Land Covers Change Assessment After Small Dam’s Construction Based on the Satellite Data

Bhatti, Nabi Bux; Siyal, Altaf Ali; Qureshi, Abdul Latif; Bhatti, Imtiaz Ali

doi:10.28991/cej-2019-03091290

Cited by 16 publications

(4 citation statements)

References 13 publications

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“…Finally, we applied a set of smoothening and aggregation operations to smooth class boundaries and combine small or isolated pixel areas in the image. This method is also applied by Bhatti et al. (2019) in his study of land cover change assessment based on satellite data.…”

Section: Methodsmentioning

confidence: 99%

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Gharaibeh

Shaamala

Obeidat

et al. 2020

Heliyon

164

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Urban growth and land-use change are a few of many puzzling factors affecting our future cities. Creating a precise simulation for future land change is a challenging process that requires temporal and spatial modeling. Many recent studies developed and trained models to predict urban expansion patterns using Artificial Intelligence (AI). This study aims to enhance the simulation capability of Cellular Automata Markov Chain (CA-MC) model in predicting changes in land-use. This study integrates the Artificial Neural Network (ANN) into CA-MC to incorporate several driving forces that highly impact land-use change. The research utilizes different socio-economic, spatial, and environmental variables (slope, distance to road, distance to urban centers, distance to commercial, density, elevation, and land fertility) to generate potential transition maps using ANN Data-driven model. The generated maps are fed to CA-MC as additional inputs. We calibrated the original CA-MC and our models for 2015 cross-comparing simulated maps and actual maps obtained for Irbid city, Jordan in 2015. Validation of our model was assessed and compared to the CA-MC model using Kappa indices including the agreement in terms of quantity and location. The results elucidated that our model with an accuracy of 90.04% substantially outperforms CA-MC (86.29%) model. The improvement we obtained from integrating ANN with CA-MC suggested that the influence imposed by the driving force was necessary to be taken into account for more accurate prediction. In addition to the improved model prediction, the predicted maps of Irbid for the years 2021 and 2027 will guide local authorities in the development of management strategies that balance urban expansion and protect agricultural regions. This will play a vital role in sustaining Jordan's food security.

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Section: Methodsmentioning

confidence: 99%

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Gharaibeh

Shaamala

Obeidat

et al. 2020

Heliyon

164

View full text Add to dashboard Cite

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“…This statistical method examines the magnitude of anomalies in each country, including investment in renewable energy. It plays a key role in analyzing statistical changes over time and provides valuable information on the impact of an AI-driven green economy on economic and environmental outcomes (Bhatti et al, 2019).…”

Section: Paired Sample T-testmentioning

confidence: 99%

Integrating AI-Driven Green Finance Strategies for Sustainable Development: A Comparative Analysis of Renewable Energy Investments in Germany and Denmark

Ravan Ramzani,

Konhaeusner,

Olaniregun

et al. 2024

EJBMR

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This research explores the convergence of synthetic intelligence (SI) and inexperienced finance techniques in influencing the development of renewable power sectors, with a specific focus on Denmark and Germany for the critical periods of 2019 and 2020. ANOVA, paired sample t-tests, and regression analysis were used as part of a strict method to look into how the production of renewable energy has changed and how AI-driven financial techniques have affected it. The results spotlight the effectiveness of AI-driven green finance solutions in bringing approximately enormous ameliorations, establishing Denmark as a probable exemplar for sustainable progress. In evaluation, Germany’s consistent power infrastructure, blended with a fantastic correlation exposed in regression evaluation, highlights the durability of its environmentally pleasant economic methods. This study presents a well-timed and informative guide for developing effective, inexperienced finance rules that guide a greener and more sustainable future as international locations all around the world address environmental-demanding situations.

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“…Water, vegetation, and soil were analyzed to detect LUCC. The supervised maximum likelihood (MLH) method was used to classify the multispectral satellite images (Bhatti et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…With numerous data based on picture satellite data, machine learning will forecast whether it will rain or not in one area (Sodhi et al, 2019). A geographic information system (GIS) provides a flexible environment for collecting, storing, displaying, and analyzing the digital data necessary for LUCC detection (Bhatti et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An assessment of pluvial hazard in South Jakarta based on land-use/cover change from 2016 to 2022

Yosua,

Kusuma,

Nugroho

2024

Front. Built Environ.

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South Jakarta is a megapolitan city that is directly affected by Land Use Cover Change (LUCC). One of the impacts that arise is urban flooding, otherwise known as pluvial floods. More work is needed to evaluate the effect of LUCC on the increase of pluvial floods in South Jakarta. In this study, the runoff coefficient value (C) caused by LUCC is determined and the hazard risk of pluvial floods is evaluated. The analysis uses linear regression classification of LUCC using GIS software and carries out hazard and risk analysis based on Minister of Public Works Regulation Number 12 of 2014. Based on the rational method flood discharge formula, the higher C value result in the greater flow rate in a watershed. To simplify the calculation, the C equivalent value used is the C value that represent the watershed. The growth of the runoff coefficient (C) in South Jakarta is 1%–1.3% per year. It is found that the rate of increase in the runoff coefficient (C) is the same as the population growth rate of DKI Jakarta, which is around 1.3% per year. The increasing of runoff coefficient also increases the risk of pluvial flooding in locations that are prone to flooding. Furthermore, the hazard risk study found that there are 2 locations with a moderate level on the hazard risk index, namely, Ciledug Seskoal and Dharmawangsa Taman Gajah. This study can be used by government agencies as a basis to construct the spatial prioritization framework for pluvial flood mitigation.

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Land Covers Change Assessment After Small Dam’s Construction Based on the Satellite Data

Cited by 16 publications

References 13 publications

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Improving land-use change modeling by integrating ANN with Cellular Automata-Markov Chain model

Integrating AI-Driven Green Finance Strategies for Sustainable Development: A Comparative Analysis of Renewable Energy Investments in Germany and Denmark

An assessment of pluvial hazard in South Jakarta based on land-use/cover change from 2016 to 2022

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