Johor River Basin Research Articles

A framework for assessing the adequacy of Water Quality Index – Quantifying parameter sensitivity and uncertainties in missing values distribution

Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQIADJUSTED) to handle missing values, and capitalise on the remaining available information for the development of a WQI. A Sub-WQI was also developed to address local water quality conditions. WQI results (weighted and non-weighted) developed using different parameter optimisation methods, namely Multivariate Linear Regression and Principal Component Analysis were compared. To build upon the current framework, a new procedure was developed to assess the adequacy of WQI based on the sensitivity analysis of parameters and uncertainties associated with each parameter's missing values distribution. The number of observations needed for the development of a robust WQI was optimised with respect to user-defined acceptable change in WQI, based on Monte Carlo probabilistic simulation. The Johor River Basin (JRB), Malaysia is used as a case-study for the application of this new framework. The JRB serves as an important resource for Johor, one of the most populous state in Malaysia, and Singapore, a country south of Johor. WQIMLR performed better in explaining the general water quality than WQIPCA for weighted water quality parameters. Optimisation of sampling frequency revealed that around 130 samples will be required if a 2% change in WQI can be tolerated. The results (specific to the JRB) also revealed that total coliform is the most sensitivity parameter to missing values, and the distribution of sensitive parameters are similar for both WQINON-ADJUSTED and WQIADJUSTED.

Assessment of water quality conditions in the Upper Johor River Basin

The water quality data from 2 sampling stations in the Upper Johor River Basin (Upper Johor River and Sayong River) were analysed to evaluate the water quality conditions. The variation in water quality parameters between base and storm flow events were compared to identify the pollutant sources. The results indicated that Biochemical Oxygen Demand (BOD) was the main cause for non-point source pollution of the study area. The overall water quality conditions were good in terms of dissolved oxygen and total nutrients (Nitrogen and Phosphorus). The dominant sources of BOD could be from the fertiliser timing and application. Different from BOD, the majority of the water quality samples were from baseflow events for dissolved oxygen, total nitrogen and total phosphorus. The major differences between four water quality parameters are failing septic system, oil palm mills, wastewater treatment plants and domestic waste. The results from this research would provide useful information for total maximum daily load calculation, establishment of watershed and nutrient model, and development of Best Management Practices for the Upper Johor River Basin and comparable study area.

Trace element concentrations in fine sediment and linkages to non-point pollution source: Lower Johor river basin

Johor Strait is an economically important freshwater system in the southern portion of Peninsular Malaysia. In past decades, Johor has been experiencing rapid developments especially in industrialisation, urbanisation and agricultural activities which have impacted the quality of Johor river. This study focused on identifying the intensity and degree of sediment contamination by trace elements from different anthropogenic sources using the multiple Risk Indexes. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) was used to detect trace element concentrations from nine sampling stations. The overall ranges for metals are 0.35-4.25, 505.86-1864.56, below detection limit (BDL)-5.37, 0.02-0.07, 0.02-0.17, 0.59-2.05, BDL-5.35, 247.07-1010.23, 0.71-9.62, 1.08-5.68 and 10.87-21.15 mg/kg for Ag, Al, As, Cd, Co, Cr, Cu, Fe, Mn, Ni, and Zn respectively. The mean concentrations of trace elements follow the order: Al > Fe > Zn > Cu > Mn > Ni > Ag > Cr > As > Co > Cd. In this study, high concentrations of most elements (Al, As, Cd, Co, Fe, Mn, and Ni) were recorded at SS5 as the station is located near the Kota Tinggi city. Comparison with the sediment quality guidelines (SQGs) portrayed that concentrations of As, Cd, Cr, Cu, Ni, and Zn were below the Threshold Effects Level (TEL), Severity Effects Level (SEL), Probable Effects level (PEL) values in all sampling stations. The Pollution Load Index (PLI) that ranged between 0.151 and 0.389 (PL1 < 1) indicates that the Johor river sediments are free of trace element contamination. Potential Ecological Risk Index (RI), and Potential Ecological Risk Factor (Er) were in the range of 3.018-11.823 (RI < 150) and 0.103-7.141 (Er <40) respectively, which indicate that trace elements in Johor river pose no adverse effects on aquatic biota. The Pearson’s correlation matrix showed a good positive correlation between Al and As (0.546), Co (0.595), Fe (0.440), Mn (0.770), and Ni (0.496), representing similar sources of pollution. The cluster analysis indicates that Al, Mn, As, Ni, Fe, Cd, and Co originated from natural processes while Cr, C, Ag, and Zn are mainly from anthropogenic sources. Suggesting that man-made activities are accelerating sedimentation rate and washing down the pollutants together to the adjacent water bodies. Tracing the origin of the elements and planning for target mitigation to reduce further deterioration to the receiving river system could be the next mode of action.

Machine learning methods for better water quality prediction

In any aquatic system analysis, the modelling water quality parameters are of considerable significance. The traditional modelling methodologies are dependent on datasets that involve large amount of unknown or unspecified input data and generally consist of time-consuming processes. The implementation of artificial intelligence (AI) leads to a flexible mathematical structure that has the capability to identify non-linear and complex relationships between input and output data. There has been a major degradation of the Johor River Basin because of several developmental and human activities. Therefore, setting up of a water quality prediction model for better water resource management is of critical importance and will serve as a powerful tool. The different modelling approaches that have been implemented include: Adaptive Neuro-Fuzzy Inference System (ANFIS), Radial Basis Function Neural Networks (RBF-ANN), and Multi-Layer Perceptron Neural Networks (MLP-ANN). However, data obtained from monitoring stations and experiments are possibly polluted by noise signals as a result of random and systematic errors. Due to the presence of noise in the data, it is relatively difficult to make an accurate prediction. Hence, a Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter. In the domain of interests, the water quality parameters primarily include ammoniacal nitrogen (AN), suspended solid (SS) and pH. In order to evaluate the impacts on the model, three evaluation techniques or assessment processes have been used. The first assessment process is dependent on the partitioning of the neural network connection weights that ascertains the significance of every input parameter in the network. On the other hand, the second and third assessment processes ascertain the most effectual input that has the potential to construct the models using a single and a combination of parameters, respectively. During these processes, two scenarios were introduced: Scenario 1 and Scenario 2. Scenario 1 constructs a prediction model for water quality parameters at every station, while Scenario 2 develops a prediction model on the basis of the value of the same parameter at the previous station (upstream). Both the scenarios are based on the value of the twelve input parameters. The field data from 2009 to 2010 was used to validate WDT-ANFIS. The WDT-ANFIS model exhibited a significant improvement in predicting accuracy for all the water quality parameters and outperformed all the recommended models. Also, the performance of Scenario 2 was observed to be more adequate than Scenario 1, with substantial improvement in the range of 0.5% to 5% for all the water quality parameters at all stations. On validating the recommended model, it was found that the model satisfactorily predicted all the water quality parameters (R2 values equal or bigger than 0.9).

Future hydro-meteorological drought of the Johor River Basin, Malaysia, based on CORDEX-SEA projections

ABSTRACTWater scarcity issues in the Johor River Basin (JRB) could affect the populations of Malaysia and Singapore. This study provides an overview of future hydro-meteorological droughts using climate projections from an ensemble of four Coordinated Regional Climate Downscaling Experiments – Southeast Asia (CORDEX-SEA) domain outputs under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios for the 2021–2050 and 2071–2100 periods. The climate projections were bias corrected using the quantile mapping approach before being incorporated into the Soil and Water Assessment Tool (SWAT) hydrological model. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to examine the meteorological and hydrological droughts, respectively. Overall, future annual precipitation, streamflow, and maximum and minimum temperatures are projected to change by about −44.2 to 24.3%, −88.7 to 42.2%, 0.8 to 3.7ºC and 0.7 to 4.7ºC, respectively. The results show that the JRB is likely to receive more frequent meteorological droughts in the future.

Spatiotemporal analysis of hydro-meteorological drought in the Johor River Basin, Malaysia

Assessment of historical hydro-meteorological drought is important to develop a robust drought monitoring and prediction system. This study aims to assess the historical hydro-meteorological drought of the Johor River Basin (JRB) from 1975 to 2010, an important basin for the population of southern Peninsular Malaysia and Singapore. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were selected to represent the meteorological and hydrological droughts, respectively. Four absolute homogeneity tests were used to assess the rainfall data from 20 stations, and two stations were flagged by these tests. Results indicate the SPI duration to be comparatively low (3 months), and drier conditions occur over the upper JRB. The annual SSI had a strong decreasing trend at 95% significance level, showing that human activities such as reservoir construction and agriculture (oil palm) have a major influence on streamflow in the middle and lower basin. In addition, moderate response rate of SSI to SPI was found, indicating that hydrological drought could also have occurred in normal climate condition. Generally, the El Nino-Southern Oscillation and Madden Julian Oscillation have greater impacts on drought events in the basin. Findings of this study could be beneficial for future drought projection and water resources management.

River water quality assessment using APCS-MLR and statistical process control in Johor River Basin, Malaysia

River water quality assessment using APCS-MLR and statistical process control in Johor River Basin, Malaysia

Soil erosion susceptibility of Johor River basin

AbstractMalaysia is undergoing tremendous environmental changes due to population growth, economic development and urbanization. This has stimulated a rapid land use changes which in turn has increased the rate of soil erosion and river sedimentation. In this study, Universal Soil Loss Equation (USLE) and the Modified Universal Soil Loss Equation (MUSLE) are applied within a geographical information system (GIS) to assess the spatial pattern of soil erosion susceptibility of Johor River basin, Malaysia. Geomorphological, landuse and soil information along with other hydrological data such as, annual peak flow and runoff volumes are used to estimate the parameters required for the calculation of soil erosion susceptibility. The results show that annual erosion rate varies between 0.2 and 248.2 t/ha/year according to USLE, and between 19.2 and 2179.9 thousand tons per event according to MUSLE. The land cover management is found as the major factor responsible for soil erosion in the basin.

Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins

Gridded climate products (GCPs) provide a potential source for representing weather in remote, poor quality or short-term observation regions. The accuracy of three long-term GCPs (Asian Precipitation—Highly-Resolved Observational Data Integration towards Evaluation of Water Resources: APHRODITE, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record: PERSIANN-CDR and National Centers for Environmental Prediction Climate Forecast System Reanalysis: NCEP-CFSR) was analyzed for the Kelantan River Basin (KRB) and Johor River Basin (JRB) in Malaysia from 1983 to 2007. Then, these GCPs were used as inputs into calibrated Soil and Water Assessment Tool (SWAT) models, to assess their capability in simulating streamflow. The results show that the APHRODITE data performed the best in precipitation estimation, followed by the PERSIANN-CDR and NCEP-CFSR datasets. The NCEP-CFSR daily maximum temperature data exhibited a better correlation than the minimum temperature data. For streamflow simulations, the APHRODITE data resulted in strong results for both basins, while the NCEP-CFSR data showed unsatisfactory performance. In contrast, the PERSIANN-CDR data showed acceptable representation of observed streamflow in the KRB, but failed to track the JRB observed streamflow. The combination of the APHRODITE precipitation and NCEP-CFSR temperature data resulted in accurate streamflow simulations. The APHRODITE and PERSIANN-CDR data often underestimated the extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations. Therefore, a direct application of NCEP-CFSR data should be avoided in this region. We recommend the use of APHRODITE precipitation and NCEP-CFSR temperature data in modeling of Malaysian water resources.

