In this study, the implementation of the Regional Climate Model into the hydrodynamic model has been applied for streamflow projection on a river located at the south of Peninsular Malaysia within the years 2070 till 2099. The data has been obtained from a Regional Climate Model (RCM), named Précis, on a daily basis. It begins by comparing historical rainfall data generated from Précis versus the actual gauged recorded rainfall data from Department of Irrigation and Drainage Malaysia (DID). The bias of the generated rainfall data has been reduced by statistical techniques. The same has been applied to the future generated rainfall data from 2070 to 2099. Using the generated precipitation data as input to the hydrological model, results in the daily output of river discharge identified as the main contributor of flood occurrences. Based on the results of the hydrological model utilised, e.g. HEC-HMS, comparison was made between the future and historical generated discharge data using Précis between the years 1960 till 1998. Dividing a year into three segments, e.g. January-April, May-August, SeptemberDecember, the results show that there would be a significant drop of peak discharge in the third segment and an increase in discharge during the second segment. The first part remains almost with no changes. As an addition, the drop of the peak shows reduction in the probability of flood occurrences. It also indicates the reduction in water storage capacity which coherently affects the water supply scheme
The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
Effluent discharge from septic tanks is affecting the environment in developing countries. The most challenging issue facing these countries is the cost of inadequate sanitation, which includes significant economic, social, and environmental burdens. Although most sanitation facilities are evaluated based on their immediate costs and benefits, their long-term performance should also be investigated. In this study, effluent quality-namely, the biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended solid (TSS)-was assessed using a biomimetics engineering approach. A novel immune network algorithm (INA) approach was applied to a septic sludge treatment plant (SSTP) for effluent-removal predictive modelling. The Matang SSTP in the city of Kuching, Sarawak, on the island of Borneo, was selected as a case study. Monthly effluent discharges from 2007 to 2011 were used for training, validating, and testing purposes using MATLAB 7.10. The results showed that the BOD effluent-discharge prediction was less than 50% of the specified standard after the 97(th) month of operation. The COD and TSS effluent removals were simulated at the 85(th) and the 121(st) months, respectively. The study proved that the proposed INA-based SSTP model could be used to achieve an effective SSTP assessment and management technique.
This study aims at developing a novel effluent removal management tool for septic sludge treatment plants (SSTP) using a clonal selection algorithm (CSA). The proposed CSA articulates the idea of utilizing an artificial immune system (AIS) to identify the behaviour of the SSTP, that is, using a sequence batch reactor (SBR) technology for treatment processes. The novelty of this study is the development of a predictive SSTP model for effluent discharge adopting the human immune system. Septic sludge from the individual septic tanks and package plants will be desuldged and treated in SSTP before discharging the wastewater into a waterway. The Borneo Island of Sarawak is selected as the case study. Currently, there are only two SSTPs in Sarawak, namely the Matang SSTP and the Sibu SSTP, and they are both using SBR technology. Monthly effluent discharges from 2007 to 2011 in the Matang SSTP are used in this study. Cross-validation is performed using data from the Sibu SSTP from April 2011 to July 2012. Both chemical oxygen demand (COD) and total suspended solids (TSS) in the effluent were analysed in this study. The model was validated and tested before forecasting the future effluent performance. The CSA-based SSTP model was simulated using MATLAB 7.10. The root mean square error (RMSE), mean absolute percentage error (MAPE), and correction coefficient (R) were used as performance indexes. In this study, it was found that the proposed prediction model was successful up to 84 months for the COD and 109 months for the TSS. In conclusion, the proposed CSA-based SSTP prediction model is indeed beneficial as an engineering tool to forecast the long-run performance of the SSTP and in turn, prevents infringement of future environmental balance in other towns in Sarawak.
