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

Immune network algorithm in monthly streamflow prediction at Johor river

This study proposes an alternative method in generating future stream flow data with single-point river stage. Prediction of stream flow data is important in water resources engineering for planning and design purposes in order to estimate long term forecasting. This paper utilizes Artificial Immune System (AIS) in modelling the stream flow of one stations of Johor River. AIS has the abilities of self-organizing, memory, recognition, adaptive and ability of learning inspired from the immune system. Immune Network Algorithm is part of the three main algorithm in AIS. The model of Immune Network Algorithm used in this study is aiNet. The training process in aiNet is partly inspired by clonal selection principle and the other part uses antibody interactions for removing redundancy and finding data patterns. Like any other traditional statistical and stochastic techniques, results from this study, exhibit that, Immune Network Algorithm is capable of producing future stream flow data at monthly duration with various advantages

Potential impacts of climate change on precipitation and temperature at Jor Dam Lake

Rising global temperatures have threatened the operating conditions of Batang Padang hydropower reservoir system, Malaysia. It is therefore crucial to analyze how such changes in temperature and precipitation will affect water availability in the reservoir in the coming decades. Thus, to predict future climate data, including daily precipitation, and minimum and maximum temperature, a statistical weather generator (LARS-WG) is used as a downscaling model. Observed climate data (1984-2012) were employed to calibrate and validate the model, and to predict future climate data based on SRES A1B, A2, and B1 scenarios simulated by the General Circulation Model's (GCMs) outputs in 50 years. The results show that minimum and maximum temperatures will increase around 0.3-0.7 °C. Moreover, it is expected that precipitation will be lower in most months. These parameters greatly influence water availability and elevation in the reservoir, which are key factors in hydropower generation potential. In the absence of a suitable strategy for the operation of the hydropower reservoir, which does not consider the effects of climate change, this research could help managers to modify their operation strategy and mitigate such effects

Artificial intelligence projection model for methane emission from livestock in Sarawak

Artificial Intelligence is a topical trend employed to solve engineering and industrial problems by virtue of its abilities to deal with data uncertainty such as methane emissions. Hard computing methods are not suitable for determining the optimal emission in a methane emission data set. Instead, soft computing solutions should be considered in an effort to obtain better optimal solutions for industrial problems. This paper utilized the Guidelines provided in the 2006 Intergovernmental Panel on Climate Change (IPCC) to calculate and project methane emissions from selected six livestock in Sarawak, Malaysia. A particle swarm optimization (PSO) model was developed to project future methane emission by using number of livestock as the input parameter. The total CH4 inventory from the enteric fermentation of cattle, buffaloes, goats, sheep, swine and deer in Sarawak decreased from 1.860 to 1.856 Gg when calculation was carried out using the Tier 1 method. This decrease was due to population growth and the emission factors employed. Three statistical measures, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were employed for evaluation. PSO has been shown to be able to give an accurate projection. The results of this study provide a benchmark information which can be used by the Sarawak government to develop appropriate policies and mitigation strategies to reduce future carbon footprint in the Sarawak livestock sector

Optimization of exclusive release policies for hydropower reservoir operation by using genetic algorithm

The efficient utilization of hydropower resources play an important role in the economic sector of power systems, where the hydroelectric plants constitute a significant portion of the installed capacity. Determination of daily optimal hydroelectric generation scheduling is a crucial task in water resource management. By utilizing the limited water resource, the purpose of hydroelectric generation scheduling is to specify the amount of water releases from a reservoir in order to produce maximum power, while the various physical and operational constraints are satisfied. Hence, new forms of release policies namely, BSOPHP, CSOPHP, and SHPHP are proposed and tested in this research. These policies could only use in hydropower reservoir systems. Meanwhile, to determine the optimal operation of each policy, real coded genetic algorithm is applied as an optimization technique and maximizing the total power generation over the operational periods is chosen as an objective function. The developed models have been applied to the Cameron Highland hydropower system, Malaysia. The results declared that by using optimal release policies, the output of power generation is increased, while these policies also increase the stability of reservoir system. In order to compare the efficiency of these policies, some reservoir performance indices such as reliability, resilience, vulnerability, and sustainability are used. The results demonstrated that SHPHP policy had the highest performance among the tested release policies

Suitability of ANN applied as a hydrological model coupled with statistical downscaling model: a case study in the northern area of Peninsular Malaysia