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

The sensitivity of streamflow simulated with the Soil and Water Assessment Tool (SWAT) model to Digital Elevation Model (DEM) resolution, DEM source and DEM resampling technique is still poorly understood. The objective of this study is to compare SWAT model streamflow estimates in the Johor River Basin (JRB), Malaysia for DEMs differing in resolution (from 20 to 1500 m), sources (Shuttle Radar Topography Mission: SRTM v4.1, Advanced Space-borne Thermal Emission and Reflection Radiometer: ASTER GDEM2, EarthEnv-DEM90 and Global Multi-resolution Terrain Elevation Data 2010: GMTED2010) and resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority). The key findings were as follows: (1) SRTM v4.1 (Root Mean Square Error (RMSE) = 11.16 m) and EarthEnv-DEM90 (RMSE = 12.4 m) had better vertical accuracy over the JRB compared to the ASTER GDEM2 (RMSE = 16.95 m); (2) Accurate annual streamflow simulations were obtained by using nearly all of the DEM resolutions, as pointed out by a relative error (RE) lower than 7% from 20 to 50 m and from 100 to 800 m DEMs; (3) Prediction errors were the lowest for ASTER GDEM2 (RE = 3.9%), followed by SRTM v4.1 (RE = 5.4%), EarthEnv-DEM90 (RE = 6.3%), and GMTED2010 (RE = 7.3%); (4) the majority and nearest neighbour resampling techniques performed the best (RE of 6.0%), followed by bilinear interpolation (RE of 7.2%) and cubic convolution (7.5%). The study indicates that DEM resolution is the most sensitive SWAT model DEM parameter compared to DEM source and DEM resampling technique for streamflow simulation within SWAT.

COMPARISON OF FLOOD DISTRIBUTION MODELS FOR JOHOR RIVER BASIN

One of the most useful and commonly used parameters to describe a flood event is peak flow or annual maximum flood. In many localities, storm water control facilities are required and their sizes are determined based on certain peak flow magnitude. This study aimed at estimating the average recurrent interval (ARI) of flood event for Johor River basin based on the distributions of annual peak flow. The analysis used annual maximum flow data from July 1965 to June 2010 recorded at the Rantau Panjang gauging station. Five distribution models, namely Generalized Extreme Value (GEV), Lognormal, Pearson 5, Weibull and Gamma were tested. The goodness fit test (GOF) of Kolmogorov-Smirnov (K-S) was used to evaluate and estimate the best-fitted distribution. The results reaffirm the current practice that GEV is still the best-fitted distribution model for fitting the annual peak flow data. On the other hand, gamma distribution showed the poorest result.

Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin

Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of &lt;0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.

Impacts of land-use and climate variability on hydrological components in the Johor River basin, Malaysia

This study aims to investigate separate and combined impacts of land-use and climate variability on hydrological components in the Johor River Basin (JRB), Malaysia. The Mann-Kendall and Sen’s slope tests were applied to detect the trends in precipitation, temperature and streamflow of the JRB. The Soil and Water Assessment Tool (SWAT) was calibrated and validated using measured monthly streamflow data. The validation results showed that SWAT was reliable in the tropical JRB. The trend analysis showed that there was an insignificant increasing trend for streamflow, whereas significant increasing trends for precipitation and temperature were found. The combined (climate + land-use change) impact caused the annual streamflow and evaporation to increase by 4.4% and 1.2%, respectively. Climate (land-use) raised annual streamflow by 4.4% (0.06%) and evaporation by 2.2% (−0.2%). Climate change imposed a stronger impact than land-use change on the streamflow and evaporation. These findings are useful for decision makers to develop better water and land-use policies.Editor Z. W. Kundzewicz; Guest editor V. Krysanova

Impacts and uncertainties of climate change on streamflow of the Johor River Basin, Malaysia using a CMIP5 General Circulation Model ensemble

The impact of climate change and uncertainty of climate projections from general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) on streamflow in the Johor River Basin, Malaysia was assessed. Eighteen GCMs were evaluated, and the six that adequately simulated historical climate were selected for an ensemble of GCMs under three Representative Concentration Pathways (RCPs; 2.6 (low emissions), 4.5 (moderate emissions) and 8.5 (high emissions)) for three future time periods (2020s, 2050s and 2080s) as inputs into the Soil and Water Assessment Tool (SWAT) hydrological model. We also quantified the uncertainties associated with GCM structure, greenhouse gas concentration pathways (RCP 2.6, 4.5 and 8.5), and prescribed increases of global temperature (1–6 °C) through streamflow changes. The SWAT model simulated historical monthly streamflow well, with a Nash–Sutcliffe efficiency coefficient of 0.66 for calibration and 0.62 for validation. Under RCPs 2.6, 4.5, and 8.5, the results indicate that annual precipitation changes of 1.01 to 8.88% and annual temperature of 0.60–3.21 °C will lead to a projected annual streamflow ranging from 0.91 to 12.95% compared to the historical period. The study indicates multiple climate change scenarios are important for a robust hydrological impact assessment.

Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia

Streamflow modelling is a mathematical computational approach that represents terrestrial hydrology cycle digitally and is used for water resources assessment. However, such modelling endeavours require a large amount of data. Generally, governmental departments produce and maintain these data sets which make it difficult to obtain this data due to bureaucratic constraints. In some countries, the availability and quality of geospatial and climate datasets remain a critical issue due to many factors such as lacking of ground station, expertise, technology, financial support and war time. To overcome this problem, this research used public domain datasets from the Internet as "input" to a streamflow model. The intention is simulate daily and monthly streamflow of the Johor River Basin in Malaysia. The model used is the Soil and Water Assessment Tool (SWAT). As input free data including a digital elevation model (DEM), land use information, soil and climate data were used. The model was validated by in-situ streamflow information obtained from Rantau Panjang station for the year 2006. The coefficient of determination and Nash-Sutcliffe efficiency were 0.35/0.02 for daily simulated streamflow and 0.92/0.21 for monthly simulated streamflow, respectively. The results show that free data can provide a better simulation at a monthly scale compared to a daily basis in a tropical region. A sensitivity analysis and calibration procedure should be conducted in order to maximize the "goodness-of-fit" between simulated and observed streamflow. The application of Internet datasets promises an acceptable performance of streamflow modelling. This research demonstrates that public domain data is suitable for streamflow modelling in a tropical river basin within acceptable accuracy.

A GIS-ANN-Based Approach for Enhancing the Effect of Slope in the Modified Green-Ampt Model

Most infiltration models survey infiltration in large scale regions using an assumption that the slope of the ground is equal to zero. The Modified Green and Ampt model is one of a few infiltration models that considers slope as an input parameter in its formulation. Here, using artificial neural networks in a raster-based design, basic research is presented regarding the effect of surface slope on infiltration. For the investigation, three catchments with different areas and slopes were selected as case studies, based on existing runoff stations in the upstream region of the Johor River Basin in southern Malaysia. In this research, the efficiency of six different functions was studied in order to determine the best performer for slope in the Modified Green and Ampt model. We also sought to find the most suitable ANN transfer function for infiltration calculations. By calculating runoff for each pixel, accumulation maps were used for corroborating the suitability of the obtained results. The results indicated that the Log-sigmoid was the most appropriate transfer function. We also determined that using the exponential form for the slope in the Modified Green and Ampt model formulation was more accurate, as compared to the original linear shape.

Application of artificial neural networks for water quality prediction

The term “water quality” is used to describe the condition of water, including its chemical, physical, and biological characteristics. Modeling water quality parameters is a very important aspect in the analysis of any aquatic systems. Prediction of surface water quality is required for proper management of the river basin so that adequate measure can be taken to keep pollution within permissible limits. Accurate prediction of future phenomena is the life blood of optimal water resources management. The artificial neural network is a new technique with a flexible mathematical structure that is capable of identifying complex non-linear relationships between input and output data when compared to other classical modeling techniques. Johor River Basin located in Johor state, Malaysia, which is significantly degrading due to human activities and development along the river. Accordingly, it is very important to implement and adopt a water quality prediction model that can provide a powerful tool to implement better water resource management. Several modeling methods have been applied in this research including: linear regression models (LRM), multilayer perceptron neural networks and radial basis function neural networks (RBF-NN). The results showed that the use of neural networks and more specifically RBF-NN models can describe the behavior of water quality parameters more accurately than linear regression models. In addition, we observed that the RBF finds a solution faster than the MLP and is the most accurate and most reliable tool in terms of processing large amounts of non-linear, non-parametric data.

