Mitigating and adapting to the impacts of climate change at regional level require downscaled projection of future climate states. This paper examined the possible changes of future climate extremes over Malaysia based on the IPCC SRES A1B emission scenario. The projected changes at 17 stations were produced by bias correcting the UKMO PRECIS downscaling simulation output. The simulation expected higher probability of rainfall extreme occurrences over the west coast of Peninsular Malaysia during the autumn transitional monsoon period. In addition, possible early monsoon rainfall was projected for certain stations located over East Malaysia. The simulation also projected larger increase of warm temperature extremes but smaller decrease of cold extremes, suggesting asymmetric expansion of the temperature distribution. The impact of the elevated green house gases (GHG) is higher in the night time temperature extremes as compared to the day time temperature extremes. The larger increment of warm night frequencies as compared to the warm day suggests smaller diurnal temperature ranges under the influence of higher greenhouse gases. Stations located in East Malaysia were projected to experience the largest increase of warm night occurrence.
Current worldwide projections of sea-level rise show a staggering increase in water level of up to 2 m by 2100 owing to global warming exacerbated by anthropogenically induced climate change. While amplified rates of sea-level rise is an immense hazard to coastal communities, storm surges are expected to increase in intensity and frequency making it an equally significant threat to coastal populations. In France, these hazards are not uncommon with records of extreme tempests every thousand years in the Holocene. Despite these recurring devastating events, in the Bay of Saint-Brieuc, Brittany, legislated laws for coastal management do not entirely focus on protecting littoral zones from such calamities. 130,739 people are concentrated in 21 municipalities with major cities located at close proximity to the shoreline with numerous socio-economic activities, which increases the vulnerability of the coastal population and infrastructures; thus, affirming the indispensable need of a thorough vulnerability assessment. Here, we conduct a mechanistic appraisal of the vulnerability of the bay considering thirteen parameters within three governing sub-systems that demonstrate the multidimensional dynamics in these municipalities. In the occasion of an extreme climatic event, our results of total vulnerability show risks in the sub-systems highlighting erosional processes due to augmented hydrodynamics, socio-economic and administrative vulnerabilities associated with anthropogenic development. Eight municipalities of the bay portray moderate to very high vulnerability and the remaining exhibits a lower risk; however, not devoid of high vulnerabilities for certain sub-systems. We posit that a more accurate fit for predicting the total vulnerability of the region can be achieved by exclusively integrating physical-natural and administrative sub-system vulnerabilities. We propose generic but requisite recommendations for Integrated Coastal Zone Management such as surveillance of urban development along the coast, implementation of coastal defense systems and appropriate industrial corridors to attenuate and dispose hazardous refuse.
Dengue is fast becoming the most urgent health issue in Malaysia, recording close to a 10-fold increase in cases over the last decade. With much uncertainty hovering over the recently introduced tetravalent vaccine and no effective antiviral drugs, vector control remains the most important strategy in combating dengue. This study analyses the relationship between weather predictors including its lagged terms, and dengue incidence in the District of Tawau over a period of 12 years, from 2006 to 2017. A forecasting model purposed to predict future outbreaks in Tawau was then developed using this data. Monthly dengue incidence data, mean temperature, maximum temperature, minimum temperature, mean relative humidity and mean rainfall over a period of 12 years from 2006 to 2017 in Tawau were retrieved from Tawau District Health Office and the Malaysian Meteorological Department. Cross-correlation analysis between weather predictors, lagged terms of weather predictors and dengue incidences established statistically significant cross-correlation between lagged periods of weather predictors-namely maximum temperature, mean relative humidity and mean rainfall with dengue incidence at time lags of 4-6 months. These variables were then employed into 3 different methods: a multivariate Poisson regression model, a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a SARIMA with external regressors for selection. Three models were selected but the SARIMA with external regressors model utilising maximum temperature at a lag of 6 months (p-value:0.001), minimum temperature at a lag of 4 months (p-value:0.01), mean relative humidity at a lag of 2 months (p-value:0.001), and mean rainfall at a lag of 6 months (p-value:0.001) produced an AIC of 841.94, and a log-likelihood score of -413.97 establishing it as the best fitting model of the methodologies utilised. In validating the models, they were utilised to develop forecasts with the model selected with the highest accuracy of predictions being the SARIMA model predicting 1 month in advance (MAE: 7.032, MSE: 83.977). This study establishes the effect of weather on the intensity and magnitude of dengue incidence as has been previously studied. A prediction model remains a novel method of evidence-based forecasting in Tawau, Sabah. The model developed in this study, demonstrated an ability to forecast potential dengue outbreaks 1 to 4 months in advance. These findings are not dissimilar to what has been previously studied in many different countries- with temperature and humidity consistently being established as powerful predictors of dengue incidence magnitude. When used in prognostication, it can enhance- decision making and allow judicious use of resources in public health setting. Nevertheless, the model remains a work in progress- requiring larger and more diverse data.
This study describes the development of a multimedia environmental fate and transport model of dichlorodiphenyltrichloroethane (DDT) at Sungai Sayong watershed. Based on the latest estimated DDT emission, the DDT concentrations in air, soil, water and sediment as well as the transfer processes were simulated under the equilibrium and steady-state assumption. Model predictions suggested that soil and sediment was the dominant sink of DDT. The results showed that the model predicted was generally good agreement with field data. Compared with degradation reaction, advection outflow was more important processes occurred in the model. Sensitivities of the model estimates to input parameters were tested. The result showed that vapour pressure (Ps) and organic carbon water partition coefficient (KOC) were the most influential parameters for the model output. The model output-concentrations of DDT in multimedia environment is very important as it can be used in future for human exposure and risk assessment of organochlorine pesticides (OCPs) at Sungai Sayong Basin.
Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity and significant forecasting errors, primarily due to non-linear datasets and hyperparameter settings. To address these challenges, we have developed an innovative HDTO-DeepAR approach for predicting water quality indicators. This proposed approach is compared with standalone algorithms, including DeepAR, BiLSTM, GRU and XGBoost, using performance metrics such as MAE, MSE, MAPE, and NSE. The NSE of the hybrid approach ranges between 0.8 to 0.96. Given the value's proximity to 1, the model appears to be efficient. The PICP values (ranging from 95% to 98%) indicate that the model is highly reliable in forecasting water quality indicators. Experimental results reveal a close resemblance between the model's predictions and actual values, providing valuable insights for predicting future trends. The comparative study shows that the suggested model surpasses all existing, well-known models.
This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.
Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.
Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.
We used the global fire detection record provided by the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) to determine the number of fires detected inside 823 tropical and subtropical moist forest reserves and for contiguous buffer areas 5, 10, and 15 km wide. The ratio of fire detection densities (detections per square kilometer) inside reserves to their contiguous buffer areas provided an index of reserve effectiveness. Fire detection density was significantly lower inside reserves than in paired, contiguous buffer areas but varied by five orders of magnitude among reserves. The buffer: reserve detection ratio varied by up to four orders of magnitude among reserves within a single country, and median values varied by three orders of magnitude among countries. Reserves tended to be least effective at reducing fire frequency in many poorer countries and in countries beset by corruption. Countries with the most successful reserves include Costa Rica, Jamaica, Malaysia, and Taiwan and the Indonesian island of Java. Countries with the most problematic reserves include Cambodia, Guatemala, Paraguay, and Sierra Leone and the Indonesian portion of Borneo. We provide fire detection density for 3964 tropical and subtropical reserves and their buffer areas in the hope that these data will expedite further analyses that might lead to improved management of tropical reserves.
Although ageing is not yet a high priority issue for health planners, policy makers and clinicians in most developing countries, there will be a growing need in coming years to pay more attention to the important health issues associated with population ageing in the developing world. This paper reports some of the relevant findings of a cross-national study (sponsored by the World Health Organization) of the health and social aspects of ageing in four developing countries: Korea, the Philippines, Fiji and Malaysia. The key findings are compared and contrasted with those of a similar 11-country WHO study in Europe. In broad terms, the overall demographic, physical, mental health and social patterns and trends associated with ageing as demonstrated by age group and sex differences were consistent throughout the four countries studied. Comparisons with European findings in other similar studies underlined the fundamental universality of age-related changes in biophysical, behavioural and social characteristics. The importance of the family in developing countries was evident with about three-quarters of those aged 60 and over in the four countries living with children, often in extended family situations. Levels of adverse health-related behaviour and the prospect of changing patterns of morbidity with further increases in the total and proportional numbers of aged persons point to a need for emphasis on preventive health measures and programmes directed to the maintenance of the physical and mental health of the ageing population.
This paper presents the existing philosophy, approach, criteria and delivery of environmental engineering education (E3) for developing countries. In general, environmental engineering is being taught in almost all major universities in developing countries, mostly under civil engineering degree programmes. There is an urgent need to address specific inputs that are particularly important for developing countries with respect to the reality of urbanisation and industrialisation. The main component of E3 in the near future will remain on basic sanitation in most developing countries, with special emphasis on the consumer-demand approach. In order to substantially overcome environmental problems in developing countries, E3 should include integrated urban water management, sustainable sanitation, appropriate technology, cleaner production, wastewater minimisation and financial framework.
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
A first experiment on malaria control in the interior of Borneo by spraying with residual insecticides is described. The work was carried out in the isolated, sparsely populated valleys of the Baram River and its tributary, the Tinjar, in northern Sarawak. The experimental area was divided into three parts: a DDT test area, where a 75% suspension of wettable powder was applied at the rate of 2 g of DDT per m(2) of surface; a BHC test area, where a 50% suspension of wettable powder was applied at the rate of 0.10 g of gamma isomer per m(2); and a check area.Entomological investigations made before the spraying operations were started showed that Anopheles leucosphyrus Dönitz, 1901 was the main malaria vector in both the test and the check areas. Out of a total of 7568 A. leucosphyrus dissected, 30 gland infections were detected-a sporozoite-rate of 0.40%. A. barbirostris was found to be a secondary vector throughout the experimental area.THE RESULTS OF INSECTICIDE SPRAYING WERE SATISFACTORY: in the DDT test area, the spleen-rate fell from 51.8% to 25.1%, and the parasite-rate from 35.6% to 1.6%, in 21 months, and a similar reduction in the rates was observed in the BHC test area. In the check area, the spleen- and parasite-rates rose during the period of observations. It is considered that if such a degree of control can be obtained in 21 months, complete eradication can be expected in the near future.Although BHC spraying proved effective, the fact that it has to be repeated every three months makes it impracticable in the interior of Sarawak, where communications are very poor and difficulties of transport very great. DDT spraying, which need only be done twice a year, is therefore to be preferred. The cost of the DDT operations-US$ 0.45 per person protected per year-is comparatively high, owing to the difficulty of communications and to the necessity for spraying not only the village "longhouses", but also the temporary shelters which the semi-nomadic people in the interior of Sarawak build each year in the rice-fields.