He has designed, developed, and analyzed several methods for decision making support systems using artificial intelligence, machine learning, geospatial information systems, and statistical techniques for more than sixteen years. Expert in time series, spatial, network and 3D analysis. Has processed recent remotely sensed datasets i.e. LiDAR, Radar, and very high-resolution satellite imagery. He has been delivering machine learning, GIS and image processing workshops. He has several published ISI papers, book chapters, and conference papers in the aforementioned topics. He has delivered several academic courses and educational workshops at universities. He collaborates with many high impacts and Scopus indexed journals such as Remote Sensing, Environmental Monitoring, Applied computing, Mathematical Geosciences, Engineering with Computers and Assessment, and Arabian journal of geosciences. He has won a couple of scholarship rounds during his studies and awarded as the best paper in IEEE conferences on geoscience and remote sensing.
- Information Systems
- Artificial intelligence
- Natural Resources and geo-hazards
- Spatial Modelling
- Image processing
- Machine learning
- Disaster forecasting
- Time series analysis
- Remote sensing
Teacher assistance in Risk Management in Engineering (49006) at UTS (2018-2019).
Tutor in Information System Development Methodologies (31257) at UTS (2019).
Lecturer assistance in Global Positioning System (GPS), Computer Aided Design (CAD), Engineering Mathematics I, and Disaster Forecasting courses (Bachelor and Master level) at faculty of Engineering of UPM (2014-2017).
Lecturer in Environmental Geospatial Modelling course at the faculty of applied science of UiTM University (2016).
Lecturer in Environmental Geospatial Modelling course at the faculty of the applied science of UiTM University.
Azeez, OS, Pradhan, B, Shafri, HZM, Shukla, N, Lee, C & Rizeei, HM 2019, 'Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks', Applied Sciences (Bucureşti), vol. 9, no. 2.View/Download from: UTS OPUS
Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model's predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.
Chen, W, Pradhan, B, Li, S, Shahabi, H, Rizeei, HM, Hou, E & Wang, S 2019, 'Novel Hybrid Integration Approach of Bagging-Based Fisher's Linear Discriminant Function for Groundwater Potential Analysis', NATURAL RESOURCES RESEARCH, vol. 28, no. 4, pp. 1239-1258.View/Download from: UTS OPUS or Publisher's site
Mojaddadi Rizeei, H, Pradhan, B & Saharkhiz, MA 2019, 'Urban object extraction using Dempster Shafer feature-based image analysis from worldview-3 satellite imagery', International Journal of Remote Sensing, vol. 40, no. 3, pp. 1092-1119.View/Download from: UTS OPUS or Publisher's site
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. A detailed and up-to-date land use of the urban environment is essentially required in many applications. Very high-resolution (VHR), Multispectral Scanner System (MSS) Worldview-3 (WV-3) satellite imagery provides detailed information on urban characteristics, which should be professionally mined. In this research, WV-3 was processed by machine learning (ML) methods to extract the most accurate urban features. Fuze-Go panchromatic sharpening in conjunction with atmospheric and topographic correction was initially utilized to increase the image quality and colour contrast. Three image analysis approaches including, current pixel-based image analysis (PBIA), object-based image analysis (OBIA) and new feature-based image analysis (FBIA) were implemented on WV-3 image. The k-nearest neighbour (k-NN), Naive Bayes (NB), support vector machine (SVM) classifiers were represented by PBIA, the Decision Tree (DT) classifier was examined as OBIA and the Dempster–Shafer (DS) fusion classifier was manifested for the first time as FBIA. In order to engage DS as FBIA, four types of Belief Masses, namely, Precision, Recall, Overall Accuracy, and kappa coefficient (ĸ) were implemented and compared to assign the most likelihood urban features. All the applied classifiers were also trained on the first site and then tested on another site to examine the transferability. The accuracy, reliability, and computational time of all classifiers were examined by confusion matrix and McNemar assessment. Results show improvements on the detailed urban extraction obtained using the proposed FBIA with 92.2% overall accuracy in compared with PBIA and OBIA. The FBIA result of urban extraction is more consistent when transferred to another study area and consumes much lesser time than OBIA. Also, the precision mass belief measurement achieved highest efficiency regarding receiver operating characteristic (ROC) curve rate.
Naderpour, M, Rizeei, HM, Khakzad, N & Pradhan, B 2019, 'Forest fire induced Natech risk assessment: A survey of geospatial technologies', Reliability Engineering and System Safety, vol. 191.View/Download from: UTS OPUS or Publisher's site
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'Allocation of emergency response centres in response to pluvial flooding-prone demand points using integrated multiple layer perceptron and maximum coverage location problem models', International Journal of Disaster Risk Reduction, vol. 38.View/Download from: UTS OPUS or Publisher's site
© 2019 The increases in the frequency and intensity of rainfall events due to global climate change and the development of additional pavement, roads and water storage sites due to population growth have enhanced the probability of pluvial flooding (PF) in urban areas. The estimation of urban pluvial flood vulnerability and prompt emergency responses are crucial steps towards urban planning and risk mitigation. However, uncertainties exist in the optimal allocation of emergency response centres (ERCs). This study assessed the current situation of ERCs in terms of PF-prone demand points. In this study, fire and police stations, hospitals and military camps were defined as ERCs, and residential buildings, where people spend most of their time, were considered demand points. Our study area was Damansara City in Peninsular Malaysia, which is frequently affected by PF. We combined an optimised PF probability model with ideal location allocation methods on a geographic information system platform to construct the proposed model for achieving accurate ERC spatial planning. Firstly, PF-prone urban areas were identified using a recent machine learning multiple layer perceptron (MLP) model. Then, a Taguchi method was used to calibrate the MLP variables, namely, seed, momentum, learning rate, hidden layer attribute and class. Fourteen important PF contributing parameters were weighted on the basis of historical flood events. The predicted PF-prone areas were validated by comparing the predictions with the data from meteorological stations and observed inventory events. In addition, the current locations of ERCs were utilised in the location allocation model to assess the ideal time for providing essential services to elements at risk. Minimum impedance and maximum coverage location problem models were implemented to assess the current allocated location of ERCs and multiple scenarios. The coverage of existing ERCs was calculated, and their suitable and optimal locations wer...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS', Complex and Intelligent Systems.View/Download from: UTS OPUS or Publisher's site
Abdullahi, S, Pradhan, B & Mojaddadi, H 2018, 'City Compactness: Assessing the Influence of the Growth of Residential Land Use', Journal of Urban Technology, vol. 25, no. 1, pp. 21-46.View/Download from: UTS OPUS or Publisher's site
© 2017 The Society of Urban Technology. In the urban sprawl paradigm, residential land use exhibits a more significant growth than other categories. Consequently, large proportions of the natural environment are converted to residential areas, particularly in tropical countries. Compact urban development is one of the most sustainable urban forms with environmental perspectives, such as rural development containment and natural environment preservation. However, no proper investigation of the relationship and influence of residential growth and city compactness is available. This study evaluated and forecasted the residential development of Kajang City in Malaysia based on compact development. First, the relationship between residential land use change and city compactness was evaluated. Second, residential growth was projected by utilizing the land transformation model (LTM) and the statistical-based weight of evidence (WoE) using various spatial parameters. Both models were evaluated with respect to observed land use and compactness maps. Results indicated that most of the newly developed residential areas were in zones where the degrees of compactness increase during certain periods. In addition, LTM performed better and provided a more accurate modeling of residential growth than the WoE. However, WoE provided clearer and more informative results than LTM in terms of functional relationships between dependent and independent variables related to city compactness.
Mezaal, MR, Pradhan, B & Rizeei, HM 2018, 'Improving landslide detection from airborne laser scanning data using optimized Dempster-Shafer', Remote Sensing, vol. 10, no. 7.View/Download from: UTS OPUS or Publisher's site
© 2018 by the authors. A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these techniques are time consuming and costly. Furthermore, complications arise for the generation of accurate landslide location maps in these regions due to dense vegetation in tropical forests. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) is typically employed to generate accurate landslide maps. The object-based technique generally consists of many homogeneous pixels grouped together in a meaningful way through image segmentation. In this paper, in order to address the limitations of this approach, the final decision is executed using Dempster-Shafer theory (DST) rule combination based on probabilistic output from object-based support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classifiers. Therefore, this research proposes an efficient framework by combining three object-based classifiers using the DST method. Consequently, an existing supervised approach (i.e., fuzzy-based segmentation parameter optimizer) was adopted to optimize multiresolution segmentation parameters such as scale, shape, and compactness. Subsequently, a correlation-based feature selection (CFS) algorithm was employed to select the relevant features. Two study sites were selected to implement the method of landslide detection and evaluation of the proposed method (subset "A" for implementation and subset "B" for the transferrable). The DST method performed well in detecting landslide locations in tropical regions such as Malaysia, with potential applications in other similarly vegetated regions.
Nampak, H, Pradhan, B, Mojaddadi Rizeei, H & Park, HJ 2018, 'Assessment of land cover and land use change impact on soil loss in a tropical catchment by using multitemporal SPOT-5 satellite images and Revised Universal Soil Loss Equation model', Land Degradation and Development, vol. 29, no. 10, pp. 3440-3455.View/Download from: UTS OPUS or Publisher's site
© 2018 John Wiley & Sons, Ltd. Soil erosion is a common land degradation problem and has disastrous impacts on natural ecosystems and human life. Therefore, researchers have focused on detection of land cover–land use changes (LCLUC) with respect to monitoring and mitigating the potential soil erosion. This article aims to appraise the relationship between LCLUC and soil erosion in the Cameron Highlands (Malaysia) by using multitemporal satellite images and ancillary data. Land clearing and heavy rainfall events in the study area has resulted in increased soil loss. Moreover, unsustainable development and agricultural practices, mismanagement, and lack of land use policies increase the soil erosion rate. Hence, the main contribution of this study lies in the application of appropriate land management practices in relation to water erosion through identification and prediction of the impacts of LCLUC on the spatial distribution of potential soil loss in a region susceptible to natural hazards such as landslide. The LCLUC distribution within the study area was mapped for 2005, 2010, and 2015 by using SPOT-5 temporal satellite imagery and object-based image classification. A projected land cover–land use map was also produced for 2025 through integration of Markov chain and cellular automata models. An empirical-based approach (Revised Universal Soil Loss Equation) coupled with geographic information system was applied to measure soil loss and susceptibility to erosion over the study area for four periods (2005, 2010, 2015, and 2025). The model comprises five parameters, namely, rainfall factor, soil erodibility, topographical factor, conservation factor, and support practice factor. Results exhibited that the average amount of soil loss increased by 31.77 t ha−1 yr−1 from 2005 to 2015 and was predicted to dramatically increase in 2025. The results generated from this research recommends that awareness of spatial and temporal patterns of high soil loss risk areas ...
Pradhan, B, Rizeei, HM & Abdulle, A 2018, 'Quantitative assessment for detection and monitoring of coastline dynamics with temporal RADARSAT images', Remote Sensing, vol. 10, no. 11.View/Download from: UTS OPUS or Publisher's site
© 2018 by the authors. This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.
Rizeei, HM, Azeez, OS, Pradhan, B & Khamees, HH 2018, 'Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models.', Environmental monitoring and assessment, vol. 190, no. 11.View/Download from: UTS OPUS or Publisher's site
Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2018, 'Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region', Arabian Journal of Geosciences, vol. 11, no. 3.View/Download from: UTS OPUS or Publisher's site
© 2018, Saudi Society for Geosciences. The effects of climate and land use/land cover (LULC) dynamics have directly affected the surface runoff and flooding events. Hence, current study proposes a full-packaged model to monitor the changes in surface runoff in addition to forecast of the future surface runoff based on LULC and precipitation variations. On one hand, six different LULC classes were extracted from Spot-5 satellite image. Conjointly, land transformation model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020 ones. On the other hand, the time series-autoregressive integrated moving average (ARIMA) model was applied to forecast the amount of rainfall in 2020. The ARIMA parameters were calibrated and fitted by latest Taguchi method. To simulate the maximum probable surface runoff, distributed soil conservation service-curve number (SCS-CN) model was applied. The comparison results showed that firstly, deforestation and urbanization have been occurred upon the given time, and they are anticipated to increase as well. Secondly, the amount of rainfall has non-stationary declined since 2000 till 2015 and this trend is estimated to continue by 2020. Thirdly, due to damaging changes in LULC, the surface runoff has been also increased till 2010 and it is forecasted to gradually exceed by 2020. Generally, model calibrations and accuracy assessments have been indicated, using distributed-GIS-based SCS-CN model in combination with the LTM and ARIMA models are an efficient and reliable approach for detecting, monitoring, and forecasting surface runoff.
