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Dr. Shamsuddin Shahid
Universiti Teknologi Malaysia

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0 Climate Change
0 climate prediction
0 Statistical Hydrology
0 Hydrology and Water Resources Management
0 Meteorology Science

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Original paper
Published: 17 August 2021 in Theoretical and Applied Climatology
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An alteration of rainfall variability and changes in rainfall driven extremes have been noticed across the globe with rising earth temperature. Such changes will undoubtedly be more devastating for agriculture-based developing countries. This study evaluated possible changes in rainfall and droughts in Bangladesh, a high climate change susceptible country, due to 1.5 and 2 °C temperature rise scenarios. Projections of global climate models (GCMs) of the coupled model intercomparison project phase 6 (CMIP6) for two shared socioeconomic pathway (SSP) scenarios, SSP-119 and SSP-126, were used for this purpose. The results showed an increase in annual rainfall over Bangladesh for both scenarios. However, the changes in rainfall variability would cause a drastic change in the drought pattern. Overall, drought frequency may decrease in the drought-prone western region up to -50% and increase in the east up to 50 to 70%, making droughts more homogeneously distributed over the country. However, a higher increase in the east than a decrease in the west for SSP119 indicates a possible shift in the country’s drought-prone region. The drought scenarios for SSP119 and SSP126 revealed that a 0.5 °C further rise in temperature might cause an increase in extreme drought frequency by 30% in the central-eastern region. Bangladesh should take effective drought mitigation measures to sustain its agricultural development.

ACS Style

A. S. M. Maksud Kamal; Farhad Hossain; Shamsuddin Shahid. Spatiotemporal changes in rainfall and droughts of Bangladesh for1.5 and 2 °C temperature rise scenarios of CMIP6 models. Theoretical and Applied Climatology 2021, 1 -16.

AMA Style

A. S. M. Maksud Kamal, Farhad Hossain, Shamsuddin Shahid. Spatiotemporal changes in rainfall and droughts of Bangladesh for1.5 and 2 °C temperature rise scenarios of CMIP6 models. Theoretical and Applied Climatology. 2021; ():1-16.

Chicago/Turabian Style

A. S. M. Maksud Kamal; Farhad Hossain; Shamsuddin Shahid. 2021. "Spatiotemporal changes in rainfall and droughts of Bangladesh for1.5 and 2 °C temperature rise scenarios of CMIP6 models." Theoretical and Applied Climatology , no. : 1-16.

Research article
Published: 27 July 2021 in Environmental Science and Pollution Research
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The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson’s correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson’s correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson’s correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.

ACS Style

Minglei Fu; Caowei Le; Tingchao Fan; Ryhor Prakapovich; Dmytro Manko; Oleh Dmytrenko; Dmytro Lande; Shamsuddin Shahid; Zaher Mundher Yaseen. Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction. Environmental Science and Pollution Research 2021, 1 -12.

AMA Style

Minglei Fu, Caowei Le, Tingchao Fan, Ryhor Prakapovich, Dmytro Manko, Oleh Dmytrenko, Dmytro Lande, Shamsuddin Shahid, Zaher Mundher Yaseen. Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction. Environmental Science and Pollution Research. 2021; ():1-12.

Chicago/Turabian Style

Minglei Fu; Caowei Le; Tingchao Fan; Ryhor Prakapovich; Dmytro Manko; Oleh Dmytrenko; Dmytro Lande; Shamsuddin Shahid; Zaher Mundher Yaseen. 2021. "Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction." Environmental Science and Pollution Research , no. : 1-12.

Original paper
Published: 05 July 2021 in Theoretical and Applied Climatology
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ACS Style

Mohammad Kamruzzaman; Shamsuddin Shahid; Arm Towfiqul Islam; Syewoon Hwang; JaePil Cho; Asad Uz Zaman; Minhaz Ahmed; Mizanur Rahman; Belal Hossain. Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study. Theoretical and Applied Climatology 2021, 1 -22.

AMA Style

Mohammad Kamruzzaman, Shamsuddin Shahid, Arm Towfiqul Islam, Syewoon Hwang, JaePil Cho, Asad Uz Zaman, Minhaz Ahmed, Mizanur Rahman, Belal Hossain. Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study. Theoretical and Applied Climatology. 2021; ():1-22.

Chicago/Turabian Style

Mohammad Kamruzzaman; Shamsuddin Shahid; Arm Towfiqul Islam; Syewoon Hwang; JaePil Cho; Asad Uz Zaman; Minhaz Ahmed; Mizanur Rahman; Belal Hossain. 2021. "Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study." Theoretical and Applied Climatology , no. : 1-22.

Research article
Published: 04 July 2021 in International Journal of Climatology
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Selection of suitable gridded precipitation data is deemed for hydroclimatic assessment and climate change impact analysis, especially in regions where long-term reliable precipitation data is unavailable. A novel approach based on fuzzy entropy similarity analysis (FESA) is proposed to evaluate the performance of four reanalysis gridded precipitation datasets (GPDs) for Egypt, namely European Reanalysis v.5. (ERA5), TerraClimate, Global Land Data Assimilation System (GLDAS)—Noah Land Surface Model L4 v.2 and Climatologies at high resolution for the Earth's land surface areas (CHELSA), against gauge records. The proposed method was verified using conventional statistics. Besides, the relative performance of different GPDs was verified according to their response to the influence of North Atlantic Oscillation (NAO) and Mediterranean Oscillation (MO) on winter precipitation. The performance of the best reanalysis GPD was also compared with the gauge-based global precipitation climatology centre (GPCC) dataset to show its reliability. The FESA revealed CHELSA as the best reanalysis GPD for Egypt. The performance assessment of GDPs based on conventional statistical metrics and visual presentation confirms the results obtained using FESA. CHELSA showed significant correlations with NAO (r = 0.3627) and MO (r = 0.624) like that obtained for gauge records. CHELSA also showed a better representation of precipitation in Egypt than GPCC at nearly half of the gauge locations. As CHELSA has a much higher spatial resolution than GPCC, it can be recommended as the proxy of gauge records in Egypt. The FESA can be used for performance analysis of gridded climate data by avoiding the complexities of using multiple statistical metrics.

ACS Style

Mohammed Magdy Hamed; Mohamed Salem Nashwan; Shamsuddin Shahid. Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis. International Journal of Climatology 2021, 1 .

AMA Style

Mohammed Magdy Hamed, Mohamed Salem Nashwan, Shamsuddin Shahid. Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis. International Journal of Climatology. 2021; ():1.

Chicago/Turabian Style

Mohammed Magdy Hamed; Mohamed Salem Nashwan; Shamsuddin Shahid. 2021. "Performance evaluation of reanalysis precipitation products in Egypt using fuzzy entropy time series similarity analysis." International Journal of Climatology , no. : 1.

Research article
Published: 03 June 2021 in Hydrological Sciences Journal
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Accurate prediction of dissolved oxygen (DO) concentration is important for managing healthy aquatic ecosystems. This study investigates the comparative potential of the Emotional Artificial Neural Network-Genetic Algorithm (EANN-GA), and three different ensemble techniques, i.e., Emotional Artificial Neural Network (EANN), Feed Forward Neural Network (FFNN), and Neural Network ensemble (NNE) to predict DO concentration in Kinta River basin of Malaysia. The performance of EANN-GA, EANN, FFNN, and NNE models in predicting DO was evaluated by using statistical metrics and visual interpretation. The appraisal of results revealed promising performance of the NNE-M3 model (Nash-Sutcliffe Efficiency: NSE = 0.8743/ 0.8630, Correlation Coefficient: CC = 0.9351/ 0.9113, Mean Square Error: MSE = 0.5757/ 0.6833 mg/L, Root Mean Square Error: RMSE = 0.7588/ 0.8266 mg/L, and Mean Absolute Percentage Error: MAPE = 20.6581/ 14.1675) during calibration/ validation period compared to EANN-GA, EANN, and FFNN models in DO prediction in the study basin.

ACS Style

S.I. Abba; R.A. Abdulkadir; Saad Sh. Sammen; A.G. Usman; Sarita Gajbhiye Meshram; Anurag Malik; Shamsuddin Shahid. Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration. Hydrological Sciences Journal 2021, 1 .

AMA Style

S.I. Abba, R.A. Abdulkadir, Saad Sh. Sammen, A.G. Usman, Sarita Gajbhiye Meshram, Anurag Malik, Shamsuddin Shahid. Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration. Hydrological Sciences Journal. 2021; ():1.

Chicago/Turabian Style

S.I. Abba; R.A. Abdulkadir; Saad Sh. Sammen; A.G. Usman; Sarita Gajbhiye Meshram; Anurag Malik; Shamsuddin Shahid. 2021. "Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration." Hydrological Sciences Journal , no. : 1.

Journal article
Published: 02 June 2021 in Sustainability
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This study projects water availability and sustainability in Nigeria due to climate change. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data (TWS), Global Precipitation Climatology Center (GPCC) precipitation data and Climate Research Unit (CRU) temperature data. Four general circulation models (GCMs) of the Coupled Model Intercomparison Project 5 were downscaled using the best of four downscaling methods. Two machine learning (ML) models, RF and SVM, were developed to simulate GRACE TWS data for the period 2002–2016 and were then used for the projection of spatiotemporal changes in TWS. The projected TWS data were used to assess the spatiotemporal changes in water availability and sustainability based on the reliability–resiliency–vulnerability (RRV) concept. This study revealed that linear scaling was the best for downscaling over Nigeria. RF had better performance than SVM in modeling TWS for the study area. This study also revealed there would be decreases in water storage during the wet season (June–September) and increases in the dry season (January–May). Decreases in projected water availability were in the range of 0–12 mm for the periods 2010–2039, 2040–2069, and 2070–2099 under RCP2.6 and in the range of 0–17 mm under RCP8.5 during the wet season. Spatially, annual changes in water storage are expected to increase in the northern part and decrease in the south, particularly in the country’s southeast. Groundwater sustainability was higher during the period 2070–2099 under all RCPs compared to the other periods and this can be attributed to the expected increases in rainfall during this period.

