Multifunctional optimized group method data handling for software effort estimation

Nowadays, the trend of significant effort estimations is in demand. Due to its popularity, the stakeholder needs effective and efficient software development processes with the best estimation and accuracy to suit all data types. Nevertheless, finding the best effort estimation model with good accuracy is hard to serve this purpose. Group Method of Data Handling (GMDH) algorithms have been widely used for modelling and identifying complex systems and potentially applied in software effort estimation. However, there is limited study to determine the best architecture and optimal weight coefficients of the transfer function for the GMDH model. This study aimed to propose a hybrid multifunctional GMDH with Artificial Bee Colony (GMDH-ABC) based on a combination of four individual GMDH models, namely, GMDH-Polynomial, GMDH-Sigmoid, GMDH-Radial Basis Function, and GMDH-Tangent. The best GMDH architecture is determined based on L9 Taguchi orthogonal array. Five datasets (i.e., Cocomo, Dershanais, Albrecht, Kemerer and ISBSG) were used to validate the proposed models. The missing values in the dataset are imputed by the developed MissForest Multiple imputation method (MFMI). The Mean Absolute Percentage Error (MAPE) was used as performance measurement. The result showed that the GMDH-ABC model outperformed the individual GMDH by more than 50% improvement compared to standard conventional GMDH models and the benchmark ANN model in all datasets. The Cocomo dataset improved by 49% compared to the conventional GMDH-LSM. Improvements of 71%, 63%, 67%, and 82% in accuracy were obtained for the Dershanis dataset, Albrecht dataset, Kemerer dataset, and ISBSG dataset, respectively, as compared with the conventional GMDH-LSM. The results indicated that the proposed GMDH-ABC model has the ability to achieve higher accuracy in software effort estimation

Non-linear water level forecasting of Dungun river using hybridization of backpropagation neural network and genetic algorithm

The Department of Irrigation and Drainage (DID) and Meteorological Malaysia Department (MMD) have identified that water level is one of the important indicators for flooding control. The aim of this study is to find the best regression model and to identify the dominant variables of water level in Dungun River. Autoregressive Integrated Moving Average (ARIMA),Seasonal ARIMA (SARIMA), Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) are popular methods in time series forecasting. However, ARIMA and SARIMA produce linear models where the approximations of linear models for the complex real-world problems are not always satisfactory. Thus, Backpropagation Neural Network (BPNN) and Nonlinear Autoregressive Exogenous Model (NARX) can be implemented in the time series forescasting due to its nonlinear modelling capability. These four methods, however, cannot be used directly for water level prediction since the original data from DID and MMD contain missing data. In this thesis, two methods are employed to treat missing data which are pre-processing using Mean and preprocessing using Ordinary Linear Regression (OLR) substitutions. In addition, BPNN and NARX may be difficult to determine the optimal network architecture and weights design since the optimal weight are different in each learning process. Thus, it is difficult to get best model in prediction. Based on the limitation of BPNN and NARX, the hybridization of Single BPPN and Genetic Algorithms (S-BPNN-GA) and Multi BPNN and Genetic Algorithms (M-BPNN-GA) have been proposed in this study. Experiments indicate hybridization of M-BPNN-GA 5-6-1 using five predictor variables including monthly, rainfall, temperature, evaporation and humidity and give better results compared to the other methods

Time series methods for water level forecasting of Dungun River in Terengganu Malaysia

Due to climate change and global warming, the possibility of floods may increase to occur in Malaysia. Water level forecasting is an important for the water catchment management in particular for flood warning systems. The aim of this study is to predict water level with input variables monthly rainfall and rate of evaporation taken from the same catchment at Dungun River, Terengganu-Malaysia, using ARIMA and Artificial Neural Network (ANN). The process of pre-processing data has been made to the original rainfall data since they contain imperfect characteristics data. Our experiments show that the ANN with cleansing rainfall data gives better performance than ARIMA and ANN without cleansing data

Neural networks based nonlinear time series regression for water level forecasting of Dungun River

The Department of Irrigation and Drainage (DID) Malaysia and Meteorological Malaysia Department (MMD) has been measured the flood characteristics benchmark which included water level, area inundation, peak inundation, peak discharge, volume of flow and duration of flooding. In terms of water levels, DID have introduced three categories of critical level stages namely normal, alert and danger levels. One of the rivers detected by DID that had reached danger level is Sungai Dungun located at Dungun district, Terengganu. The aim of this study is to find suitable prediction model of water level with input variables monthly rainfall, rate of evaporation, temperature and relative humidity taken from the same catchment at Dungun River using Neural Networks based Nonlinear Time Series Regression methods which are Backpropagation Neural Network (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) networks. The variables selection criteria procedures are also developed to select a significant explanatory variable. In addition, the process of pre-processing data such as treatment of missing data has been made on the original data collected by DID and MMD. The methods are compared to obtain the best model for prediction water level in Dungun River. Based on the experiments, the NARX model with five predictor variables is the best model compared to BPNN. In addition, treatment of missing data using mean and OLR approach produced comparable results for this case study