Rizeei, HM, Shafri, HZM, Mohamoud, MA, Pradhan, B & Kalantar, B 2018, 'Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis', Journal of Sensors, vol. 2018.View/Download from: UTS OPUS or Publisher's site
Copyright © 2018 Hossein Mojaddadi Rizeei et al. The current study proposes a new method for oil palm age estimation and counting from Worldview-3 satellite image and light detection and range (LiDAR) airborne imagery. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. The sensitivity analysis was conducted on four SVM kernel types with associated segmentation parameters to obtain the optimal crown coverage delineation. Extracting tree's crown was integrated with height model and multiregression methods to accurately estimate the age of trees. The multiregression model with multikernel sizes was examined to achieve the most optimized model for age estimation. Applied models were trained and examined over five different oil palm plantations. The results of oil palm counting had an overall accuracy of 98.80%, while the overall accuracy of age estimation showed 84.91%, over all blocks. The relationship between tree's height and age was significant which supports the polynomial regression function (PRF) model with a 3 × 3 kernel size for under 10-12-year-old oil palm trees, while exponential regression function (ERF) is more fitted for older trees (i.e., 22 years old). Overall, recent remote sensing dataset and machine learning techniques are useful in monitoring and detecting oil palm plantation to maximize productivity.
Mojaddadi, H, Pradhan, B, Nampak, H, Ahmad, N & Ghazali, AHB 2017, 'Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS', Geomatics, Natural Hazards and Risk, vol. 8, pp. 1080-1102.View/Download from: UTS OPUS or Publisher's site
In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.
Rizeei, HM, Saharkhiz, MA, Pradhan, B & Ahmad, N 2016, 'Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models', Geocarto International, vol. 31, no. 10, pp. 1158-1177.
Mojaddadi, H, Habibnejad, M, Solaimani, K, Ahmadi, MZ & Hadian-Amri, MA 2009, 'An investigation of efficiency of outlet runoff assessment models: Navroud watershed, Iran', Journal of Applied Sciences, vol. 9, no. 1, pp. 105-112.View/Download from: Publisher's site
This research has been carried out for investigation and comparison of the amount of precision and correctness of SCS unit hydrograph, GRAY, G.I.U.H and Gc.I.U.H models in determination of the shape and dimensions of outlet runoff hydrograph in Navroud watershed with 266 km2 area, located in Giulan Province of Iran and use of these models for the similar watersheds and without any data. To investigate the amount of efficiency of above-mentioned methods, first 6 equivalent rainfall-runoff events were selected and for each, hydrograph of outlet runoff were calculated. Then the models were compared with together, for peak time, base time, peak flow and volume of outlet runoff and the most efficient model in estimation of hydrograph of outlet flow for similar regions was proposed. Comparison of calculated hydrographs obtained from models under research and observed hydrographs of selected events showed that SCS unit hydrograph method had the most direct agreement in three parameters of peak time, base time and volume of direct runoff. On the other hand, the geomorphoclimatic instantaneous unit hydrograph, with the highest mean relative error of 16%, had highest harmony in estimation of peak flow direct runoff. © 2009 Asian Network for Scientific Information.
Mezaal, MR, Pradhan, B, Shafri, HZM, Mojaddadi, H & Yusoff, ZM 2019, 'Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution lidar data' in Lecture Notes in Civil Engineering, pp. 825-848.View/Download from: UTS OPUS or Publisher's site
© Springer Nature Singapore Pte Ltd. 2019. Landslide is one of the most devastating natural disasters across the world with serious negative impact on its inhabitants and the environs. Landslide is considered as a type of soil erosion which could be shallow, deep-seated, cut slope, bare soil, and so on. Distinguishing between these types of soil erosions in dense vegetation terrain like Cameron Highlands Malaysia is still a challenging issue. Thus, it is difficult to differentiate between these erosion types using traditional techniques in locations with dense vegetation. Light detection and ranging (LiDAR) can detect variations in terrain and provide detailed topographic information on locations behind dense vegetation. This paper presents a hierarchical rule-based classification to obtain accurate map of landslide types. The performance of the hierarchical rule set classification using LiDAR data, orthophoto, texture, and geometric features for distinguishing between the classes would be evaluated. Fuzzy logic supervised approach (FbSP) was employed to optimize the segmentation parameters such as scale, shape, and compactness. Consequently, a correlation-based feature selection technique was used to select relevant features to develop the rule sets. In addition, in other to differentiate between deep-seated cover under shadow and normal shadow, the band ration was created by dividing the intensity over the green band. The overall accuracy and the kappa coefficient of the hierarchal rule set classification were found to be 90.41 and 0.86%, respectively, for site A. More so, the hierarchal rule sets were evaluated using another site named site B, and the overall accuracy and the kappa coefficient were found to be 87.33 and 0.81%, respectively. Based on these results, it is demonstrated that the proposed methodology is highly effective in improving the classification accuracy. The LiDAR DEM data, visible bands, texture, and geometric features considerably influenc...
