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Freshwater salinisation and alkalinisation strongly depend on human and natural factors. We used an explainable machine learning approach to investigate the impact of natural and human factors on the salinity and alkalinity in rivers in the United States. The model achieved coefficient of determination of 0.89 for salinity predictions and 0.92 for alkalinity predictions. Shapley additive explanations revealed salinisation is driven mainly by human factors like population density (18 %) and impervious surface percentage (13 %) and natural factors like run-off (14 %). The alkalinisation is mainly influenced by natural processes such as runoff (36 %) and soil-pH (20 %).

This research explores the use of machine learning to predict the mechanical properties of cementitious materials enhanced with carbon nanotubes (CNTs). Specifically, the study focuses on estimating the elastic modulus and flexural strength of these novel composite materials, with the potential to significantly impact the construction industry. Seven key variables were analyzed including water-to-cement ratio, sand-to-cement ratio, curing age, CNT aspect ratio, CNT content, surfactant-to-CNT ratio, and sonication time. Artificial neural network, support vector regression, and histogram gradient boosting, were used to predict these mechanical properties. Furthermore, a user-friendly formula was extracted from the neural network model. Each model performance was evaluated, revealing the neural network to be the most effective for predicting the elastic modulus. However, the histogram gradient boosting model outperformed all others in predicting flexural strength. These findings highlight the effectiveness of the employed techniques, in accurately predicting the properties of CNT-enhanced cementitious materials. Additionally, extracting formulas from the neural network provides valuable insights into the interplay between input parameters and mechanical properties.

Conventional machine learning techniques in diagnosing cardiovascular disease have a limitation owing to the lack of interpretability of models. This study utilised an explainable machine learning approach to predict the likelihood of having CVD. Four machine learning models were employed for CVD diagnosis: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), and Extreme Gradient Boost (XGB). Shapley Additive Explanations (SHAP) were used to provide reasoning for the models' predictions. Using these models and explanations, a user interface was developed to assist in diagnosing CVD. All four classification models demonstrated good accuracy in diagnosing CVD, with the KNN model showcasing the best performance (Accuracy: 71 %). SHAP provided the reasoning behind KNN predictions, and the predictive interface was developed by embedding these explanations to provide transparency behind the model's decisions.

Geopolymer concrete is a sustainable and eco-friendly substitute for traditional OPC (Ordinary Portland Cement) based concrete, as it reduces greenhouse gas emissions. With various supplementary cementitious materials, the compressive strength of geopolymer concrete should be accurately predicted. Recent studies have applied deep learning techniques to predict the compressive strength of geopolymer concrete yet its hidden decision-making criteria diminish the end-users’ trust in predictions. To bridge this gap, the authors first developed three deep learning models: an artificial neural network (ANN), a deep neural network (DNN), and a 1D convolution neural network (CNN) to predict the compressive strength of slag ash-based geopolymer concrete. The performance indices for accuracy revealed that the DNN model outperforms the other two models. Subsequently, Shapley additive explanations (SHAP) were used to explain the best-performed deep learning model, DNN, and its compressive strength predictions. SHAP exhibited how the importance of each feature and its relationship contributes to the compressive strength prediction of the DNN model. Finally, the authors developed a novel DNN-based open-source software interface to predict the mix design proportions for a given target compressive strength (using inverse modeling technique) for slag ash-based geopolymer concrete. Additionally, the software calculates the Global Warming Potential (kg CO2 equivalent) for each mix design to select the mix designs with low greenhouse emissions.

Water quality assessment and prediction play crucial roles in ensuring the sustainability and safety of freshwater resources. This study aims to enhance water quality assessment and prediction by integrating advanced machine learning models with XAI techniques. Traditional methods, such as the water quality index, often require extensive data collection and laboratory analysis, making them resource-intensive. The weighted arithmetic water quality index is employed alongside machine learning models, specifically RF, LightGBM, and XGBoost, to predict water quality. The models' performance was evaluated using metrics such as MAE, RMSE, R2, and R. The results demonstrated high predictive accuracy, with XGBoost showing the best performance (R2 = 0.992, R = 0.996, MAE = 0.825, and RMSE = 1.381). Additionally, SHAP were used to interpret the model's predictions, revealing that COD and BOD are the most influential factors in determining water quality, while electrical conductivity, chloride, and nitrate had minimal impact. High dissolved oxygen levels were associated with lower water quality index, indicative of excellent water quality, while pH consistently influenced predictions. The findings suggest that the proposed approach offers a reliable and interpretable method for water quality prediction, which can significantly benefit water specialists and decision-makers.