A GIS-based survey for the optimization of infiltration forecasting models with emphasis on slope effect and land use

Morphology and specifically slope have an important effect on water losses due to infiltration and obtained runoff from rainfall. Approximately all of infiltration models survey infiltration in the large scale region (like a basin) and through a vertical column. In other words, they work with this assumption that slope is equal to zero. There are few infiltration models such as modified green and Ampt model which use the slope as an input factor. But, since this parameter is the average slope of whole catchment, it cannot be really accurate. This paper is a raster-based investigation of slope effect on infiltration by considering land use which is one of the most important parameters in this case. For this purpose, geographic information system (GIS) has been used to divide Johor river basin in the south of Malaysia to 1436200 separated pixels (10 × 10 m) and study on each pixel as an independent catchment. To corroborate the suitability of the obtained results, eighteen different type of slope has been studied. The correlation coefficient indicates that thesquare cosine of slope is the most suitable function of slope which can be used for infiltration calculations.

Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations

Abstract. This study examined the potential of Multi-layer Perceptron Neural Network (MLP-NN) in predicting dissolved oxygen (DO) at Johor River Basin. The river water quality parameters were monitored regularly each month at four different stations by the Department of Environment (DOE) over a period of ten years, i.e. from 1998 to 2007. The following five water quality parameters were selected for the proposed MLP-NN modelling, namely; temperature (Temp), water pH, electrical conductivity (COND), nitrate (NO3) and ammonical nitrogen (NH3-NL). In this study, two scenarios were introduced; the first scenario (Scenario 1) was to establish the prediction model for DO at each station based on five input parameters, while the second scenario (Scenario 2) was to establish the prediction model for DO based on the five input parameters and DO predicted at previous station (upstream). The model needs to verify when output results and the observed values are close enough to satisfy the verification criteria. Therefore, in order to investigate the efficiency of the proposed model, the verification of MLP-NN based on collection of field data within duration 2009–2010 is presented. To evaluate the effect of input parameters on the model, the sensitivity analysis was adopted. It was found that the most effective inputs were oxygen-containing (NO3) and oxygen demand (NH3-NL). On the other hand, Temp and pH were found to be the least effective parameters, whereas COND contributed the lowest to the proposed model. In addition, 17 neurons were selected as the best number of neurons in the hidden layer for the MLP-NN architecture. To evaluate the performance of the proposed model, three statistical indexes were used, namely; Coefficient of Efficiency (CE), Mean Square Error (MSE) and Coefficient of Correlation (CC). A relatively low correlation between the observed and predicted values in the testing data set was obtained in Scenario 1. In contrast, high coefficients of correlation were obtained between the observed and predicted values for the test sets of 0.98, 0.96 and 0.97 for all stations after adopting Scenario 2. It appeared that the results for Scenario 2 were more adequate than Scenario 1, with a significant improvement for all stations ranging from 4 % to 8 %.

Les stèles funéraires musulmanes dites batu Aceh de l'État de Johor (Malaisie)

Daniel Perret Muslim graves with batu Aceh tombstones from Johor (Malaysia) 211 Muslim graves with batu Aceh tombstones which can be safely dated between the mid-15th c. and the beginning of the 18th с have been described during surveys conducted between 1996 and 1999 in the State of Johor (Malaysia) by the École française d'Extrême-Orient in co-operation with the Yayasan Warisan Johor. These graves are located on 36 sites in three areas: Johor river basin, Simpang Kiri river and Muar river basin. Detailed descriptions have enabled us to define 16 types of batu Aceh. Except a very few of them, the epitaphs bear no name and no date. Thus hypotheses regarding the precise dating and the identification of the graves can be based only on written sources, oral traditions and comparative analyses. Such hypotheses are suggested here for graves located in large graveyards in the vicinity of Kota Tinggi which make up more than half of all the graves with batu Aceh tombstones in Johor. The results show that questions such as chronology and diffusion of types as well as choice of type in connection with the status of the deceased could be precisely answered only with the help of more detailed descriptions of batu Aceh outside Johor that would enable a more comprehensive comparative analysis.