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The impacts of climate change are becoming more widespread across the world, with hydro-meteorological extreme events on the rise, causing severe threats to nature and communities. Increasing trends in the frequency and intensity of floods and landslides have been projected by climate models. This necessitates the development of more effective measures such as nature-based solutions (NBS) which can complement grey infrastructures. Recent studies have identified knowledge gaps and limitations in existing research and tools that aid in spatial planning for the implementation of large-scale NBS and proposed new methodologies for the spatial allocation of large-scale NBS for flood risk reduction. This work presents a novel method for mapping the suitability of NBS addressing geo-hydrological hazards such as shallow landslides, debris flow, and rockfall, which are typically caused due to slope instability. This methodology incorporates landslide susceptibility mapping, and was used to create a toolbox ESRI ArcGIS environment to aid decision-makers in the planning and implementation of large-scale NBS. The spatial allocation toolbox was applied to the case study Portofino promontory, Liguria region, Italy, and 70% of the area was found to be highly susceptible to landslides. The produced suitability maps show that 41%, 33%, and 65% of the study area is suitable for the restoration of terraces, bio-engineering, and vegetative measures such as NBS for landslide risk reduction.

The river sand mining activity has taken place in a sand-bed river system in Selangor, Malaysia for decades even before the legalisation was initiated in 2008. This study focuses on the determination of optimum sand extraction for low-flow and high-flow seasons. The sand replenishment rate was used as the benchmark in determining the threshold level of the extractable rate in the Langat River, Selangor system. The total sediment load was computed using Yang (1973) equation due to the high percentage of agreement between the predicted sediment load and measured sediment load. Almost 41.6% of the predicted data fall within the allowable discrepancy ratio test between the predicted value and measured value. The comparison of sand replenishment rate in high and low flow seasons proved that the river system has quicker capabilities in sand replenishment rate at the extraction point during the high-flow season compared to the latter by 83%. The quantifiable volume of the extractable sand rate is proposed specifically during low-flow months (May to September) whereby the slower replenishment rate is critical and riskier. The optimal sand mining volume during the low-flow months is calculated by reducing 10% from the total replenishment volume and the recommended optimum extraction load has been delivered by the number of 25 tons lorries for easier observation by the contractor and authority’s body. The monthly optimum extraction in Langat River during low-flow months is calculated at a minimum of 437 trucks to a maximum of 20,114 trucks per month.

Considering the spatial–temporal variation of renewable energy (RE) resources, assessment of their complementarity is of great significance for decision-makers to increase the stability of power output and reduce the need for storage systems. In this regard, the current paper presents a roadmap to assess the temporal complementarity patterns between wind and solar resources for the first time in Iraq. A new approach based on re-analyzed climate data, Landcover products, and geographical information system (GIS) is proposed. As such, renewable resource datasets are collected for 759 locations with a daily timescale over five years. Landcover classes are translated into wind shear coefficients (WSCs) to model wind velocity at turbine hub height. Then, the Pearson correlation coefficient (PCC) is applied to calculate the complementarity indices for each month of the year. Results of this investigation reveal that there are significant synergy patterns spanning more than six months in the southwestern regions and some eastern parts of Iraq. The highest complementarity is observed in March and December with a value of −0.70 and −0.63, respectively. Despite this promising potential, no typical temporal complementarity has been discovered that would completely eliminate the fluctuations of clean power generation. However, the synergistic properties yielded by this work could mitigate the reliance on storage systems, particularly as they cover important regions of the country. The proposed approach and tools can help improve the planning of renewable energy systems.

The uses of clay pipes in irrigation water management are becoming popular, especially in arid and semi-arid soils. The study examined clay and zeolite materials for irrigation pipes, and this paper reported characteristic properties of these materials. Hydrometer, pychnometer and core sampler methods were for soil physical properties determination. Consistency tests using (Atterberg method) and analytical techniques (Scanning Electron Microscopy, SEM and Energy Dispersive Spectroscopy, EDS) for samples analyses. The results indicate the soil to contain 11 % sand, 34 % silt and 55 % clay fractions and texturally classified as clay with a particle density of 1.58 g/cm3 and bulk density 2.43 g/cm3 respectively. Addition of zeolite at 3:1 clay/zeolite mix ratio shows liquid limit (LL) and plastic (PL) values to decreases from 50.7% to 43.7% and 27.6% to 27.3% while plasticity index, (PI) change from 23.2 to 16.7 respectively. The shrinkage rate decreases from 11.67% for raw clay to 8.92 % for the treated sample. The EDS analysis shows both clay and zeolite samples to contain carbon, silica (SiO2) and alumina (Al2O3) as the major constituents with ferric oxide (Fe2O3), potassium oxide (K2O) and cobalt (Co) as the minor constituents. The major constituents contribute 89.26 and 94.4% while minor contribute 10.74 and 5.59 % in clay and zeolite samples. Modifying clay improved its workability, reduces cracking potential and absorption capacity and performance of porous clay pipes.

