RESUMEN
The United Arab Emirates (UAE) is located in an arid desert climate with very limited water resources and scarce rainfall. Along with the fast development of the country, the water demand for agriculture, industrial, and domestic purposes increased and led to diminishing groundwater resources. In this study, we explore the land surface deformations due to groundwater overexploitation in the agricultural area of Remah by analyzing Sentinel-1 data between 2015 and 2019 with the novel Parallelized-Persistent Scatterer Interferometry (P-PSI) technique. The detected land surface deformations have been correlated to the recorded groundwater levels at nearby water wells. This study detected land surface deformations in a form of an extensive subsidence bowl (with 28.5 km in diameter) with a maximum subsidence rate of 40 mm/year and a standard deviation within the bowl of less than 2 mm/year. The detected subsidence was associated with a 12 m drop in the water table level within the study area. The Persistent Scatterers with the highest deformations rate were spatially correlated with the depression cone of the groundwater level. These findings provide useful insights in understanding the groundwater regime of the area and have an important role in assessing regional hazards and driving mitigation measures towards managing uncontrolled groundwater overexploitation for sustainable management of groundwater resources.
RESUMEN
The main objective of the current study was to present a methodological approach that combines Information Theory, a neural network and meta-heuristic techniques so as to generate a landslide susceptibility map. Specifically, the methodology involved three important tasks: Classifying the landslide related variables, weighting them and optimizing the structural parameters of the neural network. Shannon's entropy index was used to estimate for each landslide related variable the number of classes which maximized the information coefficient, whereas the Certainty Factor method was used to weight the variables. A Neural Network, a (NN) which uses stochastic gradient descent (SGD), the structural parameters of which are optimized by a Genetic Algorithm (GA), was implemented to generate the landslide susceptibility map. A well defined spatial database which included 380 landslides and fourteen related variables (elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index, stream power index, stream transport index, land use cover, distance to road, distance to faults, distance to river, lithology and soil cover) were considered for implementing the NN-SGD-GA model, in the Yanshan County located in Shangrao Municipality, in the north-eastern of Jiangxi province, China. To validate the predictive power of the novel model, a Logistic Regression (LR) and Random Forest (RF) model were used for comparison. The results showed that the NN-SGD-GA model achieved the highest prediction accuracy (88.10%), followed by the RF (86.26%) and the LR (85.82%) models. Furthermore, by analyzing the validation data, concerning the spatial distribution of landslides and the susceptibility index, the proposed model showed an area under curve value of 0.8212, followed by the RF (0.8124) and the LR (0.8020) models. Finally, the proposed model showed the highest relative landslide density value of 65.09, followed by the RF (62.51) and the LR (61.76) models, when using the validation dataset. The novelty of our approach is the usage of an intelligent way to select and classify the most appropriate prognostic variables and also the implementation of an evolutionary wrapper automatic procedure that efficiently generates prediction models with reduced complexity and adequate generalization capacity. Overall, the proposed model can be successfully used for landslide susceptibility mapping as an alternative spatial investigation tool.
RESUMEN
The main objective of the present study was to investigate land subsidence phenomena and the spatiotemporal pattern of groundwater resources in an area located in western Thessaly, Greece, by using remote sensing techniques and data mining methods. Specifically, the nonparametric Mann-Kendall test and the Sen's slope estimator were used to estimate the trend concerning the groundwater table, whereas a set of Synthetic Aperture Radar images, processed with the Persistent Scatterer Interferometry technique, were used investigate the spatial and temporal patterns of ground deformation. Random forest (RF) method was utilized to predict the subsidence deformation rate based on three related variables, namely: thickness of loose deposits, the Sen's slope value of groundwater-level trend, and the Compression Index of the formation covering the area of interest. The outcomes of the study suggest a strong correlation among the thickness of the loose deposits and the deformation rate and also show that a clear trend between the deformation rate and the fluctuation of the groundwater table exists. For the RF model and based on the validation dataset, the r square value was calculated to be 0.7503. In the present study, the potential deformation rate assuming different water pumping scenarios was also estimated. It was observed that with a mean decrease in the Sen's slope value of groundwater-level trend of 20%, there would be a mean decrease of 9.01% in the deformation rate, while with a mean increase in the Sen's slope value of groundwater-level trend of 20%, there would be a mean increase of 12.12% in the deformation rate. The ability of identifying surface deformations allows the local authorities and government agencies to take measures before the evolution of severe subsidence phenomena and to prepare for timely protection of the affected areas.
