Hybridization of GALDIT method to assess actual and future coastal vulnerability to seawater intrusion

Contamination and seawater intrusion are esteemed as chief issues of coastal aquifers resulting from unscientific utilization, non-standard monitoring network and improper management. In the present study, an optimum monitoring network with appropriate numbers and standard spatial distribution was designed based on a vulnerability map of the Urmia coastal aquifer. A vulnerability map was extracted using a modified GALDIT-iP model and searching optimum network was done based on genetic algorithm (GA). The maximum value of the correlation between electrical conductivity (EC) and vulnerability index, the minimum number of monitoring wells and the highest value of Nash-Sutcliff were utilized for the simultaneous optimization model. The W-weighting coefficient was considered for economical goals and three targets were defined in a general objective function. The results showed that the W-weighting coefficient has a significant effect to determine optimal solution, and the best weighting was opted considering the most optimal response based on the precise of the monitoring network and vulnerability index. An acceptable optimization and validation process was obtained with the predictions of the validation results. For W = 1, the final value of the objective function was obtained 1.791 using 91 wells with a correlation coefficient of 0.935 and Nash-Sutcliff of 0.979, resulting in the appropriate spatial distribution of the wells and reduction of 18 wells from the existing monitoring wells. 

Optimization design of quality monitoring network of Urmia plain using genetic algorithm and vulnerability map

Optimal redesign of coastal groundwater quality monitoring networks under uncertainty: application of the theory of belief functions

Evolution of the fresh to salt water interface from electrical sounding near Oued Nador, Tipaza (North Algeria)

Karst vegetation is of great significance for ecological restoration in karst areas. Vegetation Indices (VIs) are mainly related to plant yield which is helpful to understand the status of ecological restoration in karst areas. Recently, karst vegetation surveys have gradually shifted from field surveys to remote sensing-based methods. Coupled with the machine learning methods, the Unmanned Aerial Vehicle (UAV) multispectral remote sensing data can effectively improve the detection accuracy of vegetation and extract the important spectrum features.In this study, UAV multispectral image data at flight altitudes of 100 m, 200 m, and 400 m were collected to be applied for vegetation detection in a karst area. The resulting ground resolutions of the 100 m, 200 m, and 400 m data are 5.29, 10.58, and 21.16 cm/pixel, respectively. Four machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and Deep Learning (DL), were compared to test the performance of vegetation coverage detection. 5 spectral values (Red, Green, Blue, NIR, Red edge) and 16 VIs were selected to perform variable importance analysis on the best detection models. The results show that the best model for each flight altitude has the highest accuracy in detecting its training data (over 90%), and the GBM model constructed based on all data at all flight altitudes yields the best detection performance covering all data, with an overall accuracy of 95.66%. The variables that were significantly correlated and not correlated with the best model were the Modified Soil Adjusted Vegetation Index (MSAVI) and the Modified Anthocyanin Content Index (MACI), respectively. Finally, the best model was used to invert the complete UAV images at different flight altitudes.In general, the GBM_all model constructed based on UAV imaging with all flight altitudes was feasible to accurately detect karst vegetation coverage. The prediction models constructed based on data from different flight altitudes had a certain similarity in the distribution of vegetation index importance. Combined with the method of visual interpretation, the karst green vegetation predicted by the best model was in good agreement with the ground truth, and other land types including hay, rock, and soil were well predicted. This study provided a methodological reference for the detection of karst vegetation coverage in eastern China. 

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Karst vegetation coverage detection using UAV multispectral vegetation indices and machine learning algorithm

Maps of the vulnerability of groundwater to seawater intrusion were developed for the multi-layered coastal aquifer of Ouled Taoui—Ouled Boujemaa in Algeria. Groundwater in this area is threatened by the risk of salinization due to excessive groundwater use. To protect this aquifer, mapping the vulnerability to potential saline intrusion was carried out using a modified GALDIT index mapping model that takes into account six parameters, namely: (G) groundwater occurrence, (A) aquifer hydraulic conductivity or network development and persistence index (IDPR), (L) depth to groundwater level above sea, (D) distance from shore, (I) impact on the existing status of seawater intrusion in the area, and (T) It’s the thickness of the aquifer. A system for rating, weighting and classifying the GALDIT model according to the relative importance of these parameters was developed under a GIS environment. The compilation results illustrate that the underlying confined aquifer with clay levels characterizing a low hydraulic potential. It will be more vulnerable during excessive pumping and in the presence of karst conducts, which produces an impact of salinity decay as soon as one moves away from the shore. The assessment indicated that the vulnerability of the uppermost unconfined aquifer was particularly sensitive to changes in the weights of parameters (A), (L) and (D). Areas with a “high” vulnerability to seawater intrusion are located especially in the coastal fringe. The results of the vulnerability assessment were validated based on: the density and distribution of existing groundwater production wells, the land cover map, and the hydrochemical quality of groundwater. Finally, the identification of areas of “high” vulnerability to saline intrusion will serve as a tool for the management, control, and protection of coastal groundwater resources.

Mapping of the groundwater vulnerability to saline intrusion using the modified GALDIT model (Case: the Ain Temouchent coastal aquifer, (North-Western Algeria))

The coastal strip of Mostaganem is an area with an agricultural vocation, characterized in recent decades by profound changes in land use, the results of biophysical processes and complex interactions between societies and their environment. These processes transform the landscape of several spatial and temporal scales. In this context, over the past few years, the evaluation of changes in cultivated areas has become one of the major priorities; it makes it possible to develop action plans and to propose management approaches that lead to sustainable agricultural development.Therefore, the use of remote sensing techniques and Landsat multispectral data for the diachronic mapping of agricultural land is a necessity. This work highlights the considerable contribution to the application of spectral indices of chlorophyll in the delimitation of cultivated areas and the analysis of their changes. In this work, we applied two spectral indices, on the one hand, the RECl index (Red-Edge Chlorophyll Vegetation Index) which shows the photosynthetic activity of the plant cover and, on the other hand, the GCI index (Green Chlorophyll Vegetation Index) Index which estimates the chlorophyll content of the leaves of various plant species and detects the physiological state of vegetation. These applications allow the evaluation of the state of vegetation growth and the monitoring of ecological changes in the environment of agricultural areas.The comparison between the results of the two indices shows that the GCI index is very effective in terms of automatic extraction of agricultural areas and discriminates between plant formations compared to the RECI index which includes all formations with important chlorophyll activity (agriculture, forest and natural vegetation).The analysis of the time series (1989, 2011, and 2021) globally confirms a trend of stability of the cultivated areas between "1989 to 2011" and a trend of improvement at the regional scale between "2011 to 2021" as a result of the improvement in rainfall conditions from 2011 and the application of effective action programs carried out by the state for the improvement of the agricultural sector.However, diachronic analyzes by the GCI index revealed significant increases in agricultural areas between 2011 and 2021 corresponding to 58% and stability between 1989 and 2011 with an increase of 0.17%. On the other hand, plant biomass grew by 22% between 1989 and 2011 and between 2011 and 2021 an increase of 7.25%.

Contribution of spectral indices of chlorophyll (RECl and GCI) in the analysis of multi-temporal mutations of cultivated land in the Mostaganem plateau

Saad Assia

Papers

Mapping of the groundwater vulnerability to saline intrusion using the modified GALDIT model (Case: the Ain Temouchent coastal aquifer, (North-Western Algeria))

Contribution of spectral indices of chlorophyll (RECl and GCI) in the analysis of multi-temporal mutations of cultivated land in the Mostaganem plateau