Forecasting hydrological parameters for reservoir system utilizing artificial intelligent models and exploring their influence on operation performance

Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique

To ease water scarcity, dynamic programming, stochastic dynamic programming, and heuristic algorithms have been applied to solve problem matters related to water resources. Development, operation, and management are vital in a reservoir operating policy, especially when the reservoir serves a complex objective. In this study, an attempt via metaheuristic algorithms, namely the Harris Hawks Optimisation (HHO) Algorithm and the Opposite Based Learning of HHO (OBL-HHO) are made to minimise the water deficit as well as mitigate floods at downstream of the Klang Gate Dam (KGD). Due to trade-offs between water supply and flood management, the HHO and OBL-HHO models have configurable thresholds to optimise the KGD reservoir operation. To determine the efficacy of the HHO and OBL-HHO in reservoir optimisation, reliability, vulnerability, and resilience are risk measures evaluated. If inflow categories are omitted, the OBL-HHO meets 71.49% of demand compared to 54.83% for the standalone HHO. The HHO proved superior to OBL-HHO in satisfying demand during medium inflows, achieving 38.60% compared to 20.61%, even though the HHO may have experienced water loss at the end of the storage level. The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. For resilience, the ABC scored 0.16, PSO scored 0.15, and real coded GA scored 0.14 whilst the binary-GA has the worst failure recovery algorithm with 0.09.

Linking river flow modification with wetland hydrological instability, habitat condition, and ecological responses

Flow modification pursuing dams is widely found. Some works also focused on its impact on floodplain wetland hydrology. However, how this change can pose an impact on habitat conditions, ecological conditions, and trophic state is also a matter of investigation. The very least attention has been paid to this so far. Therefore, the present study focused on these, taking the dam-induced Lower Tangon river basin of India and Bangladesh as a case. The degree of flow alteration in the river was presented in a heat map. Multi-parametric machine learning (ML) approaches were applied to model hydrological instability and habitat condition. The ecological consequences like evaluating eco-deficit using flow duration curve (FDC) approach, trophic state using trophic state index (TSI), fish habitat zone using image-based hydrological parameters, etc. were measured. The study exhibited that after damming, the degree of river flow modification was about 41%. Consequently, the wetland hydrological instability and habitat conditions were degraded. In the post-dam period, > 50% of wetland area was lost, and hydrological instability was enhanced considerably over wider parts of the wetland. Habitat conditions of the existing wetland also witnessed fragility (poor and very poor areas increased by about 22.23 and 9.34%). As a result of this, adverse ecological responses were found. For instance, the eco-deficit area was increased by 36.19%, a good proportion (100%) of wetlands was witnessed the transformation of TSI from oligotrophic to mesotrophic state, and optimum fish habitat area was declined. The ecological strength map integrating all the cause-effect model parameters showed that good ecological strength was reduced from 49 to 2% in the post-dam. The result of the study would be very useful for wetland restoration for ecological and human well-being.

Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum analysis-based denoising

Forecasting discharge (Q) and water level (H) are essential factors in hydrological research and flood prediction. In recent years, deep learning has emerged as a viable technique for capturing the non-linear relationship of historical data to generate highly accurate prediction results. Despite the success in various domains, applying deep learning in Q and H prediction is hampered by three critical issues: a shortage of training data, the occurrence of noise in the collected data, and the difficulty in adjusting the model’s hyper-parameters. This work proposes a novel deep learning-based Q–H prediction model that overcomes all the shortcomings encountered by existing approaches. Specifically, to address data scarcity and increase prediction accuracy, we design an ensemble learning architecture that takes advantage of multiple deep learning techniques. Furthermore, we leverage the Singular-Spectrum Analysis (SSA) to remove noise and outliers from the original data. Besides, we exploit the Genetic Algorithm (GA) to propose a novel mechanism that can automatically determine the prediction model’s optimal hyper-parameters. We conducted extensive experiments on two datasets collected from Vietnam’s Red and Dakbla rivers. The results show that our proposed solution outperforms current techniques across a wide range of metrics, including NSE, MSE, MAE, and MAPE. Specifically, by exploiting the ensemble learning technique, we can improve the NSE by at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\%$$\end{document}2%. Moreover, with the aid of the SSA-based data preprocessing technique, the NSE is further enhanced by more than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5\%$$\end{document}5%. Finally, thanks to GA-based optimization, our proposed model increases the NSE by at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6\%$$\end{document}6% and up to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40\%$$\end{document}40% in the best case.