Artificial neural networks (ANNs) have been employed to solve a broad variety of tasks. The selection of an ANN model with appropriate weights is important in achieving accurate results. This paper presents an optimization strategy for ANN model selection based on the cuckoo search (CS) algorithm, which is rooted in the obligate brood parasitic actions of some cuckoo species. In order to enhance the convergence ability of basic CS, some modifications are proposed. The fraction Pa of the n nests replaced by new nests is a fixed parameter in basic CS. As the selection of Pa is a challenging issue and has a direct effect on exploration and therefore on convergence ability, in this work the Pa is set to a maximum value at initialization to achieve more exploration in early iterations and it is decreased during the search to achieve more exploitation in later iterations until it reaches the minimum value in the final iteration. In addition, a novel master-leader-slave multi-population strategy is used where the slaves employ the best fitness function among all slaves, which is selected by the leader under a certain condition. This fitness function is used for subsequent Lévy flights. In each iteration a copy of the best solution of each slave is migrated to the master and then the best solution is found by the master. The method is tested on benchmark classification and time series prediction problems and the statistical analysis proves the ability of the method. This method is also applied to a real-world water quality prediction problem with promising results.
An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
High temperature tolerance is an important component of adaptation to arid and semiarid cropping environment in chili pepper. Two experiments were carried out to study the genetic variability among chili pepper for heat tolerance and morphophysiological traits and to estimate heritability and genetic advance expected from selection. There was a highly significant variation among the genotypes in response to high temperature (CMT), photosynthesis rate, plant height, disease incidence, fruit length, fruit weight, number of fruits, and yield per plant. At 5% selection intensity, high genetic advance as percent of the mean (>20%) was observed for CMT, photosynthesis rate, fruit length, fruit weight, number of fruits, and yield per plant. Similarly, high heritability (>60%) was also observed indicating the substantial effect of additive gene more than the environmental effect. Yield per plant showed strong to moderately positive correlations (r = 0.23-0.56) at phenotypic level while at genotypic level correlation coefficient ranged from 0.16 to 0.72 for CMT, plant height, fruit length, and number of fruits. Cluster analysis revealed eight groups and Group VIII recorded the highest CMT and yield. Group IV recorded 13 genotypes while Groups II, VII, and VIII recorded one each. The results showed that the availability of genetic variance could be useful for exploitation through selection for further breeding purposes.
Genetic based knowledge of different vegetative and yield traits play a major role in varietal improvement of rice. Genetic variation gives room for recombinants which are essential for the development of a new variety in any crop. Based on this background, this work was carried out to evaluate genetic diversity of derived mutant lines and establish relationships between their yield and yield components using multivariate analysis. To achieve this objective, two field trials were carried out on 45 mutant rice genotypes to evaluate their growth and yield traits. Data were taken on vegetative traits and yield and its components, while genotypic and phenotypic coefficients, variance components, expected genetic advance, and heritability were calculated. All the genotypes showed variations for vegetative traits and yield and its components. Also, there was positive relationship between the quantitative traits and the final yield with the exception of number of tillers. Finally, the evaluated genotypes were grouped into five major clusters based on the assessed traits with the aid of UPGMA dendrogram. So hybridization of group I with group V or group VI could be used to attain higher heterosis or vigour among the genotypes. Also, this evaluation could be useful in developing reliable selection indices for important agronomic traits in rice.
This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.
Genetic diversity is important for crop improvement. An experiment was conducted during 2011 to study genetic variability, character association, and genetic diversity among 27 soybean mutants and four mother genotypes. Analysis of variance revealed significant differences among the mutants and mothers for nine morphological traits. Eighteen mutants performed superiorly to their mothers in respect to seed yield and some morphological traits including yield attributes. Narrow differences between phenotypic and genotypic coefficients of variation (PCV and GCV) for most of the characters revealed less environmental influence on their expression. High values of heritability and genetic advance with high GCV for branch number, plant height, pod number, and seed weight can be considered as favorable attributes for soybean improvement through phenotypic selection and high expected genetic gain can be achieved. Pod and seed number and maturity period appeared to be the first order traits for higher yield and priority should be given in selection due to their strong associations and high magnitudes of direct effects on yield. Cluster analysis grouped 31 genotypes into five groups at the coefficient value of 235. The mutants/genotypes from cluster I and cluster II could be used for hybridization program with the mutants of clusters IV and V in order to develop high yielding mutant-derived soybean varieties for further improvement.
