Performance evaluation of multiple methods for landscape aesthetic suitability mapping: A comparative study between Multi-Criteria Evaluation, Logistic Regression and Multi-Layer Perceptron neural network

Saeidi, Sepideh; Mohammadzadeh, Marjan; Salmanmahiny, Abdolrassoul; Mirkarimi, Seyed Hamed

doi:10.1016/j.landusepol.2017.05.014

Cited by 52 publications

(20 citation statements)

References 33 publications

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“…The test set was used to assess the performance of a trained and validated network. In most literature [14,[19][20][21][22][23][24][25], as the network structure are usually pre-defined or tested by trial-and-error, the validation sets are usually disused or replaced by the test sets. Under such substitution, the performance of the network is only meaningful for certain sets (the 'test sets'), which have been optimized during the training, rather than for the untrained data which we expect more precise predictions.…”

Section: The Accelerated Exploration and Ann Designmentioning

confidence: 99%

“…The number of nodes in hidden layer is usually determined through trial-and-error method [19,23,43]. The range of attempts is usually within 1 to 20 [14,[19][20][21][22][23][24][25], or 3 times the number of input variables [43]. The best number of nodes was the one having the smallest mean-square error (MSE) and root-mean-square error (RMSE) and the highest correlation coefficient (r) for the validation data set.…”

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

“…Simple networks maybe less accurate in learning the problem while complex networks may take excessively long training time. one hidden layer is usually sufficient in most cases [14,[19][20][21][22][23][24][25]33,[41][42][43] while sometimes multiple hidden layers shows better learning on certain problems [35].…”

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

“…Luckily, there are also several improved first-order backpropagation methods. The most commonly used is the Backpropagation with Momentum [22,24], which significantly speed up the training process. The momentum is an inertial factor applied to the weights during the back propagate process, which aims to maintain the direction of weight changing [40].…”

Section: Learning Algorithm and Parameter Settingmentioning

confidence: 99%

“…In most cases, the start/fixed learning rate will be in the range of [0.01,0.3] [21,22,25,34] while the end learning rate within [0.00013,0.001] [19,21]. The number of epochs usually depends on the training data size and the computational capacity, ranging from 200 to 15,000 [19,21,22,24,34,35,42]. Momentum is typically set to 0.9 [22], although the optimal value might be task-specific [21,24,34].…”

Section: Learning Algorithm and Parameter Settingmentioning

confidence: 99%

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Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

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Urich

et al. 2018

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In this study, the neural network method (Multi-Layer Perceptron, MLP) was integrated with an explorative model, to study the feasibility of using machine learning to reduce the exploration time but providing the same support in long-term water system adaptation planning. The specific network structure and training pattern were determined through a comprehensive statistical trial-and-error (considering the distribution of errors). The network was applied to the case study in Scotchman’s Creek, Melbourne. The network was trained with the first 10% of the exploration data, validated with the following 5% and tested on the rest. The overall root-mean-square-error between the entire observed data and the predicted data is 10.5722, slightly higher than the validation result (9.7961), suggesting that the proposed trial-and-error method is reliable. The designed MLP showed good performance dealing with spatial randomness from decentralized strategies. The adoption of MLP-supported planning may overestimate the performance of candidate urban water systems. By adopting the safety coefficient, a multiplicator or exponent calculated by observed data and predicted data in the validation process, the overestimation problem can be controlled in an acceptable range and have few impacts on final decision making.

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Section: The Accelerated Exploration and Ann Designmentioning

confidence: 99%

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

Section: The Structure Of Mlp Networkmentioning

confidence: 99%

Section: Learning Algorithm and Parameter Settingmentioning

confidence: 99%

Section: Learning Algorithm and Parameter Settingmentioning

confidence: 99%

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Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

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et al. 2018

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A Spatial Multi-criteria Decision Support System for Stress Recovery-Oriented Forest Management

Capecchi

Grilli

Barbierato

et al. 2021

Green Energy and Technology

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A solution to cope with chaotic urban settlements and frenetic everyday life is refuging in nature as a way to reduce stress. In general—in recent years—it has been scientifically demonstrated how natural areas are an important environment for psycho-physiological health. As a consequence, it is important to plan dedicated spaces for stress recovery in order to increase the well-being of people. With respect to forests, there is a growing interest in understanding the marketing and tourist potential of forest-therapy activities and policies. This paper develops a decision support system (DSS) for decision makers, based on geographic information system to define the suitability of forest areas to improve psychological and physiological human well-being. Innovative technologies such as electroencephalography (EEG) and virtual reality (VR) are applied to test human status. The DSS combines four sets of indicators in a multi-attribute decision analysis and identifies the areas with the largest stress-recovery potential. Two multi-attribute model—one in summer and one in winter—are elaborated to obtain a dynamic evaluation of suitability. Results show significant differences among forest type, forest management, altitude range, and season in terms of stand suitability. EEG and VR seem to be promising technologies in this research area. Strengths and weaknesses of the approach, as well as potential future improvement and implications for territorial marketing, are suggested.

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Innovation on Research Methods: Geotagged Photos as a Proxy Tool to Map Heritage and Cultural Ecosystem Services Hotspots

Azevedo

Duenas

Bastos

2021

Tourism, Hospitality &Amp; Event Management

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Performance evaluation of multiple methods for landscape aesthetic suitability mapping: A comparative study between Multi-Criteria Evaluation, Logistic Regression and Multi-Layer Perceptron neural network

Cited by 52 publications

References 33 publications

Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

Accelerated Exploration for Long-Term Urban Water Infrastructure Planning through Machine Learning

A Spatial Multi-criteria Decision Support System for Stress Recovery-Oriented Forest Management

Innovation on Research Methods: Geotagged Photos as a Proxy Tool to Map Heritage and Cultural Ecosystem Services Hotspots

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