Concern on alteration of sediment natural flow caused by developments of water resources system, has been addressed in many river basins around the world especially in developing and remote regions where sediment data are poorly gauged or ungauged. Since suspended sediment load (SSL) is predominant, the objectives of this research are to: 1) simulate monthly average SSL (SSLm) of four catchments using artificial neural network (ANN); 2) assess the application of the calibrated ANN (Cal-ANN) models in three ungauged catchment representatives (UCR) before using them to predict SSLm of three actual ungauged catchments (AUC) in the Tonle Sap River Basin; and 3) estimate annual SSL (SSLA) of each AUC for the case of with and without dam-reservoirs. The model performance for total load (SSLT) prediction was also investigated because it is important for dam-reservoir management. For model simulation, ANN yielded very satisfactory results with determination coefficient (R2) ranging from 0.81 to 0.94 in calibration stage and 0.63 to 0.87 in validation stage. The Cal-ANN models also performed well in UCRs with R2 ranging from 0.59 to 0.64. From the result of this study, one can estimate SSLm and SSLT of ungauged catchments with an accuracy of 0.61 in term of R2 and 34.06% in term of absolute percentage bias, respectively. SSLA of the AUCs was found between 159,281 and 723,580 t/year. In combination with Brune’s method, the impact of dam-reservoirs could reduce SSLA between 47% and 68%. This result is key information for sustainable development of such infrastructures.
 D. E. Walling and D. Fang, “Recent Trends in the Suspended Sediment Loads of the World’s Rivers,” Global and Planetary Change, Vol. 39, No. 1-2, 2003, pp. 111-126. doi:10.1016/S0921-8181(03)00020-1
 M. Kummu, X. X. Lu, J. J. Wang and O. Varis, “Basin-Wide Sediment Trapping Efficiency of Emerging Reservoirs along the Mekong,” Geomorphology, Vol. 119, No. 3-4, 2010, pp. 181-197. doi:10.1016/j.geomorph.2010.03.018
 H. K. Cigizoglu, “Estimation and Forecasting of Daily Suspended Sediment Data by Multi-Layer Perceptrons,” Advances in Water Resources, Vol. 27, No. 2, 2003, pp. 185-195. doi:10.1016/j.advwatres.2003.10.003
 A. J. Horowitz, “An Evaluation of Sediment Rating Curves for Estimating Suspended Sediment Concentrations for Subsequent Flux Calculations,” Hydrological Processes, Vol. 17, No. 17, 2003, pp. 3387-3409. doi:10.1002/hyp.1299
 O. Kisi, “Multi-Layer Perceptrons with Levenberg-Marquardt Training Algorithm for Suspended Sediment Concentration Prediction and Estimation,” Hydrological Sciences Journal, Vol. 49, No. 6, 2004, pp. 1025-1040. doi:10.1623/hysj.49.6.1025.55720
 O. M. Rezapour, L. T. Shui and D. B. Ahmad, “Review of Artificial Neural Network Model for Suspended Sediment Estimation,” Australian Journal of Basic and Applied Sciences, Vol. 4, No. 8, 2010, pp. 3347-3353.
 A. M. Melesse, S. Ahmad, M. E. McClain, X. Wang and Y. H. Lim, “Suspended Sediment Load Prediction of River Systems: An Artificial Neural Network Approach,” Agricultural Water Management, Vol. 98, No. 5, 2011, pp. 855-866. doi:10.1016/j.agwat.2010.12.012
 A. Singh, M. Imtiyaz, R. K. Isaac and D. M. Denis, “Comparison of Soil and Water Assessment Tool (SWAT) and Multilayer Perceptron (MLP) Artificial Neural Network for Predicting Sediment Yield in the Nagwa Agricultural Watershed in Jharkhand, India,” Agricultural Water Management, Vol. 104, 2011, pp. 113-120. doi:10.1016/j.agwat.2011.12.005
 O. Kisi and J. Shiri, “River Suspended Sediment Estimation by Climatic Variables Implication: Comparative Study among Soft Computing Techniques,” Computers & Geosciences, Vol. 43, 2012, pp. 73-82. doi:10.1016/j.cageo.2012.02.007
 J. M. Wicks and J. C. Bathurst, “SHESED: A Physically Based, Distributed Erosion and Sediment Yield Component for the SHE Hydrological Model System,” Journal of Hydrology, Vol. 175, No. 1-4, 1996, pp. 213-238. doi:10.1016/S0022-1694(96)80012-6
 J. A. Warrick and D. M. Rubin, “Suspended-Sediment Rating Curve Response to Urbanization and Wildfire, Santa Ana River, California,” Journal of Geophysical Research, Vol. 112, No. F2, 2007, Article ID: F02018. doi:10.1029/2006JF000662
 J. R. Williams, “Sediment Yield Prediction with Universal Equation Using Runoff Energy Factor, in Present and Prospective Technology for Predicting Sediment Yields and Sources, ARS-S-40,” USDA, Washington DC, 1975.