The oil palm is the largest plantation industry in Malaysia. It has been one of the major contributors to the country’s economy and the main pillar of the commodity sectors. For over 40 years, the oil palm industry has faced a lethal and incurable disease, Basal Stem Rot (BSR), which is caused by a type of bracket fungus, Ganoderma boninense. The oil palm physical symptoms infected by BSR disease are appearance of many unopened spears, flattening of crown and smaller crown size. Terrestrial Laser Scanning (TLS, also known as ground-based LiDAR) can be used to provide accurate and precise information on tree morphology with high resolution. This study proposed an image processing technique using the ground input data taken from a TLS. Five parameters were used in the study are number of laser hits in strata 200 cm and 850 cm from the top, namely as C200 and C850, respectively, crown area, frond number and frond angle. The objectives of this study are to analyse the relationship between the parameters and to study the relationship of the parameters with the levels of BSR disease. Results have shown that all parameters were significant in all levels of healthiness with p-values less than 5%. Frond number and frond angle showed the highest correlation value, which is equal to -0.94. Frond angle is increasing, while frond number and crown area are decreasing concurrently with the severity levels of BSR infection.

Terrestrial laser scanning technology is an advanced active remote sensing ranging method that is well suited for yielding high-resolution scans of the morphology of a tree, which is an indicator of the health of the plant. The Ganoderma boninense fungus causes basal stem rot (BSR), which threatens the oil palm industry in Malaysia. To date, the current practice of inspection in a plantation is conducted every 6 months. Monitoring the progress with a closer gap is required to comprehend if any changes can be seen earlier than 6 months. Therefore, the objectives of this study were to identify the most suitable parameters of the oil palm trees to detect the BSR disease based on temporal laser scanning data and to identify suitable time frames for monitoring the progress of the symptoms of the disease. Terrestrial laser scanning data was used to analyse changes in the crown and frond metrics of oil palm trees with two different scan durations i.e., 2- and 4-months after the first scan. This spatio-temporal data is important in the precision agriculture field for better oil palm management, to understand the disease development for long-term solutions and also to provide a fast response so that appropriate treatment can be given to the palm as early as possible. Statistical analyses, i.e., the Kruskal–Wallis test with α = 0.05 and the Wilcoxon post-hoc test, were conducted to determine significant differences in the parameters at different points in time. The results show that crown strata number 17 (850 cm from the top) and the crown area were the most suitable parameters to be used. Furthermore, the oil palm trees with BSR can be detected by comparing the 4-month scan or the second 2-month scan. The results have shown that the effect of Ganoderma boninense infection can be differentiated at the later stage. In conclusion, the changes can be measured at 4-months after the first inspection, thus helping to preventing crop losses.

Aim: To assess and compare clinico-microbiological efficacy of Moxifloxacin (0.5%) plus Vancomycin (2.5%) combined therapy to Fortified Vancomycin (5%) in bacterial keratitis. Materials and Methods: The present study was conducted on patients who visited eye OPD, J.N.M.C.H and Gandhi Eye Hospital, Aligarh. Patients with bacterial keratitis were diagnosed and included in the study. Right eyes were instilled moxifloxacin plus vancomycin combination therapy and Left eyes were instilled fortified vancomycin. Microbiological results were recorded on first visit and on follow up visits during course of treatment. Results: Total of 32 patients were included in the study. At first visit, only 8 cases were positive for bacterial culture with coagulase negative Staphylococcus (CoNS) being the most common organisms. All the smears became negative in first follow-up (7th day) in both the groups. No significant difference were found in clinical and microbiological study outcome betweenthe two-study groups. Conclusions: We conclude that both the drugs, i.e. moxifloxacin (0.5%) plus vancomycin (2.5%) combined therapy and fortified vancomycin (5%) had good efficiency clinically and in microbial elimination in cases of bacterial keratitis with equivalent outcome. Keywords: Keratitis, Corneal ulcer, Bacterial keratitis, Efficacy.

