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Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.

Flood susceptibility prediction is complex due to the multifaceted interactions among hydrological, meteorological, and urbanisation factors, further exacerbated by climate change. This study addresses these complexities by investigating flood susceptibility in rapidly urbanising regions prone to extreme weather events, focusing on Gdańsk, Poland. Three popular ML techniques, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Networks (ANN), were evaluated for handling complex, nonlinear data using a dataset of 265 urban flood episodes. An ensemble filter feature selection (EFFS) approach was introduced to overcome the single-method feature selection limitations, optimising the selection of factors contributing to flood susceptibility. Additionally, the study incorporates explainable artificial intelligence (XAI), namely, the Shapley Additive exPlanations (SHAP) model, to enhance the transparency and interpretability of the modelling results. The models’ performance was evaluated using various statistical measures on a testing dataset. The ANN model demonstrated a superior performance, outperforming the RF and the SVM. SHAP analysis identified rainwater collectors, land surface temperature (LST), digital elevation model (DEM), soil, river buffers, and normalized difference vegetation index (NDVI) as contributors to flood susceptibility, making them more understandable and actionable for stakeholders. The findings highlight the need for tailored flood management strategies, offering a novel approach to urban flood forecasting that emphasises predictive power and model explainability.

Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flood are different for every watershed as they depend upon the characteristics of each watershed. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of ensemble model showed 99% accuracy in training and 98% accuracy in testing datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.

Aside from Ramsar Convention awareness programs, the concept of wetlands is mostly ignored in developing countries. Wetland ecosystems are essential to hydrological cycles, ecosystem diversity, climatic change, and economic activity. Under the Ramsar Convention, there are 2414 wetlands that are internationally recognized, and Pakistan is home to 19 of them. The major goal of this study is to use the satellite image technology to locate Pakistan’s underutilized wetlands (Borith, Phander, Upper Kachura, Satpara, and Rama Lakes). The other goals are to understand how these wetlands are affected by climate change, ecosystem change, and water quality. We used analytical techniques including supervised classification and Tasseled Cap Wetness to identify the wetlands. To find changes caused by climate change, Quick Bird high-resolution images was used to create the change detection index. Tasseled Cap Greenness and the Normalized Difference Turbidity Index were also used to assess the water quality and changes in the ecology in these wetlands. Sentinel-2 was used to analyze data from 2010 and 2020. ASTER DEM was also used to do a watershed analysis. The land surface temperature (°C) of a few selected wetlands was calculated using Modis data. Rainfall (mm) data was taken from PERSIANN (precipitation estimation from remotely sensed information using artificial neural networks) databases. Results indicated that in 2010, the water content of Borith, Phander, Upper Kachura, Satpara, and Rama Lakes was 22.83%, 20.82%, 22.26%, 24.40%, and 22.91%. While in 2020, these lakes’ water ratios are 21.33%, 20.65%, 21.76%, 23.85%, and 22.59%, respectively. Therefore, the competent authorities must take precautions to ensure that these wetlands are preserved in the future in order to improve the dynamics of the ecosystem.

This study undertook an assessment of 24 physiochemical parameters at over 1094 sites to compute the water quality index (WQI) across the upper and central Punjab regions of Pakistan. Prior to the WQI calculation, an analytical hierarchy process (AHP) was employed to assign specific weights to each water quality parameter. The categorization of WQI into distinct classes was achieved by constructing a pairwise matrix based on their relative importance utilizing Saaty’s scale. Additionally, the groundwater quality status for irrigation and drinking purposes across various zones in the study area was delineated through the integration of WQI and geostatistical methodologies. The findings revealed discernible heavy metal issues in the Lahore division, with emerging microbiological contamination across the entire study region, potentially attributed to untreated industrial effluent discharge and inadequately managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions fell within the marginal to unfit categories, indicating water quality concerns. In contrast, the indices for other divisions were in the medium class, suggesting suitability for drinking purposes. Scenario analysis for developing mitigation strategies indicated that primary treatment before wastewater disposal could rehabilitate 9% of the study area, followed by secondary (35%) and tertiary (41%) treatments. Microbiological contamination (27%) emerged as the predominant challenge for water supply agencies. Given the current trajectory of water quality deterioration, access to potable water is poised to become a significant public concern. Consequently, government agencies are urged to implement appropriate measures to enhance overall groundwater quality for sustainable development.

Floods are among the most destructive natural hazards. Therefore, their prediction is pivotal for flood management and public safety. Factors contributing to flooding are different for every region as they depend upon the characteristics of each region. Therefore, this study evaluated the factors contributing to flood and the precise location of high and very high flood susceptibility regions in Karachi. A new ensemble model (LR-SVM-MLP) is introduced to develop the susceptibility map and evaluate influencing factors. This ensemble model was formed by employing a stacking ensemble on Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). A spatial database was generated for the Karachi watershed, which included; twelve conditioning factors as independent variables, 652 flood points and the same number of non-flood points as dependent variables. This data was then randomly divided into 70% and 30% to train and validate models, respectively. To analyse the collinearity among factors and to scrutinize each variable's predictive power, multicollinearity test and Information Gain Ratio were applied, respectively. After training, the models were evaluated on various statistical measures and compared with benchmark models. Results revealed that the proposed ensemble model outperformed Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) and produced a precise and accurate map. Results of the ensemble model showed 99% accuracy in training and 98% accuracy in validation datasets. This ensemble model can be used by flood management authorities and the government to contribute to future research studies.