Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran)

Nasiri, Vahid; Darvishsefat, A A; Rafiee, Reza; Shirvany, A.; Hemat, M Avatefi

doi:10.1007/s11676-018-0659-9

Cited by 60 publications

(31 citation statements)

References 32 publications

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“…Different results have also been reported for flood prediction based on the ensemble models [93,94]. From these studies, we can conclude that the machine learning and ensemble learning techniques are greatly case-and site-specific, and that their performances depend heavily on the local conditions that the training datasets are developed upon, indicating that the application of different methods in different regions should be continued to find the optimum method for each environmental setting [95].…”

Section: Discussionmentioning

confidence: 94%

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Nguyen¹,

Avand

et al. 2020

Applied Sciences

136

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Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

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Section: Discussionmentioning

confidence: 94%

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Nguyen¹,

Avand

et al. 2020

Applied Sciences

136

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“…By extending the variability of the environmental conditions included in the dataset, whether topographic or hydrologic, the ensembling procedure we have presented in this study may lead to analogous predictive performances on a much larger scale. Overall, the use in tandem of remote sensing, GIS, and data mining tools is best able to produce reliable landslide susceptibility maps and other land use planning analysis [136] that can assist decision-makers for planning and development.…”

Section: Discussionmentioning

confidence: 99%

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Nhu

Shirzadi

Shahabi

et al. 2020

Forests

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We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers—Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landslide locations using 20 conditioning factors tested by the Information Gain Ratio (IGR) technique. We assessed model performance with statistically based indexes, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). All four machine learning models that we tested yielded excellent goodness-of-fit and prediction accuracy, but the RF-RAF ensemble model (AUC = 0.936) outperformed the BA-RAF, RS-RAF (AUC = 0.907), and RAF (AUC = 0.812) models. The results also show that the Random Forest model significantly improved the predictive capability of the RAF-based classifier and, therefore, can be considered as a useful and an effective tool in regional shallow landslide susceptibility mapping.

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“…The ANN enables the transmission of information from one multivariable space to another multivariable space [38]. It is a widely used approach for pattern recognition and classification problems [39][40][41]. The data statistical distribution is independently performed by the ANN and specific statistical parameters are not required for obtaining the estimation results.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

107

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The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

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Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis (case study: Arasbaran region, Iran)

Cited by 60 publications

References 32 publications

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

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