ACS Style

Mohammed Shiru; Shamsuddin Shahid; Inhwan Park. Projection of Water Availability and Sustainability in Nigeria Due to Climate Change. Sustainability 2021, 13, 6284 .

AMA Style

Mohammed Shiru, Shamsuddin Shahid, Inhwan Park. Projection of Water Availability and Sustainability in Nigeria Due to Climate Change. Sustainability. 2021; 13 (11):6284.

Chicago/Turabian Style

Mohammed Shiru; Shamsuddin Shahid; Inhwan Park. 2021. "Projection of Water Availability and Sustainability in Nigeria Due to Climate Change." Sustainability 13, no. 11: 6284.

Journal article
Published: 20 May 2021 in Journal of Hydrology
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Modelling river water level (WL) of a coastal catchment is much complex due to the tidal influences on river WL. A hybrid machine learning model based on relevance vector machine (RVM) and improved grasshopper optimization (IGOA) is proposed in this study for modelling hourly WL in a catchment located in the east coast of tropical peninsular Malaysia. Considering the non-linear relationship between inputs and output, a recursive elimination filter based on support vector machine (SVM-RFE) was employed for the selection of the best combination of inputs from antecedent WL and rainfall data for the prediction of WL one hour ahead. The performance of IGOA was compared with classical GOA and particle swarm optimization (PSO) algorithms. Besides, the performance of the hybrid RVM model was compared with the artificial neural network (ANN) models hybridized with the same optimization algorithms. The SVM-RFE selected 1-, 12- and 24-lags WL data and 1-lag rainfall data as the most potential inputs. The relative performance of the models revealed the reliability of RVM-IGOA in WL prediction of a coastal catchment. Significant improvement of model performance was noticed after optimization using IGOA with Nash-Sutcliff Efficiency (NSE) of 0.986 and 0.981, and Kling-Gupta Efficient (KGE) of 0.981 and 0.974 for RVM-IGOA and ANN-IGOA respectively, compared to the models hybridized using other optimization algorithms with NSE between 0.969 and 0.971, and KGE between 0.890 and 0.908. The study indicates the selection of predictors based on their non-linear relations with WL and better optimization of model parameters can improve model performance in simulation of highly complex hydrological phenomena like tidal river WL in a tropical coastal catchment.

ACS Style

Hai Tao; Najah Kadhim Al-Bedyry; Khaled Mohamed Khedher; Shamsuddin Shahid; Zaher Mundher Yaseen. River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization. Journal of Hydrology 2021, 598, 126477 .

AMA Style

Hai Tao, Najah Kadhim Al-Bedyry, Khaled Mohamed Khedher, Shamsuddin Shahid, Zaher Mundher Yaseen. River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization. Journal of Hydrology. 2021; 598 ():126477.

Chicago/Turabian Style

Hai Tao; Najah Kadhim Al-Bedyry; Khaled Mohamed Khedher; Shamsuddin Shahid; Zaher Mundher Yaseen. 2021. "River water level prediction in coastal catchment using hybridized relevance vector machine model with improved grasshopper optimization." Journal of Hydrology 598, no. : 126477.

Original paper
Published: 15 May 2021 in Natural Hazards
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The objective of this study was to reconstruct the long-term drought and flood time series to analyze their changing characteristics in Hengshui City of North China. Disaster records of the city for 550 years (1649–2018) were collected from different sources and sorted to reconstruct the sequences of droughts and floods. Advanced statistical methods for climate data analysis, including the Mann-Kendall test, Sen's slope estimator, Morlet wavelet analysis, mutation point tests, and Rescaled range analysis, were used to analyze the historical changes and predict the direction of possible future changes in droughts and floods. The results showed an increased frequency of droughts and a decreased frequency of floods in Hengshui City, making drought occurrence significantly higher than flood occurrence in the twentieth century. The solar activity cycle of 11-year and its multiple showed the association with 10–15 years and 25 years cycles of droughts and floods, respectively. The mutation points in drought and flood sequences during 1559–1568 and 1909–1918 showed insignificant downward (upward) and upward (downward) trends, respectively, in the drought (flood) subsequence before and after the mutation point. The rescaled range analysis revealed an insignificant decreasing trend in droughts and the continuation of the present decreasing trend in floods in the forthcoming years.

