Othman Jaafar

Water quality prediction model utilizing integrated wavelet-ANFIS model with cross-validation

This paper discusses the accuracy performance of training, validation and prediction of monthly water quality parameters utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS). This model was used to analyse the historical data that were generated through continuous monitoring stations of water quality parameters (i.e. the dependent variable) of Johor River in order to imitate their secondary attribute (i.e. the independent variable). Nevertheless, the data arising from the monitoring stations and experiment might be polluted by noise signals owing to systematic and random errors. This noisy data often made the predicted task relatively difficult. Thus, in order to compensate for this augmented noise, the primary objective of this study was to develop a technique that could enhance the accuracy of water quality prediction (WQP). Therefore, this study proposed an augmented wavelet de-noising technique with Neuro-Fuzzy Inference System (WDT-ANFIS) based on the data fusion module for WQP. The efficiency of the modules was examined to predict critical parameters that were affected by the urbanization surrounding the river. The parameters were investigated in terms of the following: the electrical conductivity (COND), the total dissolved solids (TDSs) and turbidity (TURB). The results showed that the optimum level of accuracy was achieved by making the length of cross-validation equal one-fifth of the data records. Moreover, the WDT-ANFIS module outperformed the ANFIS module with significant improvement in predicting accuracy. This result indicated that the proposed approach was basically an attractive alternative, offering a relatively fast algorithm with good theoretical properties to de-noise and predict the water quality parameters. This new technique would be valuable to assist decision-makers in reporting the status of water quality, as well as investigating spatial and temporal changes.

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.

Improving Rainfall Forecasting Efficiency Using Modified Adaptive Neuro-Fuzzy Inference System (MANFIS)

Rainfall is one of the most complicated effective hydrologic processes in runoff prediction and water management. The adaptive neuro-fuzzy inference system (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different kinds of problems, especially efficient in rainfall prediction. This paper after reconsidering conventional ANFIS architecture brings up a modified ANFlS (MANFlS) structure developed with attention to making ANFIS technique more efficient regarding to Root Mean Square Error (RMSE), Correlation Coefficient (R2), Root Mean Absolute Error (RMAE), Signal to Noise Ratio (SNR) and computing epoch. The modified ANFIS (MANFIS) architecture is simpler than conventional ANFIS with nearly the same performance for modeling nonlinear systems. In this study, two scenarios were introduced; in the first scenario, monthly rainfall was used solely as an input in different time delays from the time (t) to the time (t-4) to conventional ANFIS, second scenario used the modified ANFIS to improve the rainfall forecasting efficiency. The result showed that the model based Modified ANFIS performed higher rainfall forecasting accuracy; low errors and lower computational complexity (total number of fitting parameters and convergence epochs) compared with the conventional ANFIS model.

Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy

Complexicity in reservoir operation poses serious challenges to water resources planners and managers. These challenges of water reservoir operation are illustrated using a simulation to aid the development of an optimal operation policy for dam and reservoir. To achieve this, a Comprehensive Stochastic Dynamic Programming with Artificial Neural Network (SDP-ANN) model were developed and tested at Sg. Langat Reservoir in Malaysia. The nonlinearity of the natural physical processes was a major problem in determining the simulation of the reservoir parameters (elevation, surface-area, storage). To overcome water shortages resulting from uncertainty, the SDP-ANN model was used to evaluate the input variable and the performance outcome of the Model were compared with the Stochastic Dynamic Programming integrated with auto-regression (SDP-AR) model. The objective function of the models was set to minimize the sum of squared deviation from the desired targeted supply. Comparison result on the performance between SDP-AR model policy with SDP-ANN model found that the SDP-ANN model is a reliable and resilience model with a lesser supply deficit. The study concludes that the SDP-ANN model performs better than the SDP-AR model in deriving an optimal operating policy for the reservoir.

