method, which was 28 million USD/year, 26.84 million USD/year, and 28.97 million USD/year in 2000, 2010 and 2020, which showed that the total ESV has decreased by 1.16 and increased by 2.12 million USD from 2000-2020. An elasticity indicator shows 1% of land conversion in Siwalik resulted in 0.47 % and 0.21 % changes in ESVs during the two periods, respectively. Overall findings of this study, suggest that ESV has increased in the Siwalik region of Madhesh province in the last two decades, primarily due to an increase in forest cover. This increase should be taken as an opportunity to leverage policy support and programmatic implementation to increase forest cover and reduce land conversion

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Section: Results and Discussion Land Use Land Cover Change Analysismentioning

confidence: 88%

Impact of Land cover Dynamics on Ecosystem services value of Siwalik range of Madhesh Province Nepal

Shah¹,

Gautam²,

Dhakal³

et al. 2022

IJAAS

View full text Add to dashboard Cite

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“…The differential evolution algorithm (DEA) is an optimization algorithm used to find an optimal solution to a problem by simulating the biological evolution process . This method is based on the natural processes of natural selection and evolution.…”

Section: Methodsmentioning

confidence: 99%

“…Otherwise, go back to step 2 and continue until the stopping criterion is met. The stopping criterion is generally the maximum number of iterations and/or when the population objective function no longer changes Table shows the parameters used for the DEA.…”

Section: Methodsmentioning

confidence: 99%

“…where: The differential evolution algorithm (DEA) is an optimization algorithm used to find an optimal solution to a problem by simulating the biological evolution process. 27 This method is based on the natural processes of natural selection and evolution. The processes of mutation, crossover, and selection take place during the process of optimization to evolve a population into a new generation until certain fitness criteria or the maximum number of iterations is achieved.…”

Section: Proposed Machine-learning-based Optimizationmentioning

confidence: 99%

“…The processes of mutation, crossover, and selection take place during the process of optimization to evolve a population into a new generation until certain fitness criteria or the maximum number of iterations is achieved. As a result of its great global convergence, robustness, and ability to solve optimization problems not solvable with conventional mathematical programming methods, 27 the DEA is chosen for this problem.…”

Section: Proposed Machine-learning-based Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning-Based Modeling and Optimization Analysis for an Integrated Industrial Base Oil Production Complex

Mohd fadzil,

Razali,

Zabiri

2023

Ind. Eng. Chem. Res.

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Base oil is the core of almost any lubricant and is one of the specialty products that can be produced in an oil refinery. Base oil processing is a complex process consisting of multiple units such as the atmospheric distillation unit, the vacuum distillation unit, intermediate tanks, the treating unit, the isomerization unit, the dearomatization unit, and the product fractionation unit. Variations in the properties of the different crude oil feedstocks, combined with the complexity of the integrated processing units with batch/continuous processes, result in a significant challenge in maximizing the base oil production to meet increasing consumer demands. Typical solutions to improve the yield of the base oil adopted in practice are to manually adjust the operating conditions based on past experiences. However, manual adjustment is not trivial, as changing one parameter affects several base oil properties. Thus, only one operating condition is changed at a time, while others remain fixed, resulting in a substantial delay in assessing the impact of the changes made. Consequently, production loss, product quality deterioration, waste generation, and higher energy consumption are inevitable. In addition, most planning analyses reported in the literature tend to assume a fixed yield of the base oil for each grade, regardless of feedstock quality. However, the yield function of the base oil is greatly dependent on the feed properties and operating conditions. Hence, in this paper, an attempt is made to automate the base oil yield maximization of an integrated industrial base oil processing complex via a machine learning-based optimization framework. Five machine learning models are explored: decision tree regression, support vector regression, artificial neural networks, random forest regression, and extreme gradient boosting (XGBoost) to represent the highly integrated industrial base oil processing complex. The XGBOOST models were found to be superior in performance and subsequently introduced as part of optimization functions to the global Bayesian optimization and differential evolution optimization techniques. The resulting machine learning-based optimization framework has been successfully validated during the actual plant trial at the integrated industrial base oil complex from January 2020 to December 2021, and the results show considerable improvement in yield further by 5.24 and 4.48% for both viscosity index optimization cases using Bayesian optimization and differential evolution techniques, respectively, and 4.02 and 2.05% for pour point optimization cases using Bayesian optimization and differential evolution techniques, respectively.

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Correction: Gu et al. Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine. Land 2021, 10, 173

Abstract: In the original article [...]

Cited by 2 publications

References 1 publication

Impact of Land cover Dynamics on Ecosystem services value of Siwalik range of Madhesh Province Nepal

Impact of Land cover Dynamics on Ecosystem services value of Siwalik range of Madhesh Province Nepal

Machine Learning-Based Modeling and Optimization Analysis for an Integrated Industrial Base Oil Production Complex

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