Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms

Zamanirad, Mahtab; Sarraf, Amirpouya; Sedghi, Hossein; Saremi, Ali; Rezaee, Payman

doi:10.1007/s11053-019-09490-9

Cited by 20 publications

(11 citation statements)

References 74 publications

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“…In this study, four popular MLAs namely the Maximum Entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM) were used for modeling and mapping of LS, based on the stateof-the-art skillful characteristics and literature study (Abdollahi et al, 2019). The selection behind these MLAs are based on their earlier involvement in different research work on natural hazard susceptibility studies and respective optimal prediction performance (Zamanirad et al, 2019;Mohebbi Tafreshi et al, 2020;Najafi et al, 2020). In the case of MaxEnt, it has the ability to choose the correct estimation of the uncertain probability distribution and to select the highest entropy of the given probabilistic constraints.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…The RF, PNN, DT, and NBC MLMs were used for the GWP. All the implemented models' algorithms were suitable with R version 4.0.0 (Zamanirad et al, 2019). first, the ArcGIS spatial analysis tool (random partition algorithm) was used for classification of 85 dug wells and the location of the piezometer as occurrence (assigned as "1") and 24 dug well location as nonoccurrence (assigned as "0").…”

Section: Methodsmentioning

confidence: 99%

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

Saha¹,

Gayen²,

Mukherjee³

et al. 2021

Preprint

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Machine learning techniques offer powerful tools for the assessment and management of groundwater resources. Here, we evaluated the groundwater potential maps (GWPMs) in Md. Bazar Block of Birbhum District, India using four GIS-based machine-learning algorithms (MLA) such as predictive neural network (PNN), decision tree (DT), Naïve Bayes classifier (NBC), and random forest (RF). We used a database of 85 dug wells and one piezometer location identified using extensive field study, and employed 12 influencing factors (elevation, slope, drainage density (DD), topographical wetness index, geomorphology, lineament density, rainfall, geology, pond density, land use/land cover (LULC), geology, and soil texture) for evaluation through GIS. The 85 dug wells and 1 piezometer locations were sub-divided into two classes: 70:30 for training and model validation. The DT, RF, PNN, and NBC MLAs were implemented to analyse the relationship between the dug well locations and groundwater influencing factors to generate GWPMs. The results predict excellent groundwater potential areas (GPA) DT RF of 17.38%, 14.69%, 20.43%, and 13.97% of the study area, respectively. The prediction accuracy of each GWPM was determined using a receiver operating characteristic (ROC) curve. Using the 30% data sets (validation data), accuracies of 80.1%, 78.30%, 75.20%, and 69.2% were obtained for the PNN, RF, DT, and NBC models, respectively. The ROC values show that the four implemented models provide satisfactory and suitable results for GWP mapping in this region. In addition, the well-known mean decrease Gini (MDG) from the RF MLA was implemented to determine the relative importance of the variables for groundwater potentiality assessment. The MDG revealed that drainage density, lineament density, geomorphology, pond density, elevation, and stream junction frequency were the most useful determinants of GWPM. Our approach to delineate the GWPM can aid in the effective planning and management of groundwater resources in this region.

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“…The authors obtained satisfactory simulation results, indicating at the same time the need to use empirical data to validate the modelling outcomes. Zamanirad et al [198] used boosted regression trees (BRTs), generalized additive model (GAM) and random forest (RF), together with anthropogenic and environmental predicators, to predict land subsidence in southern Iran. These predictors were generated based on extensive field studies.…”

Section: Ai Methodsmentioning

confidence: 99%

State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal

Guzy

Malinowska

2020

Water

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Land subsidence is probably one of the most evident environmental effects of groundwater pumping. Globally, freshwater demand is the leading cause of this phenomenon. Land subsidence induced by aquifer system drainage can reach total values of up to 14.5 m. The spatial extension of this phenomenon is usually extensive and is often difficult to define clearly. Aquifer compaction contributes to many socio-economic effects and high infrastructure-related damage costs. Currently, many methods are used to analyze aquifer compaction. These include the fundamental relationship between groundwater head and groundwater flow direction, water pressure and aquifer matrix compressibility. Such solutions enable satisfactory modelling results. However, further research is needed to allow more efficient modelling of aquifer compaction. Recently, satellite radar interferometry (InSAR) has contributed to significant progress in monitoring and determining the spatio-temporal land subsidence distributions worldwide. Therefore, implementation of this approach can pave the way to the development of more efficient aquifer compaction models. This paper presents (1) a comprehensive review of models used to predict land surface displacements caused by aquifer drainage, as well as (2) recent advances, and (3) a summary of InSAR implementation in recent years to support the aquifer compaction modelling process.

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Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms

Cited by 20 publications

References 74 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms

State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal

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