Soil erosion is a serious environmental challenge which persistently diminishes available land resources globally.The impact of soil erosion is more severeat hilly regions where various techniques are deployed to evaluateits risk levels. However, the traditional approach of estimating the magnitude of erosion is tedious, costly, and considerably time consuming. This study was intended to assess the risk associated with soil erosion at hilly areas of Cameron Highlands through geospatial means. The Digital Elevation Model (DEM) with 5m resolution from Interferometric Synthetic Aperture Radar (IfSAR) was utilized to generate the slope inthe highlands. Soil erosion rates was estimated using Universal Soil Loss Equation (USLE), while information about land use and cover were sourced from relevant government agencies. Inversed Distance Weighted (IDW) method of spatial interpolation was applied to predict the values of unknown pixels. The analysis shows that, there is 217.5 km2of the highlands which is greater than 45-degreeaccounted for about 30.5% of the total land area. Moreover, erosion risk assessment indicated that 66.3%, 11.4%, 11.7% and 10.8% are respectively classified as very low, law, moderate and high susceptible to soil erosion. In general, the risk of soil erosion is relatively low and could be attributed to den sevegetation coverage within the study watershed despite the steep slopes where it was found to be at very high risk to soil erosion susceptibility. However, there is need to deploy best management practices to reduce the effect of soil disturbances at hilly areas and prevent excessive soil loss in future.

Hydrological events are expected to increase in both magnitude and frequency in tropical areas due to climate variability. The Intensity – Duration – Frequency (IDF) curves are important means of evaluating the efficiency of irrigation and drainage systems. The necessity to update IDF curves arises from the need to gain better understanding of the impacts of climate change. This study explores an approach based on weighted Global Circulation Models (GCMs) and temporal disaggregation method to develop future IDFs under Representative Concentration Pathways (RCP) emission scenarios. The work consists of 20 ensemble GCMs, three RCPs (2.6, 4.5, and 8.5) and two projection periods (2050s and 2080s). The study compared three statistical distributions and selected Generalized Extreme Value (GEV) being the best fitting distribution with baseline rainfall series and therefore used for IDF projection. The result obtained shows that, the highest rainfall intensities of 19.32, 35.07 and 39.12 mm/hr occurred under 2-, 5-, and 20 years return periods, respectively. IDFs from the multi-model ensemble GCMs have shown increasing intensity in the future for all the return periods. This study indicated that the method could produce promising results which can be extended to other catchments.

Rainfall erosivity is one of the main factors of soil erosion that is expected to change as climate variables alter and impact soil conservation policies significantly. It is important to understand the potential trends in rainfall erosivity and its impacts on the environment, especially in tropical areas where severe rainfall is expected to continue to rise. This study aimed to project spatial and temporal rainfall erosivity factor using ensemble Global Circulation Models (GCMs). The study employed 20 GCMs, four RCPs scenarios, and two projection timescales (2050s and 2080s). The erosivity factor was determined by integrating baseline rainfall intensity with Modified Fournier Index (MFI). The values of erosivity were then interpolated using GIS software, and thematic maps were generated for these variables. The result shows 92% coefficient of determination between observed and simulated rainfalls. The ensemble model revealed that the MAE, SE and RMSE are 0.1487, 1.3692 and 1.2499, respectively. The relative increment of rainfall erosivity ranged from 4.7% to 122% with the highest value of 6292 MJmmha−1 hr−1 yr−1 by 2080s at Kg. Raja sub-basin located at North-Western part of the watershed. Similarly, the peak flow response through Ringlet river was expected to increase in the range of 4.72–35.8% with peak discharge in December by 2080s under RCP8.5 emission scenario. This study disclosed a potential increase in rainfall erosivity, and availability of water resources influenced by climate change which require appropriate conservation strategies.

Palestinian healthcare institutions face difficulties in providing effective service delivery, particularly in times of crisis. Problems arising from inadequate healthcare service delivery are traceable to issues such as spatial coverage, emergency response time, infrastructure, and manpower. In the Gaza Strip, specifically, there is inadequate spatial distribution and accessibility to healthcare facilities due to decades of conflicts. This study focuses on identifying hospital site suitability areas within the Gaza Strip in Palestine. The study aims to find an optimal solution for a suitable hospital location through suitability mapping using relevant environmental, topographic, and geodemographic parameters and their variable criteria. To find the most significant parameters that reduce the error rate and increase the efficiency for the suitability analysis, this study utilized machine learning methods. Identification of the most significant parameters (conditioning factors) that influence a suitable hospital location was achieved by employing correlation-based feature selection (CFS) with the search algorithm (greedy stepwise). Thus, the suitability map of potential hospital sites was modeled using a support vector machine (SVM), multilayer perceptron (MLP), and linear regression (LR) models. The results of the predicted sites were validated using CFS cross-validation and the receiver operating characteristic (ROC) curve metrics. The CFS analysis shows very high correlations with R2 values of 0.94, 0. 93, and 0.75 for the SVM, MLP, and LR models, respectively. Moreover, based on areas under the ROC curve, the MLP model produced a prediction accuracy of 84.90%, SVM of 75.60%, and LR of 64.40%. The findings demonstrate that the machine learning techniques used in this study are reliable, and therefore are a promising approach for assessing a suitable location for hospital sites for effective health delivery planning and implementation.