Rizeei, HM, Pradhan, B & Saharkhiz, MA 2019, 'Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized arima and distributed-GIS-based SCS-CN models at tropical region' in Lecture Notes in Civil Engineering, pp. 1103-1126.View/Download from: UTS OPUS or Publisher's site
© Springer Nature Singapore Pte Ltd. 2019. The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed.
Aal-shamkhi, ADS, Mojaddadi, H, Pradhan, B & Abdullahi, S 2017, 'Extraction and Modeling of Urban Sprawl Development in Karbala City Using VHR Satellite Imagery' in Spatial Modeling and Assessment of Urban Form, Springer, Switzerland, pp. 281-296.View/Download from: UTS OPUS or Publisher's site
Adel Saharkhiz, M, Pradhan, B & Mojaddadi Rizeei, H 2018, 'Extraction of Forest Plantation Extents Using Majority Voting Classification Fusion Algorithm', Asian Conference on Remote Sensing, Kuala Lumpur, Malaysia.View/Download from: UTS OPUS
Mojaddadi Rizeei, H, Pradhan, B & Mahlia, TM 2018, 'GIS-Based Suitability Analysis on Hybrid Renewal Energy Site Allocation Using integrated MODIS and ASTER Satellite Imageries in Peninsular Malaysia', 39th ACRS 2018 PROCEEDING, Asian Conference on Remote Sensing, ACRS, Kuala Lumpur, pp. 358-368.View/Download from: UTS OPUS
This study attempts to find the most suitable place to establish a hybrid renewable energy site in Malaysia where richly endowed with resources such as a diverse form of biomass and solar energy. We used Satellite-derived solar irradiance estimation which is a useful and accurate approach for solar resource calculation. To do so, MODIS Terra and Aqua satellite were assessed to extract values of Aerosol Optical Depth (AOD) at 550 nm. Subsequently, other topographic contribution factors were derived from ASTER satellite imagery. MODIS satellite imagery was also classified by support vector machine to extract land use/land cover. Additionally, sixteen different metrological stations were utilized to calibrate the solar irradiances achieved from MODIS satellite and provide daily wind data over the entire Peninsular Malaysia. Finally, simple additive weighting method was implemented in a geographical information system platform to develop the hybrid RE suitability model. MODIS solar radiation result showed a high coloration with field observation. The result of hybrid renewable energy suitability analysis revealed that coastal areas of Hulu Terengganu, have a high potential for allocating sites. This country scale research can be used as a guidance/preliminary assessment to narrow down the scope of new potential hybrid RE in regional scale.
Rizeei, HM & Pradhan, B 2018, 'Extraction and accuracy assessment of DTMs derived from remotely sensed and field surveying approaches in GIS framework', IOP Conference Series: Earth and Environmental Science.View/Download from: UTS OPUS or Publisher's site
© Published under licence by IOP Publishing Ltd. Generating a high precision continuous surface is a key capability required in most geographic information system (GIS) applications. In fact the most commonly used surface type is a digital elevation model (DEM). Recently, there are some sources of remote sensing data that provide DEM information such as; LiDAR, InSAR and ASTER GDEM which ranged from very high to low spatial resolution. However, new methods of topographic field surveying still highly on demand e.g. Differential GPS and Total station devices. In both method of capturing the terrain elevation the post processing need to be applied to create a continuous surface from point clouds. Geostatistical analysis were used to interpolate the taken sample points from site into continuous surface. In current research, we examined the height accuracy of LiDAR point clouds and total station dataset with three non-adoptive interpolation models including, invers distance weightage (IDW), nearest neighbour (NN) and radial basis function (RBF) based on referenced DGPS points. RMSE and R square regression analysis were conducted to reveal the most accurate approaches in pilot study area. The results showed Lidar surveying (less than 0.5 meter RMSE) has higher height accuracy compared to Total station surveying (above 1 meter in RMSE) to extract DTM in flat area; while consumed less computational processing time. Moreover, IDW was the best and accurate interpolation model in both datasets to generate raster cautious terrain model.