Mapping potential groundwater accumulation zones for Karachi city using GIS and AHP techniques

Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia

Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resource is now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN based AUC validation platform showed that, 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration, and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China

Predicting groundwater potential is crucial for identifying the spatial distribution of groundwater in a region. It serves as an essential guide for the development, utilization, and protection of groundwater resources. Previous studies have primarily emphasized finding the most accurate prediction model for groundwater potential while giving less attention to the selection of training features and sample sizes. This study aims to predict groundwater potential within Qinghai Province using automated machine learning technology and assess the influence of sample sizes and feature selection on prediction accuracy. Sixteen groundwater conditioning factors were categorized into categorical and numerical variables. Four feature selection modes were utilized as input in training the model. The results indicated that, except for correlations between evaporation and landforms (- 0.8) and precipitation and normalized difference vegetation index (0.8), the Pearson correlation coefficients among the remaining sixteen factors were ≤ 0.5 or ≥  - 0.5. The models XGB-ALL, RF-Entropy, ET-CRITIC, and XGB-PCA yielded accuracy scores of 0.783, 0.685, 0.745, and 0.703, and area under curve (AUC) of 0.819, 0.724, 0.779, and 0.747, respectively. If enough samples are available with the tree model, an increased number of features can improve prediction accuracy. The principal component analysis method showed difficulty in reducing the dimensionality of the input space, while the Entropy method proved efficient. The accuracy and AUC value of the prediction model improved with an increasing number of samples. Training with 8 features and 200 data points achieved an accuracy of 0.745, sufficient to evaluate regional groundwater potential. As for training with 600 samples, the model's performance accuracy rose to 0.9, enabling precise groundwater potential prediction. The outputs of this research can provide decision-makers in groundwater resource management in Qinghai Province with crucial theoretical and practical support. The lessons learned can have future applications in similar situations.

Assessment of plastic pollution in coastal areas of Karachi: Case study of West Warf, Kemari Jetty, and Manora

This study focused on marine pollution in coastal areas of Karachi, particularly West Warf, Kemari Jetty, and Manora. The research examined the sources and quantities of waste, from boat manufacturing, export units, and local commercial activities. Stakeholder interviews were conducted to understand waste management practices and identify the key contributors to ocean litter. The results indicated that restaurants, export units, boat construction, and tourist and commuter activities were the primary sources of marine pollution. Plastic was found to be the most prevalent litter category, with LDPE (e.g., single-use bags) and polystyrene (e.g., material in floating docks) being the most common types. Additionally, multi-layer packaging, such as chip wrappers, was frequently observed in the surveyed areas. Overall, this research highlights the urgent need for improved waste management and compliance measures in coastal regions to mitigate marine pollution.

An integrated groundwater resource management approach for sustainable development in a tropical river basin, southern India

Evaluation of aquifer potential is essential, as the potable water demand has increased globally over the last few decades. The present study delineated different zones of groundwater potential and groundwater quality of the Kallada River basin (KRB) in southern India, using geo-environmental and hydrogeochemical parameters, respectively. Geo-environmental variables considered include relative relief, land use/land cover, drainage density, slope angle, geomorphology, and geology, while hydrogeochemical parameters include pH, electrical conductivity (EC), Cl-, Fe3+, and Al3+ concentrations. Analytical hierarchy process (AHP) was used for categorizing groundwater potential and quality zones. Nearly 50% of KRB is categorized as very high and high groundwater potential zones, occupying the western and midland regions. The central and west-central parts of KRB are characterized by excellent groundwater quality zones, while the eastern and western parts are characterized by good and poor groundwater quality zones, respectively. By integrating the groundwater potential and groundwater quality, sustainable groundwater management is observed to be necessary at about 54% of the basin, where site-specific groundwater management structures such as percolation ponds, injection wells, and roof water harvesting have been proposed using a rule-based approach. This integrated groundwater potential-groundwater quality approach helps policymakers to implement the most suitable management strategies with maximum performance.

Groundwater potential assessment using GIS-based ensemble learning models in Guanzhong Basin, China

Groundwater plays a crucial role in sustaining industrial and agricultural production and meeting the water demands of the growing population in the semi-arid Guanzhong Basin of China. The objective of this study was to evaluate the groundwater potential of the region through the use of GIS-based ensemble learning models. Fourteen factors, including landform, slope, slope aspect, curvature, precipitation, evapotranspiration, distance to fault, distance to river, road density, topographic wetness index, soil type, lithology, land cover, and normalized difference vegetation index, were considered. Three ensemble learning models, namely random forest (RF), extreme gradient boosting (XGB), and local cascade ensemble (LCE), were trained and cross-validated using 205 sets of samples. The models were then applied to predict groundwater potential in the region. The XGB model was found to be the best, with an area under the curve (AUC) value of 0.874, followed by the RF model with an AUC of 0.859, and the LCE model with an AUC of 0.810. The XGB and LCE models were more effective than the RF model in discriminating between areas of high and low groundwater potential. This is because most of the RF model's prediction outcomes were concentrated in moderate groundwater potential areas, indicating that RF is less decisive when it comes to binary classification. In areas predicted to have very high and high groundwater potential, the proportions of samples with abundant groundwater were 33.6%, 69.31%, and 52.45% for RF, XGB, and LCE, respectively. In contrast, in areas predicted to have very low and low groundwater potential, the proportions of samples without groundwater were 57.14%, 66.67%, and 74.29% for RF, XGB, and LCE, respectively. The XGB model required the least amount of computational resources and achieved the highest accuracy, making it the most practical option for predicting groundwater potential. The results can be useful for policymakers and water resource managers in promoting the sustainable use of groundwater in the Guanzhong Basin and other similar regions.