Land subsidence susceptibility assessment using random forest machine learning algorithm

Mohammady, Majid; Pourghasemi, Hamid Reza; Amiri, Mojtaba

doi:10.1007/s12665-019-8518-3

Cited by 86 publications

(40 citation statements)

References 65 publications

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“…To further verify the reliability of this algorithm, it is compared with other classic prediction algorithms, including the SVR prediction algorithm [15] and the RF regression prediction method [16]. The horizontal displacement deformation of monitoring point B is predicted in MATLAB, and the results of the final regression prediction output are compared with the genetic algorithm-improved BP neural network model.…”

Section: Comparison With Other Typical Prediction Methodsmentioning

confidence: 99%

“…The RF model is composed of several independent decision trees, where each decision tree randomly selects samples and features to obtain multiple weak classifiers for local domain learning and combines them into a global strong classifier [19]. In the process of building the model, there are relatively few parameters that need to be adjusted, which only include the number of Classification And Regression Tree (CART) and split attributes.…”

Section: Comparison With Other Typical Prediction Methodsmentioning

confidence: 99%

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The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

2020

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The objective is to improve the prediction accuracy of foundation pit deformation in geotechnical engineering, thereby provide early warning for engineering practice. The digital closerange photogrammetry is used to obtain monitoring data. The error compensation method is used to optimize the center of the monitoring point. Aiming at the limitations of back propagation neural network (BPNN), a genetic algorithm (GA)-optimized BPNN algorithm is proposed. Then, the optimized algorithm is applied to predict the deformation and displacement of foundation pits from three aspects, i.e., simple horizontal displacement, simple longitudinal displacement, and the combination of horizontal and longitudinal displacements. Meanwhile, the time domain, space domain, and time-space domain are used as input features to compare the prediction results of the BPNN model and the GA-optimized BPNN model. Finally, the GA-improved BPNN is compared with the Support Vector Regression (SVR) model and Random Forest (RF) model. The results show that the prediction result, obtained by simultaneously using horizontal displacement and longitudinal displacement as input features, has smaller errors; also, the actual output is closer to the expected output. Compared with the prediction result with time domain and space domain as input features, the prediction result with time-space domain as input features is closer to the expected output. Taking the combination of time and space domains as input features, compared with the BPNN model, the GA-optimized BPNN model has a lower Root Mean Squared Error (RMSE) value (0.0163), a larger Index of Agreement (IA) value (0.9800), and a shorter training time (7.08 s). Compared with the SVR model and RF model, the GA-improved BPNN model has a lower Root Mean Squared Error (RMSE) value (0.0211), a larger Index of Agreement (IA) value (0.9706), and shorter training time (7.61 s). Therefore, the foundation pit deformation prediction model based on BPNN and GA has strong prediction ability, which can be popularized and applied in similar geotechnical engineering.

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Section: Comparison With Other Typical Prediction Methodsmentioning

confidence: 99%

Section: Comparison With Other Typical Prediction Methodsmentioning

confidence: 99%

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

2020

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“…In Sirjan plain, for every 10 m of a drop in groundwater level, there has been land subsidence of 27 cm (Choopani et al, 2017). In Semnan plain for every meter of groundwater level drops per year, there has been land subsidence of 5 to 10 cm on average (Mohammady et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Consequences of groundwater overexploitation on land subsidence in Fars Province of Iran and its mitigation management programme

Golian

Saffarzadeh

Katibeh

et al. 2021

Water & Environment J

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Land subsidence induced by rapid groundwater depletion in Fars Province (Iran) has led to serious problems for agricultural lands, structures and infrastructures, also its destructive effects on the Achaemenid Empire monuments has raised national concerns. Over the past three decades, mainly due to the increasing reliance of the economy on agriculture, the number of water wells has grown rapidly (7100 new wells with the annual withdrawal of 1.1 billion m3) and therefore the groundwater storage has decreased 10 billion m3 and groundwater level has declined averagely 11 m. To cease groundwater depletion and land subsidence, Iran's sustainable groundwater management programme that is currently being implemented to sustain aquifers is considered as the main remedy solution. To analyse the efficiency of the programme implementation in sustaining the aquifers of the province so far, the status of aquifers levels before the programme implementation (before 2015) is compared to its current status (after 2015). The results indicate that by implementing the programme, 22 out of 100 aquifers, mostly located in the southern half of the province, have been sustained. Although, mainly due to inappropriate management and limited financial support, 25 aquifers are still in critical condition, some of which are the most important aquifers in the province. However, implementing sustainable groundwater management in basins facing land subsidence due to groundwater overexploitation is important since it can minimize or stop land subsidence by sustaining aquifers and preventing groundwater drawdown.

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“…These machine learning models can efficiently reveal the intricate regulations which are hidden in huge amounts of data, which has benefits to produce more reliable results. Currently, these machine learning models have been accepted by many scholars in different domains due to their prominent predictive performance (Amiri et al, 2019;Choubin et al, 2019;Hosseinalizadeh et al, 2019;Mohammady et al, 2019). In some cases, the performance of machine learning models dramatically varies with databases, indicating that there still exists latent capacity to promote in generalization performance (Pourghasemi and Rahmati, 2018).…”

Section: Introductionmentioning

confidence: 99%

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

Zhang

Fu²,

Wang³

et al. 2021

Preprint

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Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree (FT), support vector machine (SVM) and classification regression tree (CART) and were integrated with bagging strategy. Then these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation (CAE) method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve (ROC) and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. among them the Bag-CART model has the highest AUC value of 0.874, however the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

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Land subsidence susceptibility assessment using random forest machine learning algorithm

Cited by 86 publications

References 65 publications

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

The deformation monitoring of foundation pit by back propagation neural network and genetic algorithm and its application in geotechnical engineering

Consequences of groundwater overexploitation on land subsidence in Fars Province of Iran and its mitigation management programme

Bagging Based Machine Learning Algorithms for Landslide Susceptibility Modeling

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