A Review of Reservoir Operation Optimisations: from Traditional Models to Metaheuristic Algorithms

Reservoir operation optimisation secures benefits, such as optimising energy production while minimising the possibility of flooding, operating costs, and water scarcity, at the lowest possible cost. This paper carries reviews of research on reservoir optimisation models and the consequential challenges of optimally operating reservoir operations. An introductory section is given to the background of reservoir operations and the current concerns on the optimal reservoir operations, for the decision-makers and stakeholders. Next, the review covered the recent ten years (between 2011 and 2021), on the recent research developments in innovation and techniques of reservoir operation optimisation. Further reviews on the conventional techniques that are the traditional methods, linear programming, nonlinear programming, and dynamic programming are discussed. Enhancements to the techniques in improving the drawbacks of the traditional techniques in optimisation of reservoir policies are next explained and evaluated. Recent advances in applying metaheuristics optimisation algorithms beneficial to the reservoir operations are explained, including the advantages and hinderances. A comprehensive tabulated and categorised review according to the classification of reservoir models, evaluation methods, and reservoir systems is given.

Enhancing the performance of data-driven models for monthly reservoir evaporation prediction

The accuracy level for reservoir evaporation prediction is an important issue for decision making in the water resources field. The traditional methods for evaporation prediction could encounter numerous obstacles owing to the effect of several parameters on the shape of the evaporation pattern. The current research presented modern model called the Coactive Neuro-Fuzzy Inference System (CANFIS). Modification for such model has been achieved for enhancing the evaporation prediction accuracy. Genetic algorithm was utilized to select the effective input combination. The efficiency of the proposed model has been compared with popular artificial intelligence models according to several statistical indicators. Two different case studies Aswan High Dam (AHD) and Timah Tasoh Dam (TTD) have been considered to explore the performance of the proposed models. It is concluded that the modified GA-CANFIS model is better than GA-ANFIS, GA-SVR, and GA-RBFNN for evaporation prediction for both case studies. GA-CANFIS attained minimum RMSE (15.22 mm month^-1 for AHD, 8.78 mm month^-1 for TTD), minimum MAE (12.48 mm month^-1 for AHD, 5.11 mm month^-1 for TTD), and maximum determination coefficient (0.98 for AHD, 0.95 for TTD).

Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles

Due to the increasing demand for water supply of urban areas, treatment and supply plants are becoming important to ensure availability and quality of this essential resource for human health. Enabling technologies of Industry 4.0 have the potential to improve performances of treatment plants. In this paper, after reviewing contributions in scientific literature on I4.0 technologies in dam operations, a study carried out on a Brazilian dam is presented and discussed. The main purpose of the study is to evaluate the economic, environmental, and social advantages achieved through the adoption of Artificial Intelligence (AI) in dam operations. Unlike automation that just respond to commands, AI uses a large amount of data training to make computers able to take the best decision. The current study involved a company that managed six reservoirs for treatment systems supplying water to almost ten million people at the metropolitan area of São Paulo City. Results of the study show that AI adoption could lead to economic gain in figures around US$ 51,000.00 per year, as well as less trips between sites and less overtime extra costs on the main operations. Increasing gates maneuvers agility result in significant environmental gains with savings of about 4.32 billion L of water per year, enough to supply 73,000 people. Also, decreasing operational vehicle utilization results in less emissions. Finally, the AI implementation improved the safety of dam operations, resulting in social benefits such as the flood risk mitigation in cities and the health and safety of operators.

Reservoir Evaporation Prediction Modeling Based on Artificial Intelligence Methods

The current study explored the impact of climatic conditions on predicting evaporation from a reservoir. Several models have been developed for evaporation prediction under different scenarios, with artificial intelligence (AI) methods being the most popular. However, the existing models rely on several climatic parameters as inputs to achieve an acceptable accuracy level, some of which have been unavailable in certain case studies. In addition, the existing AI-based models for evaporation prediction have paid less attention to the influence of the time increment rate on the prediction accuracy level. This study investigated the ability of the radial basis function neural network (RBF-NN) and support vector regression (SVR) methods to develop an evaporation rate prediction model for a tropical area at the Layang Reservoir, Johor River, Malaysia. Two scenarios for input architecture were explored in order to examine the effectiveness of different input variable patterns on the model prediction accuracy. For the first scenario, the input architecture considered only the historical evaporation rate time series, while the mean temperature and evaporation rate were used as input variables for the second scenario. For both scenarios, three time-increment series (daily, weekly, and monthly) were considered.

A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide.