Application of ANNs model with the SDSM for the hydrological trend prediction in the sub-catchment of Kurau River, Malaysia

The paper describes the application of SDSM (statistical downscaling model) and ANNs (artificial neural networks) models for prediction of the hydrological trend due to the climate-change. The SDSM has been calibrated and generated for the possible future scenarios of meteorological variables, which are temperature and rainfall by using GCMs (global climate models). The GCM used is SRES A2. The downscaled meteorological variables corresponding to SDSM were then used as input to the ANNs model calibrated with observed station data to simulate the corresponding future streamflow changes in the sub-catchment of Kurau River. This study has discovered the hydrological trend over the catchment. The projected monthly streamflow has shown a decreasing trend due to the increase in the mean of temperature for overall months, except the month of August and November

Global optimization methods for calibration and optimization of the hydrologic tank model's parameters

The tank model, a lumped conceptual hydrological model, is well known due to its simplicity of concept, simplicity in computation while achieving forecasting accuracy comparable with more sophisticated models. However, the calibration of the hydrologic tank model required much time and effort to obtain better results through trial and error method. With the development of artificial intelligence, three probabilistic Global Optimization methods namely Genetic Algorithm (GA), Shuffle Complex Evolution (SCE) and Particle Swarm Optimization (PSO) were adopted for model calibration. The objective of the study is to find the best type of Global Optimization Methods and the best configuration to calibrate tank model that will produce the best fit between the observed and simulated runoff. The selected study area is Bedup Basin, located at Samarahan Division, Sarawak. Input data used for model calibration is a single storm event. The optimal parameters obtained will then be validated with 11 other single storm events. The performance of the optimization techniques is measured using Coefficient of Correlation (R) and Nash-Sutcliffe coefficient (E 2 ). Results show that all three probabilitic GOMs are able to obtain optimal value for 10 parameters of tank model. However, the best GOMs for hourly runoff simulation is PSO. SCE appeard to be the second best performance GOMs and the least performed is GA technique

Comparative study on the reservoir operation planning with the climate change adaptation

The management planning of Pedu–Muda reservoir, Kedah, was investigated in the context of the climate change evolution. The aim of this study was to evaluate the impact of the climate change to the reservoir operating management system and its sustainability. The study was divided into two sections; Analysis 1 refers to the reservoir optimization adapted with the climate assessment. The statistical downscaling model reacted as the climate model to generate the long-term pattern of the local climates affected by the greenhouse gases. Analysis 2 refers to the reservoir optimization but excluded the climate changes assessment in the analyses. The non-dominated sorting genetic algorithm version II (NSGA-II) was applied in both analyses to optimize the water use due to the multi-objectives demand, maximizing water release, minimizing water shortage and maximizing reservoir storage. The formation of Pareto optimal solutions from both analyses was measured and compared. The results showed the Analysis 1 potential to produce consistence monthly flow with lesser error and higher correlation values. It also produced better Pareto optimal solution set and considered all the objectives demands. The NSGA-II also successfully improves and re-manages the reservoir storage efficiently and reduce the dependency of these reservoirs

Comparison of different methods in estimating potential évapotranspiration at muda irrigation scheme of Malaysia

Evapotranspiration (ET) is a complex process in the hydrological cycle that influences the quantity of runoff and thus the irrigation water requirements. Numerous methods have been developed to estimate potential evapotranspiration (PET). Unfortunately, most of the reliable PET methods are parameter rich models and therefore, not feasible for application in data scarce regions. On the other hand, accuracy and reliability of simple PET models vary widely according to regional climate conditions. The objective of the present study was to evaluate the performance of three temperature-based and three radiation-based simple ET methods in estimating historical ET and projecting future ET at Muda Irrigation Scheme at Kedah, Malaysia. The performance was measured by comparing those methods with the parameter intensive Penman-Monteith Method. It was found that radiation based methods gave better performance compared to temperature-based methods in estimation of ET in the study area. Future ET simulated from projected climate data obtained through statistical downscaling technique also showed that radiation-based methods can project closer ET values to that projected by Penman-Monteith Method. It is expected that the study will guide in selecting suitable methods for estimating and projecting ET in accordance to availability of meteorological dat

Evaluation the performances of stochastic streamflow models for the multi reservoirs

Pedu-Muda reservoirs responsible to supply sufficient water capacity during paddy cultivation period twice a year. Thus, improper management and operation of the reservoirs creating the scarcity issue of water availability especially during dry season. Synthetic streamflow being as a main role in predicting the capability and sustainability of these reservoirs to cope the demand. This study evaluated the performances of stochastic streamflow model to produce the synthetic streamflow generation. There were two comparable models; Valencia Schaake (VS) and Thomas Fiering (TF) which represented in disaggregation and aggregation models, respectively. The basis of the statistical characteristics consist of lag one correlation, mean, mean absolute error (MAE), and standard deviation (St.D) of annual and monthly levels for both models were compared to identify the model performances. The results revealed the generated streamflow series produced by VS models had better performances to the historical streamflow record than the TF model in term of annual and monthly. However, both models can preserve a good agreement to the mean even the range of monthly streamflow were overestimated/underestimated by VS and TF models respectively

Rainfall runoff modelling in a large tropical catchment by ANFIS

Modeling the rainfall-runoff process is a significant task in hydrological modelling as it can be helpful in decreasing the damages of flooding and also managing the water resources. This could be very important for a tropical country such as Malaysia with approximately 2500 mm annual rainfall. To date, several models are developed to capture the rainfall-runoff relationship including physically-based models and system theoretic ones. Despite many uncertainties and complexities involved in physical models the system theoretic modeling techniques lately found applicants in a variety of hydrological problems including rainfall–runoff modeling. Among different types of system theoretic models Artificial Neural Networks (ANN) and Neuro-Fuzzy Systems (NFS) have been commonly used in hydrological modelling. Although ANNs have shown reasonably good performance in rainfall-runoff modelling they are suffering from several issues including long training time, non-transparent internal process, and requiring trial and error procedure to find an optimum structure. However, NFS which combine human-inspired reasoning style of fuzzy systems with learning and connectionist structure of neural networks have the significant advantage of reduced training time in comparison with ANNs. Moreover, NFS is not completely a black-box model as it can give an insight about its internal process in terms of IF-THEN rules. The well-known Adaptive Network-based Fuzzy Inference System (ANFIS) has been successfully employed in many engineering modelling applications including hydrological modelling. In ANFIS, the global parameter tuning has been considered by means of minimization of the global error of the model. Therefore, ANFIS has been found to be an appropriate tool in non-linear mapping problems between input and output such as rainfall-runoff modelling. The present study is an application of ANFIS in rainfall-runoff modeling in a large catchment (with area of 350 Km2) of Bekok River in the state of Johor, Malaysia. Approximately 85% of its area consists of agriculture fields, roads, utility reserves and the remaining 15% is in domestic use. Thirty years daily rainfall and runoff data was used in this study. The data was split into the training and testing datasets i.e. 80% for training and 20% for testing. The catchment has two rainfall stations; therefore, an input selection process based on correlation analysis was done to find the most appropriate rainfall and discharge antecedents for developing the model. Using 2 triangular membership functions and number of epoch of 30 were found to be appropriate for developing the ANFIS model

Performance assessment of different bias correction methods in statistical downscaling of precipitation

Global circulation models (GCMs) are widely used for the modeling and assessing the impacts of climate change. These models do not always accurately simulate climate variables due to the risk of considerable biases. Several bias correction methods have been proposed and applied so far. The selection and application of appropriate bias correction can improve accuracy and reduce uncertainty in downscaled precipitation in arid regions. In this study, initially multilayer perceptron (MLP) neural network was applied to downscale the mean monthly precipitation. The MLP model was calibrated by using National Center for environmental prediction (NCEP) reanalysis dataset and monthly precipitation observations located in selected hyper-arid, arid and semi-arid regions. Later, the performance of four bias correction methods namely, power transformation, simple multiplicative, variance inflation and quantile mapping were evaluated by comparing the mean and standard deviation of observed and downscaled precipitation. It has been found that the power transformation method is the most reliable and suitable method for downscaling precipitation in the arid region

Prediction in ungauged river basin in the west coast of peninsular Malaysia using linear regression model

A linear multiple regression based regionalization method has been proposed in this study to simulate streamflow in ungauged catchment in the east coast of peninsular Malaysia. Identification of unit Hydrographs And Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES) rainfall-runoff model was used to develop the relationship between model parameters and physical catchment descriptors of eight gauged catchments distributed over the west coast of peninsular Malaysia. The IHACRES model was calibrated and validated individually for each catchment with the available data for the time periods varying between three to sixteen years. The Nash-Sutcliffe efficiency index was used as criteria to evaluate the model performance. As the relationships between the physical catchment descriptors and hydrologic response characteristics are not necessarily linear, different forms of transformations were performed in order to find the most appropriate relationship. Finally, the obtained regression equations were used for simulating stream discharge in Sg Layang catchment located in the south of peninsular Malaysia. The result of the regional model was compared with observed monthly stream flow data of the catchment to assess the ability of regional model. The obtained results revealed that the regional model was able to replicate the historical monthly average flow. However, the relationship between the catchment area and hydrologic response characteristics were not fully understood by regional model which emphasize the need of consideration of other dominant catchment factors for prediction in ungauged basin

Transfer function models for statistical downscaling of monthly precipitation

Three transfer function based statistical downscaling namely, linear regression model (LM), generalized linear model (GLM), generalized additive model (GAM) have been developed to assess their performance in downscaling monthly rainfall. Previous studies reported that performance of downscaling model depends on climate region and characteristics of climatic variable being downscaled. This has motivated to assess the performance of these three statistical downscaling models to identify most suitable model for downscaling monthly rainfall in the East coast of Peninsular Malaysia. Assessment of model performance using standard statistical measures revealed that LM model performs best in downscaling monthly precipitation in the study area. The Nash–Sutcliffe efficiency (NSE) for LM was found always greater than 0.9 and 0.7 with predictor set selected using stepwise multiple regression method during model calibration and validation, respectively. The finding opposes the general conception of better performance of non-linear models compared to linear models in downscaling rainfall. The near normal distribution of monthly rainfall in the tropical region has made the LM model much stronger compared to other models which assume that distribution of dependent variable is not norma

Rainfall runoff modeling by multilayer perceptron neural network for LUI river catchment

Reliable modeling for the rainfall-runoff processes embedded with high complexity and non-linearity can overcome the problems associated with managing a watershed. Physically based rainfall-runoff models need many realistic physical components and parameters which are sometime missing and hard to be estimated. In last decades the artificial intelligence (AI) has gained much popularity for calibrating the nonlinear relationships of rainfall–runoff processes. The AI models have the ability to provide direct relationship of the input to the desired output without considering any internal processes. This study presents an application of Multilayer Perceptron neural network (MLPNN) for the continuous and event based rainfall-runoff modeling to evaluate its performance for a tropical catchment of Lui River in Malaysia. Five years (1999-2013) daily and hourly rainfall and runoff data was used in this study. Rainfall-runoff processes were also simulated with a traditionally used statistical modeling technique known as auto-regressive moving average with exogenous inputs (ARMAX). The study has found that MLPNN model can be used as reliable rainfall-runoff modeling tool in tropical catchment