The interest in visual-based surveillance systems, especially in natural disaster applications, such as flood detection and monitoring, has increased due to the blooming of surveillance technology. In this work, semantic segmentation based on convolutional neural networks (CNN) was proposed to identify water regions from the surveillance images. This work presented two well-established deep learning algorithms, DeepLabv3+ and SegNet networks, and evaluated their performances using several evaluation metrics. Overall, both networks attained high accuracy when compared to the measurement data but the DeepLabv3+ network performed better than the SegNet network, achieving over 90% for overall accuracy and IoU metrics, and around 80% for boundary F1 score (BF score), respectively. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background but the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, the DeepLabv3+ network was used for practical application using a set of images captured at five consecutive days in the study area. The segmentation result and water level markers extracted from light detection and ranging (LiDAR) data were overlaid to estimate river water levels and observe the water fluctuation. River water levels were predicted based on the elevation from the predefined markers. The proposed water level framework was evaluated according to Spearman’s rank-order correlation coefficient. The correlation coefficient was 0.91, which indicates a strong relationship between the estimated water level and observed water level. Based on these findings, it can be concluded that the proposed approach has high potential as an alternative monitoring system that offers water region information and water level estimation for flood management and related activities.

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulted in ecological changes of aquatic ecosystems, affected the aquatic food supply chain, and disrupted the socio-economy of global populations. Due to reduced human activities during the pandemic, the aquatic environment was reported to improve its water quality, wild fishery stocks, and biodiversity. However, the sudden surge of plastics and biomedical wastes during the COVID-19 pandemic masked the positive impacts and increased the risks of aquatic pollution, especially microplastics, pharmaceuticals, and disinfectants. The transmission of SARS-CoV-2 from wastewater treatment plants to natural water bodies could have serious impacts on the environment and human health, especially in developing countries with poor waste treatment facilities. The presence and persistence of SARS-CoV-2 in human excreta, wastewaters, and sludge and its transmission to aquatic ecosystems could have negative impacts on fisheries and aquaculture industries, which have direct implications on food safety and security. COVID-19 pandemic-related environmental pollution showed a high risk to aquatic food security and human health. This paper reviews the impacts of COVID-19, both positive and negative, and assesses the causes and consequences of anthropogenic activities that can be managed through effective regulation and management of eco-resources for the revival of biodiversity, ecosystem health, and sustainable aquatic food production.

Stability plays a vital role in any robotic system. Its significance increases in systems related to health and medicine. For rehabilitation devices meant for Spinal Cord Injury (SCI) patients, stability is crucial and key element in ensuring patient safety and the usefulness of the devices. In this study, kinematics, force analysis, and the static tip-over stability of a device for rehabilitation of paraplegic patients is discussed. Kinematics modeling and static force analysis provide critical information about position and loading at different points on the device. Force-Angle Stability Criterion is used to find the static tip-over stability of the device while the patient is on board the device. The Criterion relies on the support boundary, tip-over mode axes, and the Center of Mass (COM) of the complete system. The Criterion is sensitive to the COM position and therefore is more suitable for the application. The linear actuator mounted on the device causes the end effector of the device to move. The patient, strapped with the end effector, in turn moves from sitting position to standing position. The study focuses on the analysis of stability based on changing COM during this motion. The results verify that although the system is well within the stability bounds, it is more stable as it moves from sitting position to standing position.

