Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)₁₂ but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

Water level forecasting in rivers, lakes, and reservoirs is crucial for effective water resource management, flood control, and environmental planning. This review examines the latest developments and trends in water level forecasting research from 2011-2024. A wide range of methods are explored, including traditional statistical models (ARIMA, regression) and advanced techniques like artificial neural networks (ANN), fuzzy logic, support vector machines (SVM), and deep learning models (LSTM). The study assesses the performance and accuracy of these applied models, analyzing their strengths and limitations in capturing water system dynamics and uncertainties. It investigates how data sources (hydrological, meteorological, historical) and variables (rainfall, evaporation, inflow) impact forecast accuracy. The significance of different variables for improving model predictive capabilities is determined. Spatiotemporal aspects are explored, examining model applicability across local, regional, and global scales. Approaches to quantifying and communicating uncertainties associated with probabilistic forecasting for decision-making are evaluated. Detailed analysis identifies proven model efficiencies, potential challenges, and suggests future research directions. By comprehensively reviewing recent water level forecasting literature, this study provides state-of-the-art knowledge on applying machine learning models for reservoir water level prediction. It guides water resource strategies, flood mitigation measures, and decision-making for sustainable water systems management. This review is a valuable resource for researchers and practitioners in hydrology and related fields.

Two-phase Darcy’s law is a well-known mathematical model used in the petrochemical industry. It predicts the fluid flow in reservoirs and can be used to optimize oil production using recent technology. Indeed, various models have been proposed for predicting oil recovery using injected nanofluids (NFs). Among them, numerical modeling is attracting the attention of scientists and engineers owing to its ability to modify the thermophysical properties of NFs such as density, viscosity, and thermal conductivity. Herein, a new model for simulating NF injection into a 3D porous media for enhanced oil recovery (EOR) is investigated. This model has been developed for its ability to predict oil recovery across a wide range of temperatures and volume fractions (VFs). For the first time, the model can examine the changes and effects of thermophysical properties on the EOR process based on empirical correlations depending on two variables, VF and inlet temperature. The governing equations obtained from Darcy’s law, mass conservation, concentration, and energy equations were numerically evaluated using a time-dependent finite-element method. The findings indicated that optimizing the temperature and VF could significantly improve the thermophysical properties of the EOR process. We observed that increasing the inlet temperature (353.15 K) and volume fraction (4%) resulted in better oil displacement, improved sweep efficiency, and enhanced mobility of the NF. The oil recovery decreased when the VF (>4%) and temperature exceeded 353.15 K. Remarkably, the optimal VF and inlet temperature for changing the thermophysical properties increased the oil production by 30%.

The global demand for coconut and coconut-based products has increased rapidly over the past decades. Coconut price continues to fluctuate; thus, it is not easy to make predictions. Good price modelling is important to accurately predict the future coconut price. Several studies have been conducted to predict the price of coconut using various models. One of the most important and widely used models in time series forecasting is the autoregressive integrated moving average (ARIMA). Price fluctuations are, however, considered a problem with uncertain behaviour. The existing ARIMA time series model is unsuitable for solving this problem, because of the nonlinear relationship. Artificial neural networks (ANN) have been an effective method in solving nonlinear data pattern problems in the last two decades. Therefore ARIMA-NARNET is considered a universal approach to forecasting the coconut price. The aim of the study is to establish a linear and nonlinear model in time series to forecast coconut prices. The ability of a hybrid approach that combines ARIMA and NARNET(ANN) models is investigated. Based on the experimental study, the experimental results show that the proposed method ARIMA- NARNET, is better at forecasting the price of coconut, an agriculture commodity, than both the ARIMA model and NARNET models. The expected benefits of the proposed forecasting model can help farmers, exporters, and the government to maximize profits in the future.

The increasing integration of distributed renewable energy sources, particularly solar photovoltaics (PV) and wind power, presents technical and economic challenges in achieving stable and cost-effective energy systems. This study evaluates the techno-economic feasibility of a hybrid renewable configuration integrating solar PV, wind, and battery energy storage components for a suburban case study in Keperra, Queensland (27°24.8′S, 152°57.5′E). Three system architectures were simulated and optimised using HOMER Pro, taking into account realistic weather and load conditions and using Net Present Cost (NPC) and Levelized Cost of Energy (LCOE). The optimal configuration; which included about 404 kW of solar power capacity, seven G10 wind turbines, and one modular lithium-ion battery with about 31 hours of autonomy; had a 100% renewable fraction, an NPC of approximately $2.60 million and an LCOE of $0.886 per kWh (USD). The PV system produced about 662 MWh of energy per year, and the wind system produced about 75 MWh. The battery contributed the energy supply and demand over short periods with an annual throughput of about 110 MWh. The system retained stable operation, with the battery state of charge remained mostly between 60% and 100% throughout the year. The analysis showed that increasing storage beyond the optimal size provided limited economic improvement, which showed how efficient a PV-dominant system with moderate wind generation and little storage can be.