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

Metaheuristic Algorithms to Enhance the Performance of a Feedforward Neural Network in Addressing Missing Hourly Precipitation

This research study investigates the implementation of three metaheuristic algorithms, namely, Grey wolf optimizer (GWO), Multi-verse optimizer (MVO), and Moth-flame optimisation (MFO), for coupling with a feedforward neural network (FNN) in addressing missing hourly rainfall observations, while overcoming the limitation of conventional training algorithm of artificial neural network that often traps in local optima. The proposed GWOFNN, MVOFNN, and MFOFNN were compared against the conventional Levenberg Marquardt Feedforward Neural Network (LMFNN) in addressing the artificially introduced missing hourly rainfall records of Kuching Third Mile Station. The findings show that the proposed approaches are superior to LMFNN in predicting the 20% hourly rainfall observations in terms of mean absolute error (MAE) and coefficient of correlation (r). The best performance ANN model is GWOFNN, followed with MVOFNN, MFOFNN and lastly LMFNN

Imputation of Rainfall Data Using the Sine Cosine Function Fitting Neural Network

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SCFFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation

Metaheuristic Algorithms to Enhance the Performance of a Feedforward Neural Network in Addressing Missing Hourly Precipitation

This research study investigates the implementation of three metaheuristic algorithms, namely, Grey wolf optimizer (GWO), Multi-verse optimizer (MVO), and Moth-flame optimisation (MFO), for coupling with a feedforward neural network (FNN) in addressing missing hourly rainfall observations, while overcoming the limitation of conventional training algorithm of artificial neural network that often traps in local optima. The proposed GWOFNN, MVOFNN, and MFOFNN were compared against the conventional Levenberg Marquardt Feedforward Neural Network (LMFNN) in addressing the artificially introduced missing hourly rainfall records of Kuching Third Mile Station. The findings show that the proposed approaches are superior to LMFNN in predicting the 20% hourly rainfall observations in terms of mean absolute error (MAE) and coefficient of correlation (r). The best performance ANN model is GWOFNN, followed with MVOFNN, MFOFNN and lastly LMFNN

Characterization and Impact of Curing Duration on the Compressive Strength of Coconut Shell Coarse Aggregate in Concrete

Partial replacement with coconut shell coarse aggregates was studied as a means to produce lightweight coconut shell concrete (CSC). Coconut shell concrete is a structural grade lightweight concrete that has a lower self-load compared to the normal weight concrete (NWC), which allowed the production of larger precast units. An experimental study and analysis were conducted using different volume percentages of 0%, 10%, 30%, 50%, and 70% of coconut shell as coarse aggregates, to produce M30 (30 MPa) grade concrete. The compressive strength of the NWC and CSC were obtained on the 7th and 28th day. The optimum results obtained for M30 grade concrete at 7th and 28th day of CSC were 34.2 and 38.6 MPa, respectively. In addition, the workability and weight-reduction were analyzed and compared with NWC. Scanning electron microscopy (SEM) with energy dispersive X-ray spectroscopy (EDS/EDX) and Fourier transform infrared spectroscopy (FTIR) were also used to investigate the structural morphology, chemical composition, and infrared functional groups of the concret

MODELLING THE EFFECTS OF SOCIO-ECONOMIC DEMOGRAPHICS ON URBAN WATER USAGE IN KOTA SAMARAHAN, SARAWAK : A NEW EDUCATION HUB IN BORNEO ISLAND

This study was carried out to investigate the influence of socio-economic status on household water usage patterns in Kota Samarahan, which is an education hub in Sarawak, Malaysia. This study commenced with a random sampling of 200 respondents, categorised into low-, medium- and high-income households. The medium-income household category was found to have the highest amount of water usage. The results showed that an increase in income leads to an increase in socio-economic status, dwelling size, and household occupancy. It was also observed that the “numbers of children” influences the increase in water usage within a family. In addition, the data set was further analysed using multiple linear regression modelling (STEPWISE). It was found that an increase in socio-economic demographic factors, including education level, number of female adults, number of clothing washed daily, number of wage earners, and number of dishes washed daily, increased the water usage per household. The findings of this study are crucial to ensuring a sustainable urban water supply in Kota Samarahan

Imputation of rainfall data using the sine cosine function fitting neural network

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SC-FFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation

Effect of Chemical Treatment on Silicon Manganese : Its Morphological, Elemental and Spectral Properties and Its Usage in Concrete

The efect of chemical treatment on silicon manganese slag and the efect of curing time on the compressive strength of completely replacement coarse aggregate silicon manganese concrete (SMC) relative to normal weight concrete (NWC) with gravel as coarse aggregate is the subject of this paper. Alkali and acidic base chemicals are used to alter the neat silicon manganese slag during chemical preparation. The mixture pattern proportions for Grade 30 and Grade 50, respectively, were used to create the concrete. Before the compressive strength tests, the samples were cast and cured for 7-, 14-, and 28-days. The properties of the silicon manganese slag and its concrete were studied using a scanning electron microscope (SEM), energy dispersive x-ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FTIR). The compressive strength of SMC30 and SMC50 obtained were 37.3 MPa and 51.1 MPa, respectively, on the 28-day. The alkaline treatment smoothest the matrix, while the acid treatment roughens the composition of the silicon manganese slag, according to SEM. The functional group demonstrated a major improvement in FTIR, while EDS revealed a high content of both silicon (Si) and manganese (Mn) elements. As a result, it can be observed that the power of SMC increases as the curing time increases for samples with complete replacement of normal aggregates using silicon slag

Application of Building Information Modelling (BIM) Technology in Drainage System Using Autodesk InfraWorks 360 Software

The increased number of physical drainage drawings at Samarahan district, Sarawak for new development areas is difficult to manage and handle by relevant authorities. Hence, this research is conducted to determine the feasibility of Building Information Technology (BIM) to create a proper drainage inventory system to accurately list and record current drainage information using Autodesk Infraworks 360 software. This inventory system will be employed to examine and validate corresponding drainage parameters based on the recorded information. Taman UniCentral, a residential neighbourhood in Kota Samarahan, has been chosen for this case study. Drainage data, such as drainage size, length, invert level, are entered into GIS-integrated Model Builder in Autodesk InfraWorks 360. Autodesk InfraWorks 360 will conduct a preliminary analysis, including watershed analysis, to delineate the catchment area and drainage performance inspections at rainfall intensities of 2, 5, 10, 20, and 50 years (ARI). Thereafter, the InfraWorks model will be exported into Autodesk Civil3D to conduct a more extensive hydraulic analysis. The results show that full integration of these two Autodesk software packages had created a proper inventory system of existing drainage information and simulated its sufficiency in catering surcharge runoff from the new development area at the upper catchment