Basal stem rot (BSR), caused by the Ganoderma fungus, is an infectious disease that affects oil palm (Elaeis guineensis) plantations. BSR leads to a significant economic loss and reductions in yields of up to Malaysian Ringgit (RM) 1.5 billion (US$400 million) yearly. By 2020, the disease may affect ∼1.7 million tonnes of fresh fruit bunches. The plants appear symptomless in the early stages of infection, although most plants die after they are infected. Thus, early, accurate, and nondestructive disease detection is crucial to control the impact of the disease on yields. Terrestrial laser scanning (TLS) is an active remote-sensing, noncontact, cost-effective, precise, and user-friendly method. Through high-resolution scanning of a tree’s dimension and morphology, TLS offers an accurate indicator for health and development. This study proposes an efficient image processing technique using point clouds obtained from TLS ground input data. A total of 40 samples (10 samples for each severity level) of oil palm trees were collected from 9-year-old trees using a ground-based laser scanner. Each tree was scanned four times at a distance of 1.5 m. The recorded laser scans were synched and merged to create a cluster of point clouds. An overhead two-dimensional image of the oil palm tree canopy was used to analyze three canopy architectures in terms of the number of pixels inside the crown (crown pixel), the degree of angle between fronds (frond angle), and the number of fronds (frond number). The results show that the crown pixel, frond angle, and frond number are significantly related and that the BSR severity levels are highly correlated (R2 = 0.76, P < 0.0001; R2 = 0.96, P < 0.0001; and R2 = 0.97, P < 0.0001, respectively). Analysis of variance followed post hoc tests by Student–Newman–Keuls (Newman–Keuls) and Dunnett for frond number presented the best results and showed that all levels were significantly different at a 5% significance level. Therefore, the earliest stage that a Ganoderma infection could be detected was mildly infected (T1). For frond angle, all post hoc tests showed consistent results, and all levels were significantly separated except for T0 and T1. By using the crown pixel parameter, healthy trees (T0) were separated from unhealthy trees (moderate infection [T2] and severe infection [T3]), although there was still some overlap with T1. Thus, Ganoderma infection could be detected as early as the T2 level by using the crown pixel and the frond angle parameters. It is hard to differentiate between T0 and T1, because during mild infection, the symptoms are highly similar. Meanwhile, T2 and T3 were placed in the same group, because they showed the same trend. This study demonstrates that the TLS is useful for detecting low-level infection as early as T1 (mild severity). TLS proved beneficial in managing oil palm plantation disease.

Knowing actual nutrient requirements for rice plants is crucial in supplying the correct amount of fertilizer, especially nitrogen since nitrogen is one of the most key limiting nutrients in rice c...

The AquaCrop model, calibrated for 2002 and validated for 2003, is used to simulate sugar beet root dry yield, dry biomass, water productivity based on irrigation (WPi), and water productivity based on total water input (WPi+p) in an experimental field of the Karaj Sugar Beet Seed Institute (Karaj, Iran). Three irrigation treatments including full irrigation, 75% deficit irrigation, and 50% deficit irrigation were carried out in the main plots. The results of statistical comparison between the model output and observed data in the calibration (2002) and validation (2003) years showed that the AquaCrop model reliably simulated sugar beet yield and the biomass under different genotypes and irrigation levels. AquaCrop did a better job of simulating dry biomass than root dry yield. The findings show that by decreasing water input, including irrigation and precipitation, WPi, and WPi+p will increase. In total, statistical indicators and scatter plots indicated that the AquaCrop model had enough fitness to predict yield, biomass, and water productivity for the future. Keywords: AquaCrop, Yield; Biomass, WPi, WPi+p.

Rehabilitation and assistive technologies are touching new bounds of excellence due to the advent of more user friendly human–machine interfaces (HMI) and ergonomic design principles. Among the most fundamental movements which are required in performing activities of daily living is the sit and stand motion, a device is proposed in this study which enables a patient to perform his activities of daily living (ADL) tasks by enabling them to sit, stand and move without the need of an assistant. The device, in this study, is proposed to be activated by an electroencephalogram (EEG) based intention acquisition system. The intention is acquired from eye blinks. The EEG based intention detection system converts eye blinks to respective commands after classification of eye blink signals collected using EMOTIVE® EPOCH+ headset. These control commands then trigger the control algorithm which then actuates and controls the system states. For the later, two control schemes namely proportional integral derivative (PID) control and sliding mode control (SMC) are tested in this study. The simulation and experimental results are given. The experimental setup consists of an offline EEG signal classification module, Simulink® model and the prototype of the actual device. It is concluded that SMC performs far better than PID for control of the assistive device in ensuring patient comfort during motion. This project is a joint venture by Higher Education Commission of Pakistan and Department of Mechatronics and Biomedical Engineering (DMBE) at Air University. The Project is financially supported by Higher Education Commission of Pakistan under the Technology Development Fund 2018 (Grant number: TDF02-223).

Water-saving technologies have been gaining interest as a way of saving water while minimizing crop production losses. This study aims to introduce a new technique for reducing the vertical...