Adaptive neuro-fuzzy inference system–based model for elevation–surface area–storage interrelationships

In the developing of an optimal operation schedule for dams and reservoirs, reservoir simulation is one of the critical steps that must be taken into consideration. For reservoirs to have more reliable and flexible optimization models, their simulations must be very accurate. However, a major problem with this simulation is the phenomenon of nonlinearity relationships that exist between some parameters of the reservoir. Some of the conventional methods use a linear approach in solving such problems thereby obtaining not very accurate simulation most especially at extreme values, and this greatly influences the efficiency of the model. One method that has been identified as a possible replacement for ANN and other common regression models currently in use for most analysis involving nonlinear cases in hydrology and water resources–related problems is the adaptive neuro-fuzzy inference system (ANFIS). The use of this method and two other different approaches of the ANN method, namely feedforward back-propagation neural network and radial basis function neural network, were adopted in the current study for the simulation of the relationships that exist between elevation, surface area and storage capacity at Langat reservoir system, Malaysia. Also, another model, auto regression (AR), was developed to compare the analysis of the proposed ANFIS and ANN models. The major revelation from this study is that the use of the proposed ANFIS model would ensure a more accurate simulation than the ANN and the classical AR models. The results obtained showed that the simulations obtained through ANFIS were actually more accurate than those of ANN and AR; it is thus concluded that the use of ANFIS method for simulation of reservoir behavior will give better predictions than the use of any new or existing regression models.

Analysis of hydrological processes of Langat river sub basins at Lui and Dengkil

Land use changes have seriously impacted the hydrological regimes. This study considered both the upstream and downstream areas of Langat River namely the Lui and Dengkil sub basins, respectively. The important parameters selected for the study were river flowrate, precipitation distribution, and the baseflow (BF) estimation. For the Lui sub basin, the daily flowrate data obtained from 1972 to 2009 (35 years) were used, while for Dengkil sub basin, the data obtained from 1965 to 2009 (44 years) were used. The overall result showed that the monthly mean flowrate of April and November was higher as compared to other months, while the lowest mean flowrate occurred in February and August. Both sub basins showed upward trends in yearly mean flowrate throughout the study period. To obtain a clearer picture on the annual mean flowrate distribution of the two basins, Boxplot variables were plotted. The estimation of the BF was based on the separation method of the United Kingdom Institute of Hydrology (UKIH) smooth minima. Subsequently, BF indexes (BFI) for various years were determined. The Lui subbasin showed almost constant annual BFI, while Dengkil sub basin exhibited downward trend. This indicated that the contribution of the BF on total flow of the Dengkil sub basin reduced over the years. The land disturbances and the increase in the size of imperviousness has certainly reduced the opportunity for infiltration of rainwater into the ground and has most probably decreased the quantum of ground water to recharge the river system during intervening periods between rainfall events. Analysis on the trend of the annual 7-day low flow, however, seems to show increasing trend for both sub basins. This seems to contradict with the decreasing trend of BF, in particular those of Dengkil sub-basin. Finally, this study also included analysis of rainfall based on the 20 to 54 years of available data of the fourteen rain gauging stations within the study areas. The result indicated that the contribution of the total monsoon rainfall in both Lui and Dengkil sub-basins was above 77% of total rainfall received, which is similar to the average monsoon rainfall (81%) of Peninsular Malaysia. The rainfall was classified based on 5 classes as follows: 0 mm/day (no rainfall), 1 to 10 mm/day (light), 11 to 30 mm/day (moderate), 31 to 60 mm/day (heavy) and >60mm/day (very heavy).

Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Accuracy enhancement for forecasting water levels of reservoirs and river streams using a multiple-input-pattern fuzzification approach

Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.

Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region

Existing forecast models applied for reservoir inflow forecasting encounter several drawbacks, due to the difficulty of the underlying mathematical procedures being to cope with and to mimic the naturalization and stochasticity of the inflow data patterns. In this study, appropriate adjustments to the conventional coactive neuro-fuzzy inference system (CANFIS) method are proposed to improve the mathematical procedure, thus enabling a better detection of the high nonlinearity patterns found in the reservoir inflow training data. This modification includes the updating of the back propagation algorithm, leading to a consequent update of the membership rules and the induction of the centre-weighted set rather than the global weighted set used in feature extraction. The modification also aids in constructing an integrated model that is able to not only detect the nonlinearity in the training data but also the wide range of features within the training data records used to simulate the forecasting model. To demonstrate the model’s efficacy, the proposed CANFIS method has been applied to forecast monthly inflow data at Aswan High Dam (AHD), located in southern Egypt. Comparative analyses of the forecasting skill of the modified CANFIS and the conventional ANFIS model are carried out with statistical score indicators to assess the reliability of the developed method. The statistical metrics support the better performance of the developed CANFIS model, which significantly outperforms the ANFIS model to attain a low relative error value (23%), mean absolute error (1.4 BCM month−1), root mean square error (1.14 BCM month−1), and a relative large coefficient of determination (0.94). The present study ascertains the better utility of the modified CANFIS model in respect to the traditional ANFIS model applied in reservoir inflow forecasting for a semi-arid region.