The increase in global surface temperature in response to the changing composition of the atmosphere will significantly impact upon local hydrological regimes and water resources. This situation will then lead to the need for an assessment of regional climate change impacts. The objectives of this study are to determine current and future climate change scenarios using statistical downscaling model (SDSM) and to assess climate change impact on river runoff using artificial neural network (ANN) and identification of unit hydrographs and component flows from rainfall, evaporation and streamflow data (IHACRES) models, respectively. This study investigates the potential of ANN to project future runoff influenced by large-scale atmospheric variables for selected watershed in Peninsular Malaysia. In this study, simulations of general circulation models from Hadley Centre 3rd generation with A2 and B2 scenarios have been used. According to the SDSM projection, daily rainfall and temperature during the 2080s will increase by up to 2.23 mm and 2.02 °C, respectively. Moreover, river runoff corresponding to downscaled future projections presented a maximum increase in daily river runoff of 52 m3/s. The result revealed that the ANN was able to capture the observed runoff, as well as the IHACRES. However, compared to the IHACRES model, the ANN model was unable to provide an identical trend for daily and annual runoff series

The prediction of blue water footprint at Semambu water treatment plant by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM) models Machine (SVM) models.

The prediction of the blue water footprint in water services such as in water treatment plants (WTPs) is non-trivial to water resource management. Currently, the sustainability of water resources is of great concern globally, particularly in addressing the 6th goal of the United Nation's Sustainable Development Goals (UN SDGs). This study focuses on the blue water footprint (WFblue) assessment and prediction of WTP located at the Kuantan River Basin, Malaysia. The intake water of WTP is directly obtained from the mainstream river within the basin known as the Kuantan River. The predictability of the WFblue was evaluated by means of Artificial Neural Networks (ANN) and Support Vector Machine (SVM). Different hyperparameters of both the ANN and SVM models were investigated to ascertain the best prediction models attainable by evaluating both the mean squared error (MSE) as well as the coefficient of determination, R. It was demonstrated from the study that the optimised ANN model is able to yield a better prediction performance in comparison to the optimised SVM model. Therefore, it could be concluded that the application of ANN to predict the future trend is pertinent and should be incorporated in water footprint studies as it is vital for water resources regulators to anticipate the condition of WFblue in the future and to line up the appropriate actions especially in controlling the influencing parameters namely, water intake, rainfall and evaporation

Examining the impacts of individual lot stormwater detention in a housing estate

This paper describes the Storm Water Management Model (SWMM) simulations of three individual lot stormwater detention systems under the car porches of houses. These three systems consist of ready-made modular units presumably fitted under 49 m2 car porches of 204 double-story terrace houses. The 37,032 m2 housing estate is calculated to have 75% of land covered with houses, 25% with roads and other infrastructures. The housing estate was subjected to 5-minute, 10-year Average Recurrent Interval (ARI) short-duration design rainfall. The model predicted that all three systems could reduce the peak runoff at outfall from 2.79 to 0.38 m3/s. It indicated that any of the system could cause 86% reduction of the runoff for the whole housing estate. In order to differentiate the performance of the three systems, the housing lot was further investigated. When Type 1 system (1.15 m high with 49 m3 per lot) was analysed by the SWMM model, only 8% of its storage volume was filled that highlights an over design. Type 2 system (0.3 m high with 6 m3 per lot) modelled at 84% while Type 3 system (0.3 m high with 9 m3 per lot), at 54%. The difference in heights between the systems explained the low percentage of filling for the Type 1 system. Comparing Type 2 and Type 3, concrete structure within Type 3 had only half of its volume filled. In this light, the Type 2 system made of polyethylene pieces was found the most efficient in lowering post-development peak runoff

Prediction of monthly rainfall at Senai, Johor using artificial immune system and deep learning neural network

In order to obtain good accuracy for the prediction of rainfall, this paper developed the Clonal Selection Algorithm (CSA) as a model for monthly rainfall prediction at Senai, Johor, Malaysia. CSA is one of the main algorithms in the Artificial Immune System. The results were compared with an established model for prediction which is the deep Multilayer Perceptron (MLP) algorithm. MLP is a deep learning algorithm used in the Artificial Neural Network (ANN). The algorithms were modelled using rainfall historical data with four input meteorological variables which are humidity, wind speed, pressure and temperature over the period of 1987 to 2017. The result shows that CSA obtained better prediction accuracy compared to MLP. CSA was applied successfully for the prediction of a continuous time series data with a high variable in nature