Over the years, studies have been conducted to examine not only the relationship between land use and water pollution using ordinary least square (OLS) regression but also their spatially varying relationships using geographical weighted regression (GWR). However, the relationships between land use and water quality indicators may vary not only spatially but also over time. Therefore, this study was conducted to analyse the spatiotemporal variations in land use-water quality relations over time within a tropical watershed. To achieve this goal, land use data for 2006, 2010, and 2015 and the corresponding years of frequently sampled water quality data were utilised. The kriging interpolation technique was applied to estimate the unknown water quality values at 12 unmonitored sampling points using the measured values from the 9 monitored sites that were used for the regression analyses. Both OLS and GWR were applied using four groups of land use and seven water quality variables. The study found better performances of GWR models over OLS models throughout these investigation periods. The GWR results indicated that the ability of land use indicators to explain water quality change is not the same with time and space and neither the same among the different water quality parameters. For examples, in 2015, agricultural land most predicted the change in most water quality variables compared with its prediction proportion in 2010 and 2006, while urban land most predicted the change in most water quality variables in 2010 compared to other years. However, other groups of lands were more positively associated with most of the water pollutants compared to forest, agricultural, and urban areas. Therefore, this study suggests that control and management policies be adjusted to different periods and areas according to the time-space varying sources of pollution and good predictors of water quality. Although the approach used in this study tends to reveal the complex spatiotemporal relationships between land use and water quality over different time periods, it should be noted that this approach is more appropriate for comparative analysis to understand the implication of the land use chance pattern into surface water pollution

Precipitation is sensitive to increasing greenhouse gas emission which has a significant impact on environmental sustainability. Rapid change of climate variables is often result into large variation in rainfall characteristics which trigger other forms of hazards such as floods, erosion, and landslides. This study employed multi-model ensembled general circulation models (GCMs) approach to project precipitation into 2050s and 2080s periods under four RCPs emission scenarios. Spatial analysis was performed in ArcGIS10.5 environment using Inverse Distance Weighted (IDW) interpolation and Arc-Hydro extension. The model validation indicated by coefficient of determination, Nash–Sutcliffe efficiency, percent bias, root mean square error, standard error, and mean absolute error are 0.73, 0.27, 20.95, 1.25, 0.37 and 0.15, respectively. The results revealed that the Cameron Highlands will experience higher mean daily precipitations between 5.4 mm in 2050s and 9.6 mm in 2080s under RCP8.5 scenario, respectively. Analysis of precipitation concentration index (PCI) revealed that 75% of the watershed has PCI greater than 20 units which indicates substantial variability of the precipitation. Similarly, there is varied spatial distribution patterns of projected precipitation over the study watershed with the largest annual values ranged between 2900 and 3000 mm, covering 71% of the total area in 2080s under RCP8.5 scenario. Owing to this variability in rainfall magnitudes, appropriate measures for environmental protection are essential and to be strategized to address more vulnerable areas.

Soil erosion is a serious environmental challenge which is persistently diminishing the available land resource in many places around the world, particularly the highlands areas. The traditional approach of estimating the magnitude of erosion is tedious, costly, and considerably time consuming. This study is aimed at assessing the risk level associated with soil erosion at hilly areas of Cameron Highlands through Interferometric Synthetic Aperture Radar (InSAR). The digital elevation model with 5 m resolution was utilized to generate the slope map for the highlands. Soil erosion rates was estimated using universal soil loss equation, while information about land use and cover were sourced from relevant government agencies. The analysis shows that, there was about 217.5 km2 (30.5%) of highlands fall under severely steep zone with slope ≥ 45-degree. Moreover, erosion risk assessment indicated that; 66.3%, 11.4%, 11.7% and 10.8% of the severe sloppy lands are classified as very low to high susceptible to soil erosion respectively. In general, the risk of soil erosion is relatively low and could be attributed to large vegetation coverage despite steep slopes. However, there is need to deploy a control measures to reduce soil disturbance activities on highlands with extremely steep slope as a proactive measures to minimize the effect of potential soil erosion.

