A novel approach on water resource management with Multi-Criteria Optimization and Intelligent Water Demand Forecasting in Saudi Arabia

An instructional emperor pigeon optimization (IEPO) based DeepEnrollNet for university student enrolment prediction and retention recommendation

Academic institutions face increasing challenges in predicting student enrollment and managing retention. A comprehensive strategy is required to track student progress, predict future course demand, and prevent student churn across various disciplines. Institutions need an effective method to predict student enrollment while addressing potential churn. The existing approaches are often inadequate in handling both numerical and textual data, limiting the ability to provide personalized retention strategies. We propose an innovative framework that combines deep learning with recommender systems for student enrollment prediction and churn prevention. The framework integrates advanced preprocessing techniques for both numeric and textual data. Feature extraction is performed with statistical measures for numeric data, and advanced text techniques like GloVe embeddings, Latent Dirichlet Allocation (LDA) for topic modeling, and SentiWordNet for sentiment analysis. A weighted feature fusion approach combines these features, and the optimal features are selected using the Pythagorean fuzzy AHP with a Hybrid Optimization approach, specifically the Instructional Emperor Pigeon Optimization (IEPO). The DeepEnrollNet model, a hybrid CNN-GRU-Attention QCNN architecture, is used for enrollment prediction, while Deep Q-Networks (DQN) are applied to generate actionable retention recommendations. This comprehensive methodology improves predictive accuracy for student enrolment and provides tailored strategies to enhance retention by addressing both text and numeric data in a unified framework. The DeepEnrollNet has the minimum MSE of 0.218978, MSRE of 0.216445, a NMSE of 0.232453, RMSE of 0.23213, and MAPE of 0.218754.

Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences

OBJECTIVE

This study delves into the impact of urban meteorological elements-specifically, air temperature, relative humidity, and atmospheric pressure-on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius), air pressure (in hectopascals), and relative humidity (in percent) were used for the statistical period 2017-2023. Various models, including the correlation coefficient, generalized additive models (GAM), generalized linear models (GLM), and support vector machines (SVM), were employed to scrutinize the data.

RESULTS

Water consumption increases due to the influence of relative humidity and air pressure when the temperature variable is controlled. Under specific air temperature conditions, elevated air pressure coupled with high relative humidity intensifies the response of water consumption to variations in these elements. Water consumption exhibits heightened sensitivity to high relative humidity and air pressure compared to low levels of these factors. During winter, when a western low-pressure air mass arrives and disrupts normal conditions, causing a decrease in pressure and temperature, urban water consumption also diminishes. The output from the models employed in this study holds significance for enhancing the prediction and management of water resource consumption.

Water resources availability, sustainability and challenges in the GCC countries: An overview

The Gulf Cooperation Council (GCC) countries include Bahrain, Kuwait, Saudi Arabia, Sultanate of Oman, Qatar, and United Arab Emirates. The GCC countries are located in an arid region. They have limited renewable water resources due to scarcity of rainfall. This paper provides the most recent and accurate quantitative and qualitative assessment of available water resources and demands in the GCC countries. The annual renewable surface water, desalinated capacity, wastewater treatment capacity, and per capita water consumption in the GCC countries are assessed. The possible impacts of climate change are discussed. The annual renewable surface water, desalinated capacity, and wastewater treatment capacity in the GCC countries are estimated as 4.14, 26.4, and 10.07 billion m3, respectively. The average per capita water consumption is around 550 l/d. The GCC countries have high water footprints. Although tertiary treated, the reuse of treated wastewater is limited and constrained to the development of forests and green areas. Water demand trends reveal the need for the implementation of sustainable water management programs. Emerging solutions include imposing a new tariff system, improving irrigation efficiency, controlling agricultural water consumption, developing innovative desalination and treatment technologies, maximizing treated wastewater utilization and rainwater harvesting, eliminating leakage in networks, and considering virtual water concepts in the water budget and planning.

Methods for Modeling and Optimizing the Delayed Coking Process in a Fuzzy Environment

Technological objects and processes are often characterized by fuzzy initial information necessary for developing their models and optimization. The purpose of the study is to develop a method for synthesizing linguistic models of fuzzy described objects and a heuristic method for solving the multicriteria optimization problem in a fuzzy environment. Based on the expert assessments and logical rules of conditional inference, a method for synthesizing linguistic models was developed for describing processes with fuzzy input and output parameters. To solve the problem of multicriteria optimization, a heuristic method based on the modification and combination of various optimality principles is proposed. Coking reactor models were developed by modifying the successive regression inclusion method and the least squares method. Linguistic models of the delayed coking process were developed in the Fuzzy Logic Toolbox, allowing to evaluate the coke quality depending on the temperature and pressure of coking reactors. Using the proposed heuristic method, the problem of two-criteria optimization of the delayed coking process with fuzzy constraints is solved. The results confirm the advantages of the proposed fuzzy approach compared with the well-known approaches. Unlike them, the proposed method allows making adequate decisions in a fuzzy environment by maximizing the use of available fuzzy information.

Water demand in watershed forecasting using a hybrid model based on autoregressive moving average and deep neural networks

Increasing water demand is exacerbating water shortages in water-scarce regions (such as India, China, and Iran). Effective water demand forecasting is essential for the sustainable management of water supply systems in watersheds. To alleviate the contradiction between water supply and demand in the basin, with water demand for economic growth as the main target, a hybrid moving autoregressive and deep neural network model (ARMA-DNN) was developed in this study, and four commonly used statistical indicators (MAE, RMSE, MSE, and R²) were selected to evaluate the performance of the model. Finally, the validity and practicality of the model were verified by taking the Minjiang River basin in China as an example. The results show that (a) the model can predict future water demand more accurately under the conditions of actual water consumption changes, (b) the ideal agricultural production in the Minjiang River Basin is predicted to be reached 2.26 × 10⁹t in 2021, and (c) the highest industrial economic efficiency in Chengdu is 1.51 × 10⁹yuan, while water satisfaction reaches 102%. This means that effective water demand forecasting can alleviate water demand conflicts under climate change conditions to a certain extent. At the same time, watershed managers can develop different water allocation schemes based on the prediction results of the hybrid ARMA-DNN model.