In a recent paper by Sajindra et al. [1], the soil nutrient levels, specifically nitrogen, phosphorus, and potassium, in organic cabbage cultivation were predicted using a deep learning model. This model was designed with a total of four hidden layers, excluding the input and output layers, with each hidden layer meticulously crafted to contain ten nodes. The selection of the tangent sigmoid transfer function as the optimal activation function for the dataset was based on considerations such as the coefficient of correlation, mean squared error, and the accuracy of the predicted results. Throughout this study, the objective is to justify the tangent sigmoid transfer function and provide mathematical justification for the obtained results. • This paper presents the comprehensive methodology for the development of deep neural network for predict the soil nutrient levels. • Tangent Sigmoid transfer function usage is justified in predictions. • Methodology can be adapted to any similar real-world scenarios.

This study introduces the first-ever self-explanatory interface for diagnosing diabetes patients using machine learning. We propose four classification models (Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGB)) based on the publicly available diabetes dataset. To elucidate the inner workings of these models, we employed the machine learning interpretation method known as Shapley Additive Explanations (SHAP). All the models exhibited commendable accuracy in diagnosing patients with diabetes, with the XGB model showing a slight edge over the others. Utilising SHAP, we delved into the XGB model, providing in-depth insights into the reasoning behind its predictions at a granular level. Subsequently, we integrated the XGB model and SHAP’s local explanations into an interface to predict diabetes in patients. This interface serves a critical role as it diagnoses patients and offers transparent explanations for the decisions made, providing users with a heightened awareness of their current health conditions. Given the high-stakes nature of the medical field, this developed interface can be further enhanced by including more extensive clinical data, ultimately aiding medical professionals in their decision-making processes.

Chronic Kidney Disease (CKD) is increasingly recognised as a major health concern due to its rising prevalence. The average survival period without functioning kidneys is typically limited to approximately 18 days, creating a significant need for kidney transplants and dialysis. Early detection of CKD is crucial, and machine learning methods have proven effective in diagnosing the condition, despite their often opaque decision-making processes. This study utilised explainable machine learning to predict CKD, thereby overcoming the 'black box' nature of traditional machine learning predictions. Of the six machine learning algorithms evaluated, the extreme gradient boost (XGB) demonstrated the highest accuracy. For interpretability, the study employed Shapley Additive Explanations (SHAP) and Partial Dependency Plots (PDP), which elucidate the rationale behind the predictions and support the decision-making process. Moreover, for the first time, a graphical user interface with explanations was developed to diagnose the likelihood of CKD. Given the critical nature and high stakes of CKD, the use of explainable machine learning can aid healthcare professionals in making accurate diagnoses and identifying root causes.

Climate change is a serious global issue causing more extreme weather patterns, resulting in more frequent and severe events like urban flooding. This review explores the connection between climate change and urban flooding, offering statistical, scientific, and advanced perspectives. Analyses of precipitation patterns show clear changes, establishing a strong link between climate change and the heightened intensity of global rainfall events. Hydrological modeling and case studies provide compelling scientific evidence attributing urban flooding to climate-induced changes. Urban infrastructure, including transportation networks and critical facilities, is increasingly vulnerable, worsening the impact on people's lives and businesses. Examining adaptation strategies, the review highlights the need for resilient urban planning and the integration of green infrastructure. Additionally, it delves into the role of advanced technologies, such as artificial intelligence, remote sensing, and predictive modeling, in improving flood prediction, monitoring, and management. The socio-economic implications of urban flooding are discussed, emphasizing unequal vulnerability and the importance of inclusive policies. In conclusion, the review stresses the urgency of addressing climate-induced urban flooding through a holistic analysis of statistical trends, scientific evidence, infrastructure vulnerabilities, and adaptive measures. The integration of advanced technologies and a comprehensive understanding of socio-economic implications are essential for developing effective, inclusive strategies. This review serves as a valuable resource, offering insights for policymakers, researchers, and practitioners striving for climate-resilient urban futures amid escalating climate change impacts.

Streamflow forecasting is crucial for effective water resource planning and early warning systems, especially in regions with complex hydrological behaviors and uncertainties. While machine learning (ML) has gained popularity for streamflow prediction, many studies have overlooked the predictability of future events considering anthropogenic, static physiographic, and dynamic climate variables. This study, for the first time, used a modified generative adversarial network (GAN) based model to predict streamflow. The adversarial training concept modifies and enhances the existing data to embed featureful information enough to capture extreme events rather than generating synthetic data instances. The model was trained using (sparse data) a combination of anthropogenic, static physiographic, and dynamic climate variables obtained from an ungauged basin to predict monthly streamflow. The GAN-based model was interpreted for the first time using local interpretable model-agnostic explanations (LIME), explaining the decision-making process of the GAN-based model. The GAN-based model achieved R2 from 0.933 to 0.942 in training and 0.93–0.94 in testing. Also, the extreme events in the testing period have been reasonably well captured. The LIME explanations generally adhere to the physical explanations provided by related work. This approach looks promising as it worked well with sparse data from an ungauged basin. The authors suggest this approach for future research work that focuses on machine learning-based streamflow predictions.

