Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management.

In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. The urgent need for rapid and accurate monitoring and assessment of water quality requires sophisticated, data-driven solutions for better decision-making in water management. This study aims to develop optimised data-driven models for comprehensive water quality assessment to enable informed decisions that are critical for sustainable water resources management. We used an entropy-weighted arithmetic technique to calculate the Water Quality Index (WQI), which integrates the World Health Organization (WHO) standards for various water quality parameters. Our methodology incorporated advanced machine learning (ML) models, including decision trees, random forests (RF) and correlation analyses to select features essential for identifying critical water quality parameters. We developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling. Interpretation of these models was achieved through a three-pronged explainable artificial intelligence (XAI) approach: model diagnosis with residual analysis, model parts with permutation-based feature importance and model profiling with partial dependence plots (PDP), accumulated local effects (ALE) plots and individual conditional expectation (ICE) plots. The quantitative results revealed insightful findings: fluoride and residual chlorine had the highest and lowest entropy weights, respectively, indicating their differential effects on water quality. Over 35% of the water samples were categorised as 'unsuitable' for consumption, highlighting the urgency of taking action to improve water quality. Amongst the optimised models, the Random Forest (model 79) and the Deep Neural Network (model 81) proved to be the most effective and showed robust predictive abilities with R2 values of 0.96 and 0.97 respectively for testing dataset. Model profiling as XAI highlighted the significant influence of key parameters such as nitrate, total hardness and pH on WQI predictions. These findings enable targeted water quality improvement measures that are in line with sustainable water management goals. Therefore, our study demonstrates the potential of advanced, data-driven methods to revolutionise water quality assessment in Saudi Arabia. By providing a more nuanced understanding of water quality dynamics and enabling effective decision-making, these models contribute significantly to the sustainable management of valuable water resources.

Applying deep learning to manage urban ecosystems in arid Abha, Saudi Arabia: Remote sensing-based modelling for ecological condition assessment and decision-making.

This study aims to quantitatively and qualitatively assess the impact of urbanisation on the urban ecosystem in the city of Abha, Saudi Arabia, by analysing land use changes, urbanisation processes and their ecological impacts. Using a multidisciplinary approach, a novel remote sensing-based urban ecological condition index (RSUSEI) will be developed and applied to assess the ecological status of urban surfaces. Therefore, the identification and quantification of urbanisation is important. To do so, we used hyper-tuned artificial neural network (ANN) as well as Land Cover Change Rate (LCCR), Land Cover Index (LCI) and Landscape Expansion Index (LEI). For the development of (RSUSEI), we have used four advanced models such as fuzzy Logic, Principle Component Analysis (PCA), Analytical Hierarchy Process (AHP) and fuzzy Analytical Hierarchy Process (FAHP) to integrate various ecological parameters. In order to obtain more information for better decision making in urban planning, sensitivity and uncertainty analyses based on a deep neural network (DNN) were also used. The results of the study show a multi-layered pattern of urbanisation in Saudi Arabian cities reflected in the LCCR, indicating rapid urban expansion, especially in the built-up areas with an LCCR of 0.112 over the 30-year period, corresponding to a more than four-fold increase in urban land cover. At the same time, the LCI shows a remarkable increase in 'built-up' areas from 3.217% to 13.982%, reflecting the substantial conversion of other land cover types to urban uses. Furthermore, the LEI emphasises the complexity of urban growth. Outward expansion (118.98 km2), Edge-Expansion (95.22 km2) and Infilling (5.00 km2) together paint a picture of a city expanding outwards while filling gaps in the existing urban fabric. The RSUSEI model shows that the zone of extremely poor ecological condition covers an area of 157-250 km2, while the natural zone covers 91-410 km2. The DNN based sensitivity analysis is useful to determine the optimal model, while the integrated models have lower input parameter uncertainty than other models. The results of the study have significant implications for the management of urban ecosystems in arid areas and the protection of natural habitats while improving the quality of life of urban residents. The RSUSEI model can be used effectively to assess urban surface ecology and inform urban management techniques.