The oil palm industry is vital for the Malaysian economy. However, it is threatened by the Ganoderma boninense fungus, which causes basal stem rot (BSR) disease. Foliar symptoms of the disease include the appearance of several unopened spears, flat crowns, and small crown size. The effect of this disease depends on the severity of the infection. Currently, the disease can be detected manually by analyzing the oil palm tree&rsquo, s physical structure. Terrestrial laser scanning (TLS) is an active ranging method that uses laser light, which can directly represent the tree&rsquo, s external structure. This study aimed to classify the healthiness levels of the BSR disease using a machine learning (ML) approach. A total of 80 oil palm trees with four different healthiness levels were pre-determined by the experts during data collection with 40 each for training and testing. The four healthiness levels are T0 (healthy), T1 (mildly infected), T2 (moderately infected), and T3 (severely infected), with 10 trees in each level. A terrestrial scanner was mounted at a height of 1 m, and each oil palm was scanned at four positions at a distance of 1.5 m around the tree. Five tree features were extracted from the TLS data: C200 (crown slice at 200 cm from the top), C850 (crown slice at 850 cm from the top), crown area (number of pixels inside the crown), frond angle, and frond number. C200 and C850 were obtained using the crown stratification method, while the other three features were obtained from the top-down image. The obtained features were then analyzed by principal component analysis (PCA) to reduce the dimensionality of the dataset and increase its interpretability while at the same time minimizing information loss. The results showed that the kernel na&iuml, ve Bayes (KNB) model developed using the input parameters of the principal components (PCs) 1 and 2 had the best performance among 90 other models with a multiple level accuracy of 85% and a Kappa coefficient of 0.80. Furthermore, the combination of the two highest PC variance with the most weighted to frond number, frond angle, crown area, and C200 significantly contributed to the classification success. The model also could classify healthy and mildly infected trees with 100% accuracy. Therefore, it can be concluded that the ML approach using TLS data can be used to predict early BSR infection with high accuracy.

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976&ndash, 2005) and future (2010&ndash, 2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.

This study focuses on suitable site identification for constructing a hospital in Malacca, Malaysia. Using significant environmental, topographic, and geodemographic factors, the study evaluated and compared machine learning (ML) and multicriteria decision analysis (MCDA) for hospital site suitability mapping to discover the highest influential factors that minimize the error ratio and maximize the effectiveness of the suitability investigation. Identification of the most significant conditioning parameters that impact the choice of an appropriate hospital site was accomplished using correlation-based feature selection (CFS) with a search algorithm (greedy stepwise). To model the potential hospital site map, we utilized multilayer perceptron (MLP) and analytical hierarchy process (AHP) models. The outcome of the predicted site models was validated utilizing CFS 10-fold cross-validation, as well as ROC curve (receiver operating characteristic curve). The analysis of CFS indicated a very high correlation with R2 values of 0.99 for the MLP model. However, the ROC curve indicated a prediction accuracy of 80% for the MLP model and 83% for the AHP model. The findings revealed that the MLP model is reliable and consistent with the AHP. It is a sufficiently promising approach to the location suitability of hospitals to ensure effective planning and performance of healthcare delivery.

Earth is faced with dramatic changes in the weather systems, which leads to climate change. Climate change affects water resources and crop production. In this study, five and seven general circulation models (GCMs) were respectively collected via the IPCC Fourth and Fifth Assessment Reports. Emission scenarios including B1, A1B, and A2 for AR4 and RCP2.6 and RCP8.5 for AR5 were applied to predict future climate change. The weighting method of mean observed temperature-precipitation (MOTP) was utilized to compute uncertainty related to different climate models. The scenario files made by ΔT and ΔP were applied to the downscaled model of LARS-WG to generate weighted multi-model ensemble means of temperature and precipitation for the period 2020–2039 centered on 2030s. These ensemble means were incorporated into the calibrated AquaCrop model to predict final yield and biomass. In this study, soybean data were applied for four different varieties under three irrigation treatments in field experiments carried out at Karaj Seed and Plant Improvement Institute in two successive years. However, the results of statistical analysis between the model output and observed data for all varieties and irrigation treatments in the calibration year (2010) and validation year (2011) were the same at the 95% confidence level. It is suggested that AquaCrop is a valid model to predict yield and biomass for the study area in the future. Furthermore, comparing future climatic variables to the historical period during the soybean growing season showed enhancement of these variables by the 2030s. The amplitude change of temperature was larger in AR5, whereas the amplitude change of precipitation and CO2 were larger in AR4. The soybean yield and biomass increased for all treatments in the 2030 s with positive correlation with the climatic variables. The maximum temperature represented the most significant correlation with yield and biomass for almost all treatments. Finally, soybeans might achieve an optimal threshold temperature in the future, leading to yield increases in the 2030s.

The main objectives of this paper are to design and evaluate a hybrid approach based on Gaussian mixture model (GMM) and random forest (RF) for detecting rockfall source areas using airborne laser scanning data. The former model was used to calculate automatically slope angle thresholds for different type of landslides such as shallow, translational, rotational, rotational-translational, complex, debris flow, and rockfalls. After calculating the slope angle thresholds, a homogenous morphometric land use area (HMLA) was constructed to improve the performance of the model computations and reduce the sensitivity of the model to the variations in different conditioning factors. After that, the support vector machine (SVM) was applied in addition to backward elimination (BE) to select and rank the conditioning factors considering the type of landslides. Then, different machine learning methods [artificial neural network (ANN), logistic regression (LR), and random forest (RF) were trained with the selected best factors and previously prepared inventory datasets. The best fit method (RF) was then used to generate the probability maps and then the source areas were detected by combining the slope raster (reclassified according to the thresholds found by the GMM model) and the probability maps. The accuracy assessment shows that the proposed hybrid model could detect the potential rockfalls with an accuracy of 0.92 based on training data and 0.96 on validation data. Overall, the proposed model is an efficient model for identifying rockfall source areas in the presence of other types of landslides with an accepted generalization performance.