ACS Style

Jiaqi Sun; Xiaojun Wang; Yixing Yin; Shamsuddin Shahid. Analysis of historical drought and flood characteristics of Hengshui during the period 1649–2018: a typical city in North China. Natural Hazards 2021, 108, 2081 -2099.

AMA Style

Jiaqi Sun, Xiaojun Wang, Yixing Yin, Shamsuddin Shahid. Analysis of historical drought and flood characteristics of Hengshui during the period 1649–2018: a typical city in North China. Natural Hazards. 2021; 108 (2):2081-2099.

Chicago/Turabian Style

Jiaqi Sun; Xiaojun Wang; Yixing Yin; Shamsuddin Shahid. 2021. "Analysis of historical drought and flood characteristics of Hengshui during the period 1649–2018: a typical city in North China." Natural Hazards 108, no. 2: 2081-2099.

Research article
Published: 29 April 2021 in International Journal of Climatology
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This study compared the historical simulations and future projections of precipitation and temperature of Coupled Model Intercomparison Project (CMIP)5 and CMIP6 GCMs to quantify the differences in the projections due to differences in scenarios. Five performance indicators were used to quantify the model reproducibility of the observed precipitation levels at 22 stations for the historical period of 1970‐2005. The percentages of change in precipitation and temperature were estimated for the near (2025‐2060) and far future (2065‐2100) for two Representative Concentration Pathway (RCP)4.5 and RCP8.5 scenarios of CMIP5 and two Shared Socioeconomic Pathway (SSP)2‐4.5 and SSP5‐8.5 scenarios of CMIP6. The uncertainty in the projection in each case was calculated using the reliability ensemble average (REA) method. As a result, the CMIP6 GCMs showed an improvement compared to the CMIP5 GCMs with regard to the ability to simulate the historical climate. The uncertainty in the precipitation projections was higher for SSPs than that in RCPs. With regard to the temperature, the uncertainty was higher for RCPs than for SSPs. The ensemble means of the precipitation and temperature showed higher changes in the far future compared to the near future for both RCPs and SSPs. This study contributes to improvement in the confidence of future projections using CMIP6 GCMs and bolsters our understanding of the relative uncertainty in SSPs and RCPs.

ACS Style

Young Hoon Song; Eun‐Sung Chung; Shamsuddin Shahid. Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s. International Journal of Climatology 2021, 1 .

AMA Style

Young Hoon Song, Eun‐Sung Chung, Shamsuddin Shahid. Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s. International Journal of Climatology. 2021; ():1.

Chicago/Turabian Style

Young Hoon Song; Eun‐Sung Chung; Shamsuddin Shahid. 2021. "Spatiotemporal differences and uncertainties in projections of precipitation and temperature in South Korea from CMIP6 and CMIP5 general circulation model s." International Journal of Climatology , no. : 1.

Journal article
Published: 22 April 2021 in Sustainability
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The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

ACS Style

Rana Adnan; Kulwinder Parmar; Salim Heddam; Shamsuddin Shahid; Ozgur Kisi. Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering. Sustainability 2021, 13, 4648 .

AMA Style

Rana Adnan, Kulwinder Parmar, Salim Heddam, Shamsuddin Shahid, Ozgur Kisi. Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering. Sustainability. 2021; 13 (9):4648.

Chicago/Turabian Style

Rana Adnan; Kulwinder Parmar; Salim Heddam; Shamsuddin Shahid; Ozgur Kisi. 2021. "Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering." Sustainability 13, no. 9: 4648.

Original paper
Published: 13 April 2021 in Theoretical and Applied Climatology
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Reference evapotranspiration (ETo) is one of the foremost elements of the hydrology cycle which is essential for water resources management and irrigation applications. The current study is emphasized on the implementation of evolutionary computing models (i.e., gene expression programming (GEP)) for the simulation daily ETo in different locations of Peninsular Malaysia. The ETo models are developed using various input combinations of meteorological variables including air temperature (mean, maximum, and minimum), relative humidity, solar radiation, and mean wind speed. The in situ measurements of the ET are used to validate the model’s performance. The performance of the proposed GEP model is also compared with five well-established empirical formulations (EFs) developed based on the related climatological variability. The attained results evidenced the potential of GEP-derived ETo models in terms of all the statistical measures used. The best GEP model attained when all the meteorological variables are incorporated. However, the study revealed that the use of only temperature information can provide substantial predictability compared to EFs at all the studied stations across Peninsular Malaysia. This confirms the applicability of GEP in simulating ETo with fewer meteorological variables. The major advantage of GEP compared to other black box artificial intelligence algorithms is that GEP provides a set of equations which can be used by practitioners for reliable estimation of ETo at field with a fewer meteorological variable and, thus, can have wide applicability in water resources management.

