Estimation of Concentration of Suspended Sediments with Optical-ultrasonic Hybrid System and ANFIS Modeling

Document Type : Research Paper

Authors

1 Ph.D. Candidate., Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

2 Assistant Professor, Mechanical Engineering of Biosystems Department, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

3 Associate Professor, Agricultural Machinery Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

Abstract

Developing a robust and reliable estimation method to detect suspended sediment concentrations from various environmental and geomorphological aspects including water quality, riverbed sustainability engineering, flood management and aquatic habitats is an unavoidable necessity. In this research, a new approach has been developed using a combined optical-acoustic sensors and hybrid intelligence-based system of ANFIS modeling to predict the concentration of suspended river sediments. Also, two measurement systems were placed in a water tank in vitro, and every 50 seconds, 10 g of soil (passed through sieve 140) was added to the water as suspended sediment until the total sediment in the water was 100 grams. The operation was performed in 20 iterations and the output values ​​of the two measurement methods were given as inputs. Interface structure with only optical sensor inputs with higher efficiency coefficient of determination (R2) 0.94 and mean square error root mean square error (RMSE) 7.15 (gr) compared with the ultrasonic sensor inputs with coefficient of determination (R2) of 0.91 and root of the mean squared error was 8.72 (gr). Also, the highest efficiency of hybrid structure with two inputs of two measurement methods had coefficient of determination (R2) 0.97 and root mean square error was 5.26 (gr). According to the results, the best distance between receiver and transmitter in the ultrasonic sensor was between 8 and 15 cm and the use of hybrid system in sediment estimation was more efficient with an error of 3 and 1.5 percent less than the error of separate ultrasonic and optical systems.

