In arid regions like the Mornag region of Tunisia, groundwater is a critical resource for agriculture, drinking water, and ecosystem sustainability. However, the degradation of groundwater quality due to high levels of potentially toxic elements (PTEs) poses significant risks to human health and the environment. Addressing this issue is crucial for ensuring water security and protecting public health in such vulnerable regions. This study aims to assess groundwater quality, identify contamination sources, and evaluate the associated health risks using an integrated approach combining geochemical modeling, machine learning (ML) techniques, and water quality indices. The methodology involved geochemical modeling, detection of recharge sources, and the development of an optimized model using artificial neural networks (ANN) to forecast integrated weight water quality indices (IWQI), carcinogenic risk (CR), hazard risk (HI), and heavy metal pollution index, metal index, and contamination degree (HPI, MI, and C_d) related to PTEs. The findings revealed that the aquifer system contains groundwater of paleo-meteoric origin, influenced by cation exchange, mixing, and water–rock interactions. The Mornag groundwaters were oversaturated with calcite, dolomite, magnesite, and aragonite minerals, while undersaturated with halite, epsomite, gypsum, and anhydrite minerals. Based on IWQI values, 36.84% of water samples fell within the “no restriction” class, indicating excellent water quality, while 26.31% were classified as “low restriction,” suitable for irrigation without threatening soil fertility or plant production. However, 13.15% of samples fell within the “moderate restriction” class, and 23.7% exhibited high to severe restrictions, indicating significant deterioration in irrigation water quality. The HPI indicator showed no significant contamination from PTEs such as Cd, Cr, F, Fe, As, and Ni. Most samples (47.36%) were classified as “very pure” based on MI values, with 42.10% as “pure,” and only 5.26% slightly affected by PTEs. However, two samples from the western part of the Mornag region showed elevated MI values, suggesting potential geogenic or anthropogenic contamination sources. Chromium was identified as a significant cancer risk for both adults and children in many water samples. The use of Monte Carlo simulation proved effective in predicting dermal carcinogenic risks. Additionally, ANN models demonstrated high accuracy in predicting all indices, with R² values ranging from 0.90 to 0.98 during training and 0.86 to 0.94 during testing. These findings provide valuable insights for policymakers and stakeholders to implement effective groundwater management and mitigation strategies in arid regions.

Accurately predicting crop yield in different environmental conditions and irrigation regimes plays a vital role in optimizing agricultural practices and ensuring food security. This research aims to develop a tomato yield estimation model using machine learning (ML) models such as artificial neural network (ANN), random forest (RF), and decision tree (DT) models, based on climate and plant variables. To enhance the models' performance and prevent overfitting, a hyper-parameter tuning technique was implemented through cross-validation. Field experiments were conducted during the 2022 and 2023 growing seasons, implementing three irrigation regimes: 100%, 75%, and 50% of the full irrigation requirements (FIR). The results demonstrate that the ML models effectively captured the relationship between input variables and tomato production under deficit irrigation, achieving a desirable level of accuracy. Impressively, these models showcased predictive prowess 3-7 weeks before the harvest period. The artificial neural network models yielded an average root mean squared error (RMSE) of 3.9 ton/ha and a coefficient of determination (R2) of 0.95 for tomato yield prediction. The RF model displayed even better accuracy, with an RMSE of 3.50 ton/ha and an R2 of 0.96. The DT model forecasted tomato yield with an RMSE of 3.77 ton/ha and an R2 of 0.95. These findings highlight the practicality and reliability of utilizing climate and plant variables in combination with machine learning models to effectively manage tomato crop production, particularly when facing limited water availability for irrigation.

Major international nutrition organizations are becoming increasingly concerned about global agriculture production. In particular, food insecurity has emerged in Egypt as a result of population expansion and rising food demand. Climate change and its variability are significant factors to the worldwide food insecurity problem. Therefore, cutting-edge techniques like IOT and machine learning which have shown its worth in several ways are needed by farmers and agricultural decision-makers to assist it in making timely judgments that will affect the quality of agricultural harvests. A novel open-source technology based on Arduino Board has been developed to predict onion crop irrigation needs. By utilizing three irrigation levels (100%, 85%, and 70% ETc) and integrating machine learning, the system predicts onion output in Talkha city, Egypt for 2023/2024. Meteorological and agronomic data are combined to aid farmers and decision-makers in predicting seasonal onion yields. Our system was constructed using Artificial Neural Network (ANN), Random Forest (RF), and Decision trees (DT) models. We found that, Using Arduino device at T85% and T70% helps to rationalize amount of water by 13% and 28% comparing with T100%. Furthermore, Arduino design with algorithm model gain a good value for (CWP) by (14.5 and 16 kg/m3) at T85% and T70%. Additionally, the results indicated that the ANN model works well during the testing phase, with a R2 of 94.3%, while the RF model and DT perform 90% and 86.1%, respectively. Our results showcase the effectiveness of the technology in enhancing agricultural decision-making and crop management.