ACS Style

Mohd Khairul Idlan Muhammad; Shamsuddin Shahid; Tarmizi Ismail; Sobri Harun; Ozgur Kisi; Zaher Mundher Yaseen. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theoretical and Applied Climatology 2021, 144, 1419 -1434.

AMA Style

Mohd Khairul Idlan Muhammad, Shamsuddin Shahid, Tarmizi Ismail, Sobri Harun, Ozgur Kisi, Zaher Mundher Yaseen. The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia. Theoretical and Applied Climatology. 2021; 144 (3):1419-1434.

Chicago/Turabian Style

Mohd Khairul Idlan Muhammad; Shamsuddin Shahid; Tarmizi Ismail; Sobri Harun; Ozgur Kisi; Zaher Mundher Yaseen. 2021. "The development of evolutionary computing model for simulating reference evapotranspiration over Peninsular Malaysia." Theoretical and Applied Climatology 144, no. 3: 1419-1434.

Journal article
Published: 02 April 2021 in Journal of Environmental Management
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Climate extremes have a significant impact on vegetation. However, little is known about vegetation response to climatic extremes in Bangladesh. The association of Normalized Difference Vegetation Index (NDVI) with nine extreme precipitation and temperature indices was evaluated to identify the nexus between vegetation and climatic extremes and their associations in Bangladesh for the period 1986–2017. Moreover, detrended fluctuation analysis (DFA) and Morlet wavelet analysis (MWA) were employed to evaluate the possible future trends and decipher the existing periodic cycles, respectively in the time series of NDVI and climate extremes. Besides, atmospheric variables of ECMWF ERA5 were used to examine the casual circulation mechanism responsible for climatic extremes of Bangladesh. The results revealed that the monthly NDVI is positively associated with extreme rainfall with spatiotemporal heterogeneity. Warm temperature indices showed a significant negative association with NDVI on the seasonal scale, while precipitation and cold temperature extremes showed a positive association with yearly NDVI. The DEA revealed a continuous increase in temperature extreme in the future, while no change in precipitation extremes. NDVI also revealed a significant association with extreme temperature indices with a time lag of one month and with precipitation extreme without time lag. Spatial analysis indicated insensitivity of marshy vegetation type to climate extremes in winter. The study revealed that elevated summer geopotential height, no visible anticyclonic center, reduced high cloud cover, and low solar radiation with higher humidity contributed to climatic extremes in Bangladesh. The nexus between NDVI and climatic extremes established in this study indicated that increasing warm temperature extremes due to global warming might have severe implications on Bangladesh's ecology and the environment in the future.

ACS Style

Abu Reza Md Towfiqul Islam; H.M.Touhidul Islam; Shamsuddin Shahid; Mst Khadiza Khatun; Mir Mohammad Ali; M.Safiur Rahman; Sobhy M. Ibrahim; Alia M. Almoajel. Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change. Journal of Environmental Management 2021, 289, 112505 .

AMA Style

Abu Reza Md Towfiqul Islam, H.M.Touhidul Islam, Shamsuddin Shahid, Mst Khadiza Khatun, Mir Mohammad Ali, M.Safiur Rahman, Sobhy M. Ibrahim, Alia M. Almoajel. Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change. Journal of Environmental Management. 2021; 289 ():112505.

Chicago/Turabian Style

Abu Reza Md Towfiqul Islam; H.M.Touhidul Islam; Shamsuddin Shahid; Mst Khadiza Khatun; Mir Mohammad Ali; M.Safiur Rahman; Sobhy M. Ibrahim; Alia M. Almoajel. 2021. "Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change." Journal of Environmental Management 289, no. : 112505.

Paper
Published: 16 March 2021 in Hydrogeology Journal
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A cost-effective “parsimonious” approach to delineating groundwater potential zones is proposed, based on catastrophe theory (CT) and analytical hierarchy process (AHP) in a geographic information system (GIS). Eleven indicators that influence groundwater storage (slope, drainage density, surface-water body (proximity), soil permeability, aquitard thickness, aquifer thickness, hydraulic conductivity, specific yield, recharge, aquitard resistivity and aquifer resistivity) were prepared. The suitable weights of the factors and the index values of the features of the factors were normalized using AHP and CT multicriteria decision analysis (MCDA) techniques for the development of a groundwater potential index (GWPI) map. Finally, the relative sensitivity of the factors was evaluated to develop a parsimonious groundwater potential index (P-GWPI) map using the most sensitive themes. GWPI and P-GWPI maps were validated using 14-year average annual post-monsoon depth to groundwater level data of 36 monitoring wells in a study area in Bangladesh. The generated GWPI map classified the study area as moderately good, good and very good groundwater potential covering an area of 19.5, 40.3 and 40.2% respectively. Subsequently, a modified GWPI map was developed using effective weights derived from single-parameter sensitivity analysis. The P-GWPI map developed using the most sensitive factors categorized the groundwater potential zones as moderately good (13.0%), good (38.2%) and very good (48.8%). The results of this study can serve as guidelines for future groundwater exploration, planning and management of the area, and the methodology used can also be easily adopted in other similar and data-scarce areas.

