Dams significantly impact the environment, industries, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely to contribute to sustainable water management by enabling efficient water allocation among sectors, proactive drought management, controlled flood risk mitigation, and preservation of downstream ecological integrity. Our research suggests that combining physical models of water inflow and outflow “such as evapotranspiration using the Penman–Monteith equation, along with parameters like water consumption, solar radiation, and rainfall” with data-driven models based on historical reservoir data is crucial for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted statistical tests on the mean absolute percentage errors of the models. Consequently, we demonstrated the impact of physical model parameters on prediction performance and identified the best method for predicting dam occupancy levels by comparing it with findings from the scientific literature.

Drawing on impression management theory, this study investigates the presence of potential greenwashing in sustainability reports (SRs) by examining three textual dimensions: readability, sentiment, and boilerplate content. A novel three-dimensional categorization framework was developed to capture companies’ sustainability communication strategies. Using text mining and natural language processing techniques, we analyzed 105 English- language SRs published by companies listed on the BIST Sustainability Index between 2021 and 2023, comprising over 250,000 sentences. The findings reveal a general trend toward more readable and positive narratives; however, in some cases, this was accompanied by increased use of boilerplate language, suggesting impression management practices. Notably, a small group of companies consistently exhibited a high-risk communication profile indicative of potential greenwashing. This study contributes to the literature by integrating three complementary textual indicators into a unified model for detecting greenwashing behaviors. The results provide practical implications for regulators and stakeholders seeking to enhance the credibility and transparency of corporate sustainability reporting.

Dams significantly impact the environment, industry, residential areas, and agriculture. Efficient dam management can mitigate negative impacts and enhance benefits such as flood and drought reduction, energy efficiency, water access, and improved irrigation. This study tackles the critical issue of predicting dam occupancy levels precisely within the framework of Integrated Water Resource Management (IWRM). Our research proposes that combining the physical model of evapotranspiration using the Penman–Monteith equation with data-driven models based on historical reservoir data, weather data, and consumption data is essential for accurately predicting occupancy levels. We implemented various prediction models, including Random Forest, Extra Trees, Long Short-Term Memory, Orthogonal Matching Pursuit CV, and Lasso Lars CV. To strengthen our proposed model with robust evidence, we conducted a statistical test on the MAPE values. We achieved a remarkable accuracy in predicting the occupancy level one month ahead, with an error margin of just 1% using Extra Trees. This study represents a pioneering effort in providing guidance and proposing a hybrid model in this field.