Twelve introgressed oil palm (Elaeis guineensis) progenies of Nigerian dura x Deli dura were evaluated for bunch yield, yield attributes, bunch quality components and vegetative characters at the Malaysian Palm Oil Board Research Station, in Keratong, Pahang, Malaysia. Analysis of variance revealed significant to highly significant genotypic differences, indicating sufficient genetic variability among the progenies for bunch yield and its attributes, vegetative characters and bunch quality components, except fruit to bunch ratio. Fresh fruit bunch yield ranged from 167 kg·palm(-1)·year(-1) in PK1330 to 212 kg·palm(-1)·year(-1) in PK1351, with a mean yield of 192 kg·palm(-1)·year(-1). Among the progeny, PK1313 had the highest oil to bunch ratio (19.36%), due to its high mesocarp to fruit ratio, fruit to bunch ratio and low shell to fruit ratio. Among the progenies, PK1313 produced the highest oil yield of 31.4 kg·palm(-1)·year(-1), due to a high mesocarp to fruit ratio (61.2%) and a low shell to fruit ratio (30.7%), coupled with high fruit to bunch ratio (65.6%). PK1330 was found promising for selection, as it had desirable vegetative characters, including smaller petiole cross section (27.15 cm2), short rachis length (4.83 m), short palm height (1.85 m), and the lowest leaf number (164.6), as these vegetative characters are prerequisites for selecting palms for high density planting and high yield per hectare. The genetic variability among the progenies was found to be high, indicating ample scope for further breeding, followed by selection.
A study was performed using 6 × 6 F1 diallel population without reciprocals to assess the mode of inheritance of pod yield and related traits in groundnut with imposed salinity stress. Heterosis was found for pod number and yield. Data on general and specific combining ability (gca and sca) indicated additive and nonadditive gene actions. The gca: sca ratios were much less than unity suggesting predominant role of nonadditive gene effects. Cultivars "Binachinabadam-2" and "Dacca-1" and mutant M6/25/64-82 had the highest, second highest, and third highest pod number, as well as gca values, respectively. These two cultivars and another mutant M6/15/70-19 also had the highest, second highest, and third highest pod yield, as well as gca values, respectively. Therefore, "Dacca-1", "Binachinabadam-2", M6/25/64-82, and M6/15/70-19 could be used as source of salinity tolerance. Cross combinations showing high sca effects arising from parents with high and low gca values for any trait indicate the influence of nonadditive genes on their expression. Parents of these crosses can be used for biparental mating or reciprocal recurrent selection for developing high yielding varieties. Crosses with high sca effects having both parents with good gca effects could be exploited by pedigree breeding to get transgressive segregants.
Growth parameters such as leaf area (LA), total dry mass (TDM) production, crop growth rate (CGR), relative growth rate (RGR), and net assimilation rate (NAR) were compared in six varieties of mungbean under subtropical condition (24°8' N 90°0' E) to identify limiting growth characters for the efficient application of physiology breeding for higher yields. Results revealed that a relatively smaller portion of TDM was produced before flower initiation and the bulk of it after anthesis. The maximum CGR was observed during pod filling stage in all the varieties due to maximum leaf area (LA) development at this stage. Two plant characters such as LA and CGR contributed to the higher TDM production. Results indicated that high yielding mungbean varieties should possess larger LA, higher TDM production ability, superior CGR at all growth stages, and high relative growth rate and net assimilation rate at vegetative stage which would result in superior yield components.