ACS Style

M. Shahinuzzaman; M. Nozibul Haque; Shamsuddin Shahid. Delineation of groundwater potential zones using a parsimonious concept based on catastrophe theory and analytical hierarchy process. Hydrogeology Journal 2021, 29, 1091 -1116.

AMA Style

M. Shahinuzzaman, M. Nozibul Haque, Shamsuddin Shahid. Delineation of groundwater potential zones using a parsimonious concept based on catastrophe theory and analytical hierarchy process. Hydrogeology Journal. 2021; 29 (3):1091-1116.

Chicago/Turabian Style

M. Shahinuzzaman; M. Nozibul Haque; Shamsuddin Shahid. 2021. "Delineation of groundwater potential zones using a parsimonious concept based on catastrophe theory and analytical hierarchy process." Hydrogeology Journal 29, no. 3: 1091-1116.

Original paper
Published: 12 March 2021 in Theoretical and Applied Climatology
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Accurate representation of precipitation over time and space is vital for hydro-climatic studies. Appropriate selection of gridded precipitation data (GPD) is important for regions where long-term in situ records are unavailable and gauging stations are sparse. This study was an attempt to identify the best GPD for the data-poor Amu Darya River basin, a major source of freshwater in Central Asia. The performance of seven GPDs and 55 precipitation gauge locations was assessed. A novel algorithm, based on the integration of a compromise programming index (CPI) and a global performance index (GPI) as part of a multi-criteria group decision-making (MCGDM) method, was employed to evaluate the performance of the GPDs. The CPI and GPI were estimated using six statistical indices representing the degree of similarity between in situ and GPD properties. The results indicated a great degree of variability and inconsistency in the performance of the different GPDs. The CPI ranked the Climate Prediction Center (CPC) precipitation as the best product for 20 out of 55 stations analysed, followed by the Princeton University Global Meteorological Forcing (PGF) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS). Conversely, GPI ranked the CPC product the best product for 25 of the stations, followed by PGF and CHRIPS. Integration of CPI and GPI ranking through MCGDM revealed that the CPC was the best precipitation product for the Amu River basin. The performance of PGF was also closely aligned with that of CPC.

ACS Style

Obaidullah Salehie; Tarmizi Ismail; Shamsuddin Shahid; Kamal Ahmed; S Adarsh; Asaduzzaman; Ashraf Dewan. Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin. Theoretical and Applied Climatology 2021, 144, 985 -999.

AMA Style

Obaidullah Salehie, Tarmizi Ismail, Shamsuddin Shahid, Kamal Ahmed, S Adarsh, Asaduzzaman, Ashraf Dewan. Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin. Theoretical and Applied Climatology. 2021; 144 (3-4):985-999.

Chicago/Turabian Style

Obaidullah Salehie; Tarmizi Ismail; Shamsuddin Shahid; Kamal Ahmed; S Adarsh; Asaduzzaman; Ashraf Dewan. 2021. "Ranking of gridded precipitation datasets by merging compromise programming and global performance index: a case study of the Amu Darya basin." Theoretical and Applied Climatology 144, no. 3-4: 985-999.

Journal article
Published: 04 March 2021 in Chemosphere
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Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.

ACS Style

Suraj Kumar Bhagat; Konstantina Pyrgaki; Sinan Q. Salih; Tiyasha Tiyasha; Ufuk Beyaztas; Shamsuddin Shahid; Zaher Mundher Yaseen. Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model. Chemosphere 2021, 276, 130162 .

AMA Style

Suraj Kumar Bhagat, Konstantina Pyrgaki, Sinan Q. Salih, Tiyasha Tiyasha, Ufuk Beyaztas, Shamsuddin Shahid, Zaher Mundher Yaseen. Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model. Chemosphere. 2021; 276 ():130162.

Chicago/Turabian Style

Suraj Kumar Bhagat; Konstantina Pyrgaki; Sinan Q. Salih; Tiyasha Tiyasha; Ufuk Beyaztas; Shamsuddin Shahid; Zaher Mundher Yaseen. 2021. "Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model." Chemosphere 276, no. : 130162.

