Prediction of Land Surface Temperature (LST) Changes within Ikom City in Nigeria Using Artificial Neural Network (ANN)

Maduako, Ikechukwu; Ebinne, Elijah S.; Zhang, Yun; Bassey, Patrick

doi:10.14355/ijrsa.2016.06.010

Cited by 12 publications

(9 citation statements)

References 3 publications

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“…The ANN was first created to identify the intricate situation and cognitive process of the human brain (Fernandez-Fernandez et al 2023). It doesn't need a lot of prior knowledge about the components, and its intricate structure replicates the system's basic mechanism to mimic the future LST (Dhamge, Atmapoojya, and Kadu 2012;Maduako 2015). The multi-layered perceptron (MLP) method, upon which the ANN is built, produces autonomous judgments about network parameters for improved modeling (Szabó et al 2006).…”

Section: Prediction Of Lst Ndvi Ndwi Uhi Using Artificial Neural Networkmentioning

confidence: 99%

Environmental Impact Assessment of Export Processing Zone Using Deep Learning-Based Approach: A Case Study of Mongla EPZ

Ahmed,

Uddin,

Saha

2024

Preprint

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The transformation induced by development in our environment leads to pollution, impacting both human life and economic output. Coastal regions, particularly vulnerable in the era of global climate change, bear significant ecological importance for habitation. The establishment of the Mongla Export Processing Zone in 1998 in the coastal thana of Mongla, Bangladesh, renowned for its seaport, has resulted in pronounced challenges such as salinity intrusion and diminished agricultural fertility. This study, spanning from 2007 to 2023, scrutinizes the influence of land use and land cover (LULC) on the land surface temperature (LST), urban heat island (UHI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) in the Mongla EPZ. Employing a deep learning-based Artificial Neural Network (ANN) model, predictions for 2027 and 2031 are derived. A noteworthy finding revolves around settlement dynamics, with virtually no settlement before 2011, experiencing a substantial increase (8.27%) thereafter. The NDWI analysis underscores the region's drought-free status before 2011, evolving into exposure to severe (10.12%) and moderate (15.06%) drought conditions with increased industrialization. The vegetation undergoes an inverse transformation. The mean temperature exhibits an ascending trend due to industrialization, soaring from 18.9°C in 2007 to 21.61°C in 2023. The predictive CA-ANN algorithm anticipates a further rise, projecting a substantial portion of the LST escalating to 27 degrees Celsius, covering an estimated 28.33% of the municipal area by 2031. Additionally, areas with LST values 2°C higher than the surroundings are expected to reach 6.5% by 2031. The study underscores the profound impact of industrialization within EPZs on the surrounding environment and ecosystem.

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Section: Prediction Of Lst Ndvi Ndwi Uhi Using Artificial Neural Networkmentioning

confidence: 99%

Environmental Impact Assessment of Export Processing Zone Using Deep Learning-Based Approach: A Case Study of Mongla EPZ

Ahmed,

Uddin,

Saha

2024

Preprint

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“…With the advancement in artificial intelligence and machine learning-based modeling approaches, it has now become possible to spatially simulate climate-related indices such as LST with higher confidence. For instance, a Multi-layer Feed-Forward Back-Propagation Artificial Neural Network approach in TerrSet software (Maduako et al, 2016) is used to model, simulate, and project future LST patterns (Maduako et al, 2016;Ullah et al, 2019a). The Multi-Layer Perceptron (MLP) neural network decides network parameters and how they should be updated to automatically model the networks (Dey et al, 2021).…”

Section: Simulating Lst Projection For Years 2032mentioning

confidence: 99%

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

Mehmood

Rehman²,

Sajjad³

et al. 2023

Front. Ecol. Evol.

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While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

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“…Multi-layer perceptron (MLP), a class of feedforward ANN, is a non-linear function approximator in layers using back propagation with no activation function in the output layer. It used the rectified linear unit (Relu) function as the activation function in the hidden layers [7,8,[36][37][38][39]:…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

“…Although apart from air temperature, other different parameters are involved in the models of published papers, the participation percentage was not that high. In papers published in the last 10 years, the rough participation rate of climate variables as input parameters in soil temperature prediction models is as follows [5,7,8,17,29,[35][36][37][38][39]41]: Solar radiation and humidity are involved in 40% of studies; wind and pressure are considered in 25% of papers; and less than 15% of studies explored rainfall, sunshine and dewpoint. Additionally, 30% of the models used monthly parameters and 55% considered parameters on a daily basis.…”

Section: Importance Of Different Input Variablesmentioning

confidence: 99%

A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events

et al. 2022

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Soil temperature is a fundamental parameter in water resources and irrigation engineering. A cost-effective model that can accurately forecast soil temperature is urgently needed. Recently, many studies have applied artificial intelligence (AI) at both surface and underground levels for soil temperature predictions. In the present study, attempts are made to deliver a comprehensive and detailed assessment of the performance of a wide range of AI approaches in soil temperature prediction. In this regard, thirteen approaches, from classic regressions to well-established methods of random forest and gradient boosting to more advanced AI techniques, such as multi-layer perceptron and deep learning, are taken into account. Meanwhile, great varieties of land and atmospheric variables are applied as model inputs. A sensitivity analysis was conducted on input climate variables to determine the importance of each variable in predicting soil temperature. This examination reduced the number of input variables from 8 to 7, which decreased the simulation load. Additionally, this showed that air temperature and solar radiation play the most important roles in soil temperature prediction, while precipitation can be neglected in forecast AI models. The comparison of soil temperature predicted by different AI models showed that deep learning demonstrated the best performance with R-squared of 0.980 and NRMSE of 2.237%, followed by multi-layer perceptron with R-squared of 0.980 and NRMSE of 2.266%. In addition, the performance of developed AI models was evaluated in extremely hot events since heat warnings are essential to protect lives and properties. The assessment showed that deep learning and multi-layer perceptron methods still have the best prediction. However, their R-squared decreased to 0.862 and 0.859, and NRMSE increased to 6.519% and 6.601%, respectively.

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Prediction of Land Surface Temperature (LST) Changes within Ikom City in Nigeria Using Artificial Neural Network (ANN)

Cited by 12 publications

References 3 publications

Environmental Impact Assessment of Export Processing Zone Using Deep Learning-Based Approach: A Case Study of Mongla EPZ

Environmental Impact Assessment of Export Processing Zone Using Deep Learning-Based Approach: A Case Study of Mongla EPZ

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events

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