Treated palm oil mill effluents (POME) is of great concern as it still has colour from its dissolved organics which may pollute receiving water bodies. In this study, the removal of colour from treated palm oil mill effluent were investigated through adsorption studies using carbon derived from wastewater sludge (WSC). Sludge from activated sludge plants were dried and processed to produce WSC. In this study, three different bed depths of WSC were used: 5 cm, 10 cm, and 15 cm. For each bed depth, the flowrate was varied at three different values: 100 mL/hr, 50 mL/hr and 25 mL/hr. It was found that at bed depth of 5 cm, the breakthrough curves were occurred at 360 min, 150 min and 15 min for flowrates of 25, 50 and 100 mL/hr respectively. It was observed that at a particular depth the exhaustion time for column reduced as flow rate increases. Kinetic models, Adams-Bohart and Yoon-Nelson were used to analyze the performance of the adsorption. It was found that rate constant for Adams Bohart model decreased with the increase in bed depth. Adsorption capacity obtained from Adams-Bohart model ranged from 2676.19 mg/L up to 8938.78 mg/L. The maximum adsorption capacity increases with smaller bed depth. For Yoon-Nelson model, the rate constant decreases with increase in bed depth. The required time for 50% breakthrough obtained from the models ranged from 17.01 to 104.17 minutes for all three bed depths. The reduction of colour was found to be effective at all bed depths. The experimental data was best described by both models as with higher values of correlation coefficient (R2).
In soybean breeding program, continuous selection pressure on traits response to yield created a genetic bottleneck for improvements of soybean through hybridization breeding technique. Therefore an initiative was taken to developed high yielding soybean variety applying mutation breeding techniques at Plant Breeding Division, Bangladesh Institute of Nuclear Agriculture (BINA), Bangladesh. Locally available popular cultivar BARI Soybean-5 was used as a parent material and subjected to five different doses of Gamma ray using Co60. In respect to seed yield and yield attributing characters, twelve true breed mutants were selected from M4 generation. High values of heritability and genetic advance with high genotypic coefficient of variance (GCV) for plant height, branch number and pod number were considered as favorable attributes for soybean improvement that ensure expected yield. The mutant SBM-18 obtained from 250Gy provided stable yield performance at diversified environments. It provided maximum seed yield of 3056 kg ha-1 with highest number of pods plant-1 (56). The National Seed Board of Bangladesh (NSB) eventually approved SBM-18 and registered it as a new soybean variety named 'Binasoybean-5' for large-scale planting because of its superior stability in various agro-ecological zones and consistent yield performance.
Rapid growth in industrialization and urbanization have resulted in high concentration of air pollutants in the environment and thus causing severe air pollution. Excessive emission of particulate matter to ambient air has negatively impacted the health and well-being of human society. Therefore, accurate forecasting of air pollutant concentration is crucial to mitigate the associated health risk. This study aims to predict the hourly PM2.5 concentration for an urban area in Malaysia using a hybrid deep learning model. Ensemble empirical mode decomposition (EEMD) was employed to decompose the original sequence data of particulate matter into several subseries. Long short-term memory (LSTM) was used to individually forecast the decomposed subseries considering the influence of air pollutant parameters for 1-h ahead forecasting. Then, the outputs of each forecast were aggregated to obtain the final forecasting of PM2.5 concentration. This study utilized two air quality datasets from two monitoring stations to validate the performance of proposed hybrid EEMD-LSTM model based on various data distributions. The spatial and temporal correlation for the proposed dataset were analysed to determine the significant input parameters for the forecasting model. The LSTM architecture consists of two LSTM layers and the data decomposition method is added in the data pre-processing stage to improve the forecasting accuracy. Finally, a comparison analysis was conducted to compare the performance of the proposed model with other deep learning models. The results illustrated that EEMD-LSTM yielded the highest accuracy results among other deep learning models, and the hybrid forecasting model was proved to have superior performance as compared to individual models.