Original paper
Published: 28 February 2021 in Theoretical and Applied Climatology
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This research investigates spatiotemporal variations in ETo and the controlling factor of those variations using the modified Mann-Kendall test, empirical Bayesian kriging model, Morlet wavelet analysis (MWA), and cross-wavelet transform (XWT) model relying on daily climate data sets obtained from 18 meteorological stations for the period 1980–2017. Additionally, the stepwise linear regression analysis and partial correlation coefficient (PCC) were employed to determine the variables driving the changes in ETo. The investigation exhibited a decline in annual for −1.19 mm year−1 and seasonal (−0.40 mm decade−1 during pre-monsoon, −0.47 mm decade−1 during post-monsoon, −0.50 mm decade−1 during winter) ETo, which indicates the existence of “evapotranspiration paradox” in Bangladesh, similar to many regions across the globe. The trend test depicted that despite the increase in mean temperature (MT), a noteworthy decrease in sunshine duration (SD), and wind speed (WS) are the main reasons for the reduction in ETo. Spatial analysis of ETo revealed the highest annual values in the southwest while the lowest in the northwest. Two cycles, 1–3 and 3–5 years were found significant in the annual and seasonal ETo. The outcomes revealed coherence among ETo with meteorological factors at different time-frequency bands, which is noteworthy. Stepwise regression and PCC showed that the impact of meteorological factors on ETo varies on the annual and seasonal scales where MT, RH, and SD are the major factors responsible for the variations of ETo in both annual and seasonal scales. These outcomes of the research can be advantageous for designing irrigation and management of sustainable water resources to mitigate climate change impacts as well as controlling anthropogenic activities.

ACS Style

Jannatun Nahar Jerin; H. M. Touhidul Islam; Abu Reza Md. Towfiqul Islam; Shamsuddin Shahid; Zhenghua Hu; Mehnaz Abbasi Badhan; Ronghao Chu; Ahmed Elbeltagi. Spatiotemporal trends in reference evapotranspiration and its driving factors in Bangladesh. Theoretical and Applied Climatology 2021, 144, 793 -808.

AMA Style

Jannatun Nahar Jerin, H. M. Touhidul Islam, Abu Reza Md. Towfiqul Islam, Shamsuddin Shahid, Zhenghua Hu, Mehnaz Abbasi Badhan, Ronghao Chu, Ahmed Elbeltagi. Spatiotemporal trends in reference evapotranspiration and its driving factors in Bangladesh. Theoretical and Applied Climatology. 2021; 144 (1-2):793-808.

Chicago/Turabian Style

Jannatun Nahar Jerin; H. M. Touhidul Islam; Abu Reza Md. Towfiqul Islam; Shamsuddin Shahid; Zhenghua Hu; Mehnaz Abbasi Badhan; Ronghao Chu; Ahmed Elbeltagi. 2021. "Spatiotemporal trends in reference evapotranspiration and its driving factors in Bangladesh." Theoretical and Applied Climatology 144, no. 1-2: 793-808.

Journal article
Published: 02 February 2021 in Sustainability
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An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources.

ACS Style

Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability 2021, 13, 1549 .

AMA Style

Ziauddin Safari, Sayed Rahimi, Kamal Ahmed, Ahmad Sharafati, Ghaith Ziarh, Shamsuddin Shahid, Tarmizi Ismail, Nadhir Al-Ansari, Eun-Sung Chung, Xiaojun Wang. Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing. Sustainability. 2021; 13 (3):1549.

Chicago/Turabian Style

Ziauddin Safari; Sayed Rahimi; Kamal Ahmed; Ahmad Sharafati; Ghaith Ziarh; Shamsuddin Shahid; Tarmizi Ismail; Nadhir Al-Ansari; Eun-Sung Chung; Xiaojun Wang. 2021. "Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing." Sustainability 13, no. 3: 1549.

Research article
Published: 01 January 2021 in Engineering Applications of Computational Fluid Mechanics
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Ensuring accurate estimation of evaporation is weighty for effective planning and judicious management of available water resources for agricultural practices. Thus, this work enhances the potential of support vector regression (SVR) optimized with a novel nature-inspired algorithm, namely, Slap Swarm Algorithm (SVR-SSA) against Whale Optimization Algorithm (SVR-WOA), Multi-Verse Optimizer (SVR-MVO), Spotted Hyena Optimizer (SVR-SHO), Particle Swarm Optimization (SVR-PSO), and Penman model (PM). Daily EP (pan-evaporation) was estimated in two different agro-climatic zones (ACZ) in northern India. The optimal combination of input parameters was extracted by applying the Gamma test (GT). The outcomes of the hybrid of SVR and PM models were equated with recorded daily EP observations based on goodness-of-fit measures along with graphical scrutiny. The results of the appraisal showed that the novel hybrid SVR-SSA-5 model performed superior (MAE = 0.697, 1.556, 0.858 mm/day; RMSE = 1.116, 2.114, 1.202 mm/day; IOS = 0.250, 0.350, 0.303; NSE = 0.0.861, 0.750, 0.834; PCC = 0.929, 0.868, 0.918; IOA = 0.960, 0.925, 0.956) than other models in testing phase at Hisar, Bathinda, and Ludhiana stations, respectively. In conclusion, the hybrid SVR-SSA model was identified as more suitable, robust, and reliable than the other models for daily EP estimation in two different ACZ.