Flood is an annual disaster in Malaysia, especially in the east coast region. Recently, other regions in Malaysia have also experience devastating flood. A flood aerial monitoring is one of the approaches that involved in the flood disaster management. Compared to space borne remote sensing, aerial platforms are much reliable to obtain higher spatial resolution and real time data. Due to cloud coverage and revisit limitations, space borne remote sensing approach is a less desirable option for flood monitoring. In this paper, a review of existing aerial platforms that perform remote sensing task under various weather conditions, specifically for flood monitoring is presented. There are four types of aerial platforms being reviewed, which are rotary wings, fixed wings, blimps and helikites. The main criteria of the review are the payload capacity, endurance (flight time), altitudes, tolerable wind speed, cost, vertical take-off and landing ability, and the ability to perform under various weather conditions, such as heavy precipitation and winds. From this review, the applications of aerial platform in recent studies never mentioned the capability of aerial platform used in such weather conditions. From all types of aerial platform discussed, helikite seems to be the most suitable to fly in extreme weather. The only drawback is that helikite has mobility issue since it is tethered to the ground. The application of helikite is useful for a small area coverage of flood monitoring. The authors will continue the study to evaluate the reliability of helikite in performing such task in the future.

Malaysia receives a lot of water from its two main monsoon periods. Generally, there is a lot of precipitation throughout the year, with drought periods lasting less than three months. To date, irrigation has been treated homogenously, even though soil properties can vary spatially over a field, requiring site-specific applications. The aim of this study was to establish an irrigation management zone (IMZ) covering 23.4 ha, which was previously determined under the same soil series. Soil sampling was done according to a grid system over an area of 100 m × 100 m. Three soil depth ranges were examined for every sampling point, namely 0–30, 30–60, and 60–90 cm from the soil surface. Samples were taken to a laboratory for physical analysis and determination of the available water-holding capacity (AWHC). Delineation of AWHC values was achieved using GIS software and the Kriging method. Estimated irrigation depth (EID) data for the plantation were collected for the years 2016 and 2017. Afterward, EID and total net irrigation (TNI) data were simulated in the FAO Cropwat model and compared. The results showed that clay, sand, and organic matter (OM) distributions varied with soil depth, however, no strong correlation was found between these variable with AWHC. The IMZ was classified into three areas named zones A, B, and C, ranging from 79 to 167 mm. The crop water requirement (CWR) was 667 mm in 2016 but only 260 mm in 2017. Based on the AWHC values, the EID for 2016 was found to be below the TNI requirement range of about 106 to 110 mm. In contrast, the EID range was approximately 34 to 62 mm and above TNI requirements for 2017. This study indicates that water inputs for irrigation can be optimized with knowledge of the water-holding capacity of a specific soil. Subsequently, this can be related to crop yield and the impact on sustainable agriculture.

Soil water dynamics under drip irrigation systems are of considerable importance in designing, managing and operating these systems. Emitter discharge, soil type, soil chemical properties, crop water-requirements, and quality of applied water are the main factors affecting soil water dynamics under drip irrigation. In this study, laboratory experiments were conducted to study the effect of magnetized water on wetting pattern dimensions and water content distributions under surface emitter. Passing water through permanent or electro magnets installed on feeding pipeline resulted in producing magnetized water. Two emitter discharges (3 and 4.5 l/h, in average), two soil types (sand and clay), two soil profiles (homogeneous and layered-textural) and two water types (plain and magnetized water) were considered in the experiments. It was found that using magnetized water led to increase surface wetted radius by 6.2% and decrease vertical wetted depth by 6.3% in homogeneous soil profiles. In layered-textural soil profiles, the surface wetted radius slightly decreased by 1.8% while the vertical wetted depth increased by 7.0% in case of sand over clay and decreased by 2.0% in case of clay over sand when using magnetized water. As a result of using magnetized water, the total wetted area decreased for homogeneous profiles and increased for layered-textural profiles. It was concluded that the impact of magnetized water is statistically significant on wetted bulb dimensions and not statistically significant on water content distributions. The results revealed that using magnetized water is recommended especially in homogeneous soil profiles.