 H. R. Maier and G. C. Dandy, “Neural Networks for the Prediction and Forecasting of Water Resources Variables: A Review of Modelling Issues and Applications,” Environmental Modelling & Software, Vol. 15, No. 1, 1999, pp. 101-124. doi:10.1016/S1364-8152(99)00007-9
 V. Nourani, O. Kalantari and A. Baghanam, “Two Semi-distributed ANN-Based Models for Estimation of Suspended Sediment Load,” Journal of Hydrologic Engineering, Vol. 17, No. 12, 2012, pp 1368-1380. doi:10.1061/(ASCE)HE.1943-5584.0000587
 Y. M. Zhu, X. X. Lu and Y. Zhou, “Suspended Sediment Flux Modeling with Artificial Neural Network: An Example of the Longchuangjiang River in the Upper Yangtze Catchment, China,” Geomorphology, Vol. 84, No. 1-2, 2006, pp. 111-125. doi:10.1016/j.geomorph.2006.07.010
 M. Talebizadeh, S. Morid, S. A. Ayyoubzadeh and M. Ghasemzadeh, “Uncertainty Analysis in Sediment Load Modeling using ANN and SWAT Model,” Water Resources Management, Vol. 24, No. 9, 2009, pp. 1747-1761. doi:10.1007/s11269-009-9522-2
 H. K. Cigizoglu, “Suspended Sediment Estimation for Rivers Using Artificial Neural Networks and Sediment Rating Curves,” Turkish Journal of Engineering and Environmental Sciences, Vol. 26, No. 1, 2002, pp. 27-36.
 H. J. Fuchs, “Data Availability for Studies on Effects of Land-Cover Changes on Water Yield, Sediment and Nutrient Load at Catchments of the Lower Mekong Basin,” MRC-GTZ Cooperation Programme, Gottingen, 2004.
 METI and NASA (Ministry of Economy, Trade and Industry, Japan, and National Aeronautics and Space Administration), “Advanced Spaceborne Thermal Emission and Reflection Radiometer: Global Digital Elevation Model Version 2,” 2011. http://www.jspacesystems.or.jp/ersdac/GDEM/E/4.html
 EC-JRC (European Commission Joint Research Center), “The Land Cover Map for South East Asia in the Year 2000,” 2003. http://bioval.jrc.ec.europa.eu/products/glc2000/products.php
 FAO (Food and Agriculture Organization of the United Nations), “The Digital Soil Map of the World,” 2003. http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116
 A. Agarwal, R. K. Rai and A. Upadhyay, “Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks,” Journal of Water Resource and Protection, Vol. 1, No. 5, 2009, pp. 368-375. doi:10.4236/jwarp.2009.15044
 N. N. I. Ahmat, S. Harun, J. Ariffin and S. Abdul-Talib, “Sediment Transport Prediction by Using 3-Layer Feed-forward MLP Networks,” The 11th International Conference on Urban Drainage, Edinburgh, 31 August-5 September 2008.
 L. E. Besaw, D. M. Rizzo, P. R. Bierman and W. R. Hackett, “Advances in Ungauged Streamflow Prediction Using Artificial Neural Networks,” Journal of Hydrology, Vol. 386, No. 1-4, 2010, pp. 23-37. doi:10.1016/j.jhydrol.2010.02.037
 B. Bhattacharya, R. K. Price and D. P. Solomatine, “Data-Driven Modelling in the Context of Sediment Transport,” Physics and Chemistry of the Earth, Vol. 30, No. 4-5, 2005, pp. 297-302. doi:10.1016/j.pce.2004.12.001
 C. Chutachindakate and T. Sumi, “Sediment Yield and Transportation Analysis: Case Study on Managawa River Basin,” Annual Journal of Hydraulic Engineering, Vol. 52, 2008, pp. 157-162. doi:10.2208/prohe.52.157
 M. R. Mustafa, M. H. Isa and R. B. Rezaur, “A Comparison of Artificial Neural Networks for Prediction of Suspended Sediment Discharge in River: A Case Study in Malaysia,” World Academy of Science, Engineering and Technology, Vol. 81, 2011, pp. 372-376.