This study presents an integrated computational framework for quantifying industrial impacts on marine ecosystems through the combined assessment of multiple environmental quality indices. The Aquatic Water Quality Index (AWQI) and four diagnostic pollution indices, namely the Heavy Metal Pollution Index (HPI), Metal Index (MI), Degree of Contamination (C_d), and Pollution Index (PI), were applied across 23 offshore sites in Mesaieed Industrial City, Qatar, to establish a high-resolution baseline for evaluating the effects of industrial effluents and brine discharge. Multivariate statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), identified Cr, Pb, Mn, Ni, and Zn as the principal drivers of water quality variability, effectively distinguishing anthropogenic influences from natural background conditions. To enable rapid and automated marine environmental assessment, three machine learning models—Artificial Neural Networks (ANN), Random Forest (RF), and Decision Trees (DT)—were developed and evaluated for predicting the investigated indices. Model performance was assessed through rigorous training–testing validation and the Diebold–Mariano test. The results demonstrated that model selection significantly influences predictive accuracy. Among the evaluated algorithms, RF achieved the highest predictive performance for AWQI (R² = 0.88) and C_d (R² = 0.92), whereas ANN performed best for HPI (R² = 0.89), and DT yielded the most accurate predictions for MI (R² = 0.82). Despite the index-specific strengths of individual models, RF emerged as the most robust and generalizable approach, consistently providing superior performance across heterogeneous environmental datasets. The proposed framework advances marine water quality assessment from conventional descriptive monitoring toward a proactive, data-driven paradigm, offering a scalable and cost-effective decision support tool for environmental management, pollution mitigation, and evidence-based coastal governance in industrialized coastal regions.

Given its availability amid the increasing scarcity of surface freshwater, groundwater has become a vital and increasingly relied-upon resource, especially in semi-arid and arid regions. Thus, to ensure groundwater complies with standards before use, continuous monitoring and comprehensive quality assessment are essential. This study aimed to assess the quality of groundwater (GW) in the Skikda aquifer, northeastern Algeria, for irrigation using irrigation water quality indices (IWQIs), multivariate statistical analysis,and machine learning algorithms (MLAs): Random Forest regression (RF), Extreme Gradient Boosting regression (XGBR), and Adaptive Boosting Regression (ABR), integrated with SHAP analysis. Forty-four groundwater samples were collected from the study area during summer and winter seasons andanalysed for temperature, pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), and concentrations of calcium (Ca²⁺), magnesium (Mg²⁺),sodium (Na⁺),potassium (K⁺), chloride (Cl⁻),bicarbonate (HCO₃⁻), sulfate (SO₄²⁻), and nitrate (NO₃⁻).The dominating hydrochemical facies in the study area were Mg-Ca-SO ₄ , accompanied by the Sodium-Chloride (Na-Cl).Principle Component Analysis (PCA) for summer and winter datasets identified four key components suggesting a strong correlation between variables and factors, with PCA indicating that geochemical processes, such as rock0water interaction and dissolution of evaporite minerals, control the groundwater’s chemical composition.Groundwater quality for irrigation varied across the samples, with most exhibiting moderate to high constraints based on IWQI. Sodium Adsorption Ratio (SAR) and Permeability Index (PI) suggested excellent to good water quality,while Sodium Percent (Na%) and Soluble Sodium Percentage (SSP) indicate a small but significant fraction of inappropriate samples.Magnesium Hazard (MH) and SSP indicated that most samples were safe.Compared to winter, summer samples showed slightly poorer quality (higher Na%, SSP, and lower IWQI), likely due toevaporative solute concentration. Random Forest (RF) modelshowed superior predictive accuracy for all Water Quality Indices (WQIs), with strong validation results for both seasons. These results highlight RF's effectiveness in predicting WQIs and highlight the influence of seasonal geochemical processes on groundwater quality, requiring the development of management strategies for sustainable irrigation.