Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Evaluation of methane generation rate and potential from selected landfills in Malaysia

Methane emissions and oxidation were measured during the wet and dry seasons at the Air Hitam, Jeram, and Sungai Sedu landfills in Malaysia. The resulting levels of methane emissions and oxidation were then modeled using the Inter-governmental Panel on Climate Change 1996 first order decay (FOD) model to obtain methane generation rate and potential values. Emissions measurements were performed using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface methane and carbon dioxide emissions at four monitoring locations in each landfill was used to estimate the methane oxidation capacity. The methane potential value was 151.7 m³ t^?1 for the Air Hitam and Jeram sanitary landfills and 75.9 m³ t^?1 for the Sungai Sedu open dumping landfill. The methane generation rate value of the Jeram and Air Hitam sanitary landfills during the wet season was 0.136 year^?1, while that of Jeram during the dry season was 0.072 year^?1. The methane generation rate values of the Sungai Sedu open dumping landfill during the wet and dry seasons were 0.008 and 0.0049 year^?1, respectively. The observed values of methane generation rate and potential assist to accurately estimate total methane emissions from Malaysian landfills using the Inter-governmental Panel on Climate Change FOD model.     

Optimization of area–volume–elevation curve using GIS–SRTM method for rainwater harvesting in arid areas

The major limitation in planning water harvesting is the lack of knowledge in the estimation of surface area and storage volume at any depth of dam reservoir. The area–volume–elevation (AVE) curve of a reservoir plays a key role in estimating the most suitable depth, optimum surface area and highest capacity of reservoir storage. The existing methods to estimate the AVE curve are costly and time-consuming and require laborious work. This study attempts to develop a method to optimize the AVE curve for earth dams, using the digital elevation model generated by the Shuttle Radar Topography Mission (SRTM) data, and integrate it with the geographic information system (GIS), known as the GIS–SRTM. The proposed method was tested using field data in the Western Desert of Iraq, which is an arid environment. Three constructed small earth dams were selected for this study. The AVE curves were extracted for Horan 2 (H2), Al-gara 2 (G2) and Al-gara 4 (G4) earth dams. Comprehensive analyses have been carried out to evaluate the performance of the AVE curves using the proposed GIS–SRTM method and the field data. From the comparison, the proposed GIS–SRTM method was able to produce reliable AVE curves with a relative error less than 20%. Additionally, the proposed method was less time-consuming and the AVE curves can be visualized immediately. The proposed GIS–SRTM method is relatively supportive in analyzing spatial data to select the optimal site for rainwater harvesting and prevent excessive evaporation losses.     

Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam

ABSTRACT

The potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models – CEEMDAN-ANN and CEEMDAN-M5-MT – with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error.     

Reservoir-system simulation and optimization techniques

Reservoir operation is one of the challenging problems for water resources planners and managers. In developing countries the end users are represented by the water sectors in most parts and conflict over water is resolved at the agency level. This paper discusses an overview of simulation and optimization modeling methods utilized in resolving critical issues with regard to reservoir systems. In designing a highly efficient as well as effective dam and reservoir operational system, reservoir simulation constitutes one of the most important steps to be considered. Reservoirs with well-functional and reliable optimization models require very accurate simulations. However, the nonlinearity of natural physical processes causes a major problem in determining the simulation of the reservoir’s parameters (elevation, surface-area, storage). Optimization techniques have shown high efficiency when used with simulation modeling and the combination of the two methods had given the best results in the reservoir management. The principal concern of this review study is to critically evaluate and analyze simulation, optimization and combined simulation–optimization modeling approach and present an overview of their utility in previous studies. Inferences and suggestions which may assist in improving quality of this overview in the future are provided. These will also enable future researchers, system analysts and managers to achieve more precise optimal operational system.     

Bat algorithm for dam–reservoir operation

Optimizing reservoir operation rule is considered as a complex engineering problem which requires an efficient algorithm to solve. During the past decade, several optimization algorithms have been applied to solve complex engineering problems, which water resource decision-makers can employ to optimize reservoir operation. This study investigates one of the new optimization algorithms, namely, Bat Algorithm (BA). The BA is incorporated with different rule curves, including first-, second-, and third-order rule curves. Two case studies, Aydoughmoush dam and Karoun 4 dam in Iran, are considered to evaluate the performance of the algorithm. The main purpose of the Aydoughmoush dam is to supply water for irrigation. Hence, the objective function for the optimization model is to minimize irrigation deficit. On the other hand, Karoun 4 dam is designed for hydropower generation. Three different evaluation indices, namely, reliability, resilience, and vulnerability were considered to examine the performance of the algorithm. Results showed that the bat algorithm with third-order rule curve converged to the minimum objective function for both case studies and achieved the highest values of reliability index and resiliency index and the lowest value of the vulnerability index. Hence, the bat algorithm with third-order rule curve can be considered as an appropriate optimization model for reservoir operation.     

Linear and stratified sampling-based deep learning models for improving the river streamflow forecasting to mitigate flooding disaster

Natural Hazards - Due to the need to reduce the flooding disaster, river streamflow prediction is required to be enhanced by the aid of deep learning algorithms. To achieve accurate model of...     

Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm

Natural Hazards - The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer...     

Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir,Peninsular Malaysia

Natural Hazards - Forecasting of reservoir inflow is one of the most vital concerns when it comes to managing water resources at reservoirs to mitigate natural hazards such as flooding. Machine...