In this study, we used four different machine learning models - artificial neural network (ANN), support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF) - to predict the natural period of reinforced concrete frame structures with masonry infill walls. To interpret the models and their predictions, we employed Shapley additive explanations (SHAP), Local interpretable model-agnostic explanations (LIME), and partial dependency plots (PDP). All models showed good accuracy in predicting the fundamental period (T). The post-hoc explanations provided insights into (a) the importance of each feature, (b) their interaction, and (c) the underlying reasoning behind the predictions. For the first time, we have created a graphical interface that can predict the value of T along with its SHAP explanation. This interface can be useful in manually optimizing the design of reinforced concrete frame structures with masonry infill walls. However, the local explanations from SHAP and LIME exhibited significant discrepancies, and LIME underestimated the feature importance of dominant features compared to SHAP. These discrepancies observed in the explanations highlight the need for further research in the field of explainable artificial intelligence (XAI) in structural engineering.

The number of expressway road accidents in Sri Lanka has significantly increased (by 20%) due to the expansion of the transport network and high traffic volume. It is crucial to identify the causes of these crashes for effective road safety management. However, traditional statistical methods may be insufficient due to their inherent assumptions. This study utilized explainable machine learning to investigate the factors that affect the severity of traffic crashes on expressways. The study evaluated two groups of traffic crashes: fatal or severe crashes, and other crashes that included non-severe injuries or only property damage. Five factors that contribute to crashes were analyzed: road surface condition, road alignment, location, weather condition, and lighting effect. Four machine learning models (Random Forest (RF), Decision Tree (DT), extreme gradient boosting (XGB), K-Nearest Neighbor (KNN)) were developed and compared with Logistic Regression (LR) using 223 training and 56 testing data instances. The study revealed that the machine learning algorithms provided more accurate predictions than the LR model. To explain the machine learning models, Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were used. These methods revealed that all five features decreased the possibility of occurrence of fatal accidents. SHAP and LIME explanations confirmed the known interactions between factors influencing crash severity in expressway operational conditions. These explanations increase the trust of end-users and domain experts on machine learning models. Furthermore, the study concluded that using explainable machine learning methods is more effective than traditional regression analysis in evaluating safety performance. Additionally, the results of the study can be utilized to improve road safety by providing accurate explanations for decision-making processes for black-box models.

Heat transfer through roof slabs significantly increases the operational energy consumption of buildings. Therefore, passive implementations are necessary to improve the thermal performance of roof slabs in tropical climates. This paper presents a novel roof slab insulation using expanded polystyrene (EPS) based lightweight concrete panels. The workflow consists of field experiments and numerical simulations performed in Design Builder. Moreover, we offered a holistic life-cycle approach to investigate the economic and environmental feasibility of alternate forms. Accordingly, the roof slab with 75 mm EPS insulation and a white exposed surface performed satisfactorily. Corresponding decrease in life cycle cost, carbon emission (kgCO2e), and operational energy consumption were 8.3%, 20%, and 41%, respectively. The overall eco-efficiency index (EEI) implies that the recommended insulation system is environmentally and economically feasible under tropical climatic conditions. Further, manufacturing EPS concrete is eco-friendly since it reduces EPS waste content which does not decay through natural means.

Machine learning (ML) techniques are often employed for the accurate prediction of the compressive strength of concrete. Despite higher accuracy, previous ML models failed to interpret the rationale behind predictions. Model interpretability is essential to appeal to the interest of domain experts. Therefore, overcoming research gaps identified, this research study proposes a way to predict the compressive strength of concrete using supervised ML algorithms (Decision tree, Extra tree, Adaptive boost (AdaBoost), Extreme gradient boost (XGBoost), Light gradient boosting method (LGBM), and Laplacian Kernel Ridge Regression (LKRR). Alternatively, SHapley Additive exPlainations (SHAP) – a novel black-box interpretation approach - was employed to elucidate the predictions. The comparison revealed that tree-based algorithms and LKRR provide acceptable accuracy for compressive strength predictions. Moreover, XGBoost and LKRR algorithms evinced superior performance (R = 0.98). According to SHAP interpretation, XGBoost predictions capture complex relationships among the constituents. On the other hand, SHAP provides unified measures on feature importance and the impact of a variable for a prediction. Interestingly, SHAP interpretations were in accordance with what is generally observed in the compressive behavior of concrete, thus validating the causality of ML predictions.