 V. Nourani, “Using Artificial Neural Networks (ANNs) for Sediment Load Forecasting of Talkherood River Mouth,” Journal of Urban and Environmental Engineering, Vol. 3, No. 1, 2009, pp. 1-6. doi:10.4090/juee.2009.v3n1.001006
 T. Rajaee, “Wavelet and ANN Combination Model for Prediction of Daily Suspended Sediment Load in Rivers,” Science of the Total Environment, Vol. 409, No. 15, 2010, pp. 2917-2928. doi:10.1016/j.scitotenv.2010.11.028
 T. Rajaee, S. A. Mirbagheri, M. Zounemat-Kermani and V. Nourani, “Daily Suspended Sediment Concentration Simulation Using ANN and Neuro-Fuzzy Models,” Science of the Total Environment, Vol. 407, No. 17, 2009, pp. 4916-4927. doi:10.1016/j.scitotenv.2009.05.016
 A. Sarangi, C. A. Madramootoo, P. Enright, S. O. Prasher and R. M. Patel, “Performance Evaluation of ANN and Geomorphology-Based Models for Runoff and Sediment Yield Prediction for a Canadian Watershed,” Current Science, Vol. 89, No. 12, 2005, pp. 2022-2033.
 G. Singh and R. K. Panda, “Daily Sediment Yield Modeling with Artificial Neural Network Using 10-Fold Cross Validation Method: A Small Agricultural Watershed, Ka- pgari, India,” International Journal of Earth Sciences and Engineering, Vol. 4, No. 6, 2011, pp. 443-450.
 C. T. Yang, R. Marsooli and M. T. Aalami, “Evaluation of Total Load Sediment Transport Formulas Using ANN,” International Journal of Sediment Research, Vol. 24, No. 3, 2009, pp. 274-286. doi:10.1016/S1001-6279(10)60003-0
 D. N. Moriasi, J. G. Arnold, M. W. V. Liew, R. L. Bingner, R. D. Harmel and T. L. Veith, “Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations,” Transactions of the American Society of Agriculture and Biological Engineers, Vol. 50, No. 3, 2007, pp. 885-900.
 B. Hu, Z. Yang, H. Wang, X. Sun, N. Bi and G. Li, “Sedimentation in the Three Gorges Dam and the Future Trend of Changjiang (Yangtze River) Sediment Flux to the Sea,” Hydrology and Earth System Sciences, Vol. 13, No. 11, 2009, pp. 2253-2264. doi:10.5194/hess-13-2253-2009
 V. Jothiprakash and V. Garg, “Re-Look to Conventional Techniques for Trapping Efficiency Estimation of a Reservoir,” International Journal of Sediment Research, Vol. 23, No. 1, 2008, pp. 76-84. doi:10.1016/S1001-6279(08)60007-4
 C. J. Vorosmarty, M. Meybeck, B. Fekete, K. Sharma, P. Green and J. P. M. Syvitski, “Anthropogenic Sediment Retention: Major Global Impact from Registered River Impoundments,” Global and Planetary Change, Vol. 39, No. 1-2, 2003, pp. 169-190. doi:10.1016/S0921-8181(03)00023-7
 M. Kummu and O. Varis, “Sediment-Related Impacts due to Upstream Reservoir Trapping, the Lower Mekong River,” Geomorphology, Vol. 85, No. 3-4, 2006, pp. 275- 293. doi:10.1016/j.geomorph.2006.03.024
 K. D. Fu and D. M. He, “Analysis and Prediction of Sediment Trapping Efficiencies of the Reservoirs in the Mainstream of the Lancang River,” Chinese Science Bulletin, Vol. 52, No. 2, 2007, pp. 134-140. doi:10.1007/s11434-007-7026-0
 H. Memarian and S. K. Balasundram, “Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed,” Journal of Water Resource and Protection, Vol. 4, No. 10, 2012, pp. 870-876. doi:10.4236/jwarp.2012.410102
 T. Rajaee, S. A. Mirbagheri, V. Nourani and A. Alikhani, “Prediction of Daily Suspended Sediment Load Using Wavelet and Neuro-Fuzzy Combined Model,” International Journal of Environmental Science and Technology, Vol. 7, No. 1, 2009, pp. 93-110.