ACS Style

Anurag Malik; Yazid Tikhamarine; Nadhir Al-Ansari; Shamsuddin Shahid; Harkanwaljot Singh Sekhon; Raj Kumar Pal; Priya Rai; Kusum Pandey; Padam Singh; Ahmed Elbeltagi; Saad Shauket Sammen. Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Engineering Applications of Computational Fluid Mechanics 2021, 15, 1075 -1094.

AMA Style

Anurag Malik, Yazid Tikhamarine, Nadhir Al-Ansari, Shamsuddin Shahid, Harkanwaljot Singh Sekhon, Raj Kumar Pal, Priya Rai, Kusum Pandey, Padam Singh, Ahmed Elbeltagi, Saad Shauket Sammen. Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Engineering Applications of Computational Fluid Mechanics. 2021; 15 (1):1075-1094.

Chicago/Turabian Style

Anurag Malik; Yazid Tikhamarine; Nadhir Al-Ansari; Shamsuddin Shahid; Harkanwaljot Singh Sekhon; Raj Kumar Pal; Priya Rai; Kusum Pandey; Padam Singh; Ahmed Elbeltagi; Saad Shauket Sammen. 2021. "Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test." Engineering Applications of Computational Fluid Mechanics 15, no. 1: 1075-1094.

Journal article
Published: 31 December 2020 in Sustainability
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The potential or reference evapotranspiration (ET0) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET0. The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures (Tmax and Tmin) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET0, respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET0. The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

ACS Style

Rana Adnan; Salim Heddam; Zaher Yaseen; Shamsuddin Shahid; Ozgur Kisi; Binquan Li. Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches. Sustainability 2020, 13, 297 .

AMA Style

Rana Adnan, Salim Heddam, Zaher Yaseen, Shamsuddin Shahid, Ozgur Kisi, Binquan Li. Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches. Sustainability. 2020; 13 (1):297.

Chicago/Turabian Style

Rana Adnan; Salim Heddam; Zaher Yaseen; Shamsuddin Shahid; Ozgur Kisi; Binquan Li. 2020. "Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches." Sustainability 13, no. 1: 297.

Journal article
Published: 25 December 2020 in Atmospheric Research
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The provision of high resolution near real-time rainfall data has made satellite rainfall products very potential for monitoring hydrological hazards. However, a major challenge in their direct-use can be problematic due to measurement error. In this study, an attempt was made to correct the bias of Global Satellite Mapping of Precipitation near-real-time (GSMaP_NRT) product. Physical factors, including topography, season, windspeed and cloud types were accounted for correcting bias. Peninsular Malaysia was used as the case study area. Gridded rainfall, developed from 80 gauges for the period 2000–2018, was used along with physical factors in a two-stage procedure. The model consisted of a classifier to categorise rainfall of different intensity and regression models to predict rainfall amount of different intensity class. An ensemble tree-based learning algorithm, called random forest, was used for classification and regression. The results revealed a big improvement of near-real-time GSMaP_NRT product after bias correction (GSMaP_BC) compared to the gauge corrected version (GSMaP_GC). Accuracy evaluation for complete timeseries indicated about 110% reduction of normalized root-mean-square error (NRMSE) in GSMaP_BC (0.8) compared to GSMaP_NRT (1.7) and GSMaP_GC (1.75). On the other hand, the bias of GSMaP_BC became nearly zero (0.3) compared to 2.1 and − 3.1 for GSMaP_NRT and GSMaP_GC products. The spatial correlation of GSMaP_BC was >0.7 with observed rainfall data for all months compared to 0.2–0.78 for GSMaP_NRT and GSMaP_GC, indicating capability of GSMaP_BC to replicate spatial pattern of rainfall. The bias-corrected near-real-time GSMaP data can be used for monitoring and forecasting floods and hydrological phenomena in the absence of dense rain-gauge network in areas, frequently experience hydro-meteorological hazards.

ACS Style

Ghaith Falah Ziarh; Shamsuddin Shahid; Tarmizi Bin Ismail; Asaduzzaman; Ashraf Dewan. Correcting bias of satellite rainfall data using physical empirical model. Atmospheric Research 2020, 251, 105430 .

AMA Style

Ghaith Falah Ziarh, Shamsuddin Shahid, Tarmizi Bin Ismail, Asaduzzaman, Ashraf Dewan. Correcting bias of satellite rainfall data using physical empirical model. Atmospheric Research. 2020; 251 ():105430.

Chicago/Turabian Style

Ghaith Falah Ziarh; Shamsuddin Shahid; Tarmizi Bin Ismail; Asaduzzaman; Ashraf Dewan. 2020. "Correcting bias of satellite rainfall data using physical empirical model." Atmospheric Research 251, no. : 105430.