Sustainable water demand management has become a necessity to the world since the immensely growing population and development have caused water deficit and groundwater depletion. This study aims to overcome water deficit by analyzing water demand at Kenyir Lake, Terengganu, using a fuzzy inference system (FIS). The analysis is widened by comparing FIS with the multiple linear regression (MLR) method. FIS applied as an analysis tool provides good generalization capability for optimum solutions and utilizes human behavior influenced by expert knowledge in water resources management for fuzzy rules specified in the system, whereas MLR can simultaneously adjust and compare several variables as per the needs of the study. The water demand dataset of Kenyir Lake was analyzed using FIS and MLR, resulting in total forecasted water consumptions at Kenyir Lake of 2314.38 m3 and 1358.22 m3, respectively. It is confirmed that both techniques converge close to the actual water consumption of 1249.98 m3. MLR showed the accuracy of the water demand values with smaller forecasted errors to be higher than FIS did. To attain sustainable water demand management, the techniques used can be examined extensively by researchers, educators, and learners by adding more variables, which will provide more anticipated outcomes.
Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBoost were applied to develop water level forecasting models in Muda River, Malaysia. The models were developed using limited amount of daily water level and meteorological data from 2016 to 2018. Different input scenarios were tested to investigate the performance of the models. The results of the evaluation showed that the MLP model outperformed both the LSTM and the XGBoost models in predicting water levels, with an overall accuracy score of 0.871 compared to 0.865 for LSTM and 0.831 for XGBoost. No noticeable improvement has been achieved after incorporating meteorological data into the models. Even though the lowest reported performance was reported by the XGBoost, it is the faster of the three algorithms due to its advanced parallel processing capabilities and distributed computing architecture. In terms of different time horizons, the LSTM model was found to be more accurate than the MLP and XGBoost model when predicting 7 days ahead, demonstrating its superiority in capturing long-term dependencies. Therefore, it can be concluded that each ML model has its own merits and weaknesses, and the performance of different ML models differs on each case because these models depends largely on the quantity and quality of data available for the model training.
The importance of graft copolymerization in the field of polymer science is analogous to the importance of alloying in the field of metals. This is attribute to the ability of the grafting method to regulate the properties of polymer 'tailor-made' according to specific needs. This paper described a novel plant-based coagulant, LE-g-DMC that synthesized through grafting of 2-methacryloyloxyethyl trimethyl ammonium chloride (DMC) onto the backbone of the lentil extract. The grafting process was optimized through the response surface methodology (RSM) using three-level Box-Behnken Design (BBD). Under optimum conditions, a promising grafting percentage of 120% was achieved. Besides, characterization study including SEM, zeta potential, TGA, FTIR and EDX were used to confirm the grafting of the DMC monomer chain onto the backbone of lentil extract. The grafted coagulant, LE-g-DMC outperformed lentil extract and alum in turbidity reduction and effective across a wide range of pH from pH 4 to pH 10. Besides, the use of LE-g-DMC as coagulant produced flocs with excellent settling ability (5.09 mL/g) and produced the least amount of sludge. Therefore, from an application and economic point of views, LE-g-DMC was superior to native lentil extract coagulant and commercial chemical coagulant, alum.
Occurrence of chalkiness in rice is attributed to genetic and environmental factors, especially high temperature (HT). The HT induces heat stress, which in turn compromises many grain qualities, especially transparency. Chalkiness in rice is commonly studied together with other quality traits such as amylose content, gel consistency, and protein storage. In addition to the fundamental QTLs, some other QTLs have been identified which accelerate chalkiness occurrence under HT condition. In this review, some of the relatively stable chalkiness, amylose content, and gel consistency related QTLs have been presented well. Genetically, HT effect on chalkiness is explained by the location of certain chalkiness gene in the vicinity of high-temperature-responsive genes. With regard to stable QTL distribution and availability of potential material resources, there is still feasibility to find out novel stable QTLs related to chalkiness under HT condition. A better understanding of those achievements is essential to develop new rice varieties with a reduced chalky grain percentage. Therefore, we propose the pyramiding of relatively stable and nonallelic QTLs controlling low chalkiness endosperm into adaptable rice varieties as pragmatic approach to mitigate HT effect.