Keywords


Afan, H.A., El-Shafie, A., Yaseen, Z.M., Hameed, M.M., Wan Mohtar, W.H.M., Hussain, A., (2014). ANN based sediment prediction model utilizing different input scenarios. Water Resour. Manag. 29, 1231–1245. https://doi.org/10.1007/s11269-014-0870-1.
Ali, J. M., Hussain, M. A., Tade, M. O., & Zhang, J. (2015). Artificial Intelligence techniques applied as estimator in chemical process systems–A literature survey. Expert Systems with Applications, 42(14), 5915-5931.‏
ASTM D3977-97 2013 Standard test methods for determining sediment concentration in water samples www.astm.org/ Standards/D3977.htm
Ban, Y., Chen, T., Yan, J., & Lei, T. (2017). Accurate mass replacement method for the sediment concentration measurement with a constant volume container. Measurement Science and Technology, 28(4), 045906.‏
Bricaud, A., Roesler, C., & Zaneveld, J. R. V. (1995). In situ methods for measuring the inherent optical properties of ocean waters. Limnology and Oceanography, 40(2), 393-410.‏
Buttmann, M. (2001). Suspended solids measurement as reliable process control. In Proceedings of ISA TECH EXPO Technology Update Conference, Houston, TX: Instrument Society of America (Vol. 413, No. 1, pp. 563-572).‏
Buyukyildiz, M., & Kumcu, S. Y. (2017). An estimation of the suspended sediment load using adaptive network based fuzzy inference system, support vector machine and artificial neural network models. Water resources management, 31(4), 1343-1359.‏
Chang, H. H. (2008). River morphology and river channel changes. Transactions of Tianjin University, 14(4), 254-262.‏
Crickmore, M. Tazioli, G. S., Appleby, P. G., & Oldfield, F. (1990). The use of nuclear techniques in sediment transport and sedimentation problems (p. 170p). M. J. Crickmore (Ed.). Unesco.‏
Dogan, E. (2005). Suspended Sediment Load Estimation in Lower Sakarya River By Using Artificial Neural Networks, Fuzzy Logic and Neuro-Fuzzy Models. Electronic Letters on Science&Engineering, 1(2), 22-32.‏
DotOcean, Dot ocean Company (website: https://www.dotocean.eu/products-2/densx/ at Feb 2018). 2018.
Felix, D. (2017). Experimental investigation on suspended sediment, hydro-abrasive erosion and efficiency reductions of coated Pelton turbines (Doctoral dissertation, ETH Zurich).‏
Frings, R. M. (2008). Downstream fining in large sand-bed rivers. Earth-Science Reviews, 87(1-2), 39-60.‏
Guerrero, M., Rüther, N., Haun, S., & Baranya, S. (2017). A combined use of acoustic and optical devices to investigate suspended sediment in rivers. Advances in Water Resources, 102, 1-12.‏
Ha, H. K., Maa, J. Y., Park, K., & Kim, Y. H. (2011). Estimation of high-resolution sediment concentration profiles in bottom boundary layer using pulse-coherent acoustic Doppler current profilers. Marine Geology, 279(1-4), 199-209.‏
Huang, M., Ma, Y., Wan, J., & Chen, X. (2015). A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Applied Soft Computing, 27, 1-10.‏
Jang, J. (1993). ANFIS: adaptive network-based fuzzy inference system, IEEE T. Syst. Man Cyb., 23 (3), 665-685.‏
Li, X., Lei, T., Wang, W., Xu, Q., & Zhao, J. (2005). Capacitance sensors for measuring suspended sediment concentration. Catena, 60(3), 227-237.‏
Lynch, J. F., Irish, J. D., Sherwood, C. R., & Agrawal, Y. C. (1994). Determining suspended sediment particle size information from acoustical and optical backscatter measurements. Continental Shelf Research14(10-11), 1139-1165.‏
Martinez, J. M., Guyot, J. L., Filizola, N., & Sondag, F. (2009). Increase in suspended sediment discharge of the Amazon River assessed by monitoring network and satellite data. Catena, 79(3), 257-264.‏.
Niazi, H., Mohammad Zamani, D., Sedaghat Hosseini. (2015), Design, construction and evaluation of a system for determining the actual cutting width of cylinder harvesters by ultrasonic sensors, Bioengineering Engineering Certificate .4( 2).
Rezai Banafshe. M., Feyzolahpour. M., SadrAfshary. S., (2013). Using Neural Fuzzy Inference System to Estimate Sediment Load and a Comparison with MLR and SRC Models in Ghranghu River Basin. physical geography research quarterly. 45,77-90. https://doi.org/10.22059/JPHGR.2013.35145
Samantaray, S., & Sahoo, A. (2020). Assessment of sediment concentration through RBNN and SVM-FFA in Arid Watershed, India. In Smart Intelligent Computing and Applications (pp. 701-709). Springer, Singapore.‏
Sheikhali Pour .Z., Hassan Pour. F., Azimi .V.(2015). Comparison of artificial intelligence methods in estimation of suspended sediment load (Case Study: Sistan River). Water and Soil Conservation.22., 41-60
Sherman, C. H., & Butler, J. L. (2007). Transducers and arrays for underwater sound (Vol. 4). New York: Springer.‏
Stoll, Q. M. (2004). Design of a real-time, optical sediment concentration sensor (Doctoral dissertation, Kansas State University).‏.
Stolojanu, V., & Prakash, A. (2001). Characterization of slurry systems by ultrasonic techniques. Chemical Engineering Journal, 84(3), 215-222.‏
Sung, C. C., Huang, Y. J., Lai, J. S., & Hwang, G. W. (2008). Ultrasonic measurement of suspended sediment concentrations: an experimental validation of the approach using kaolin suspensions and reservoir sediments under variable thermal conditions. Hydrological Processes: An International Journal, 22(16), 3149-3154.‏
Teixeira, L. C., de Paiva, J. B. D., da Silva Pereira, J. E., & de Moura Lisbôa, R. (2016). Relationship between turbidity and suspended sediment concentration from a small hydrographic basin in Santa Maria (Rio Grande do Sul, Brazil). International Journal of River Basin Management, 14(4), 393-399.
Wren, D. G., & Kuhnle, R. A. (2002, April). Surrogate techniques for suspended-sediment measurement. In Turbidity and other sediment surrogates workshop.‏
Zhang, Y. (2009). An optical sensor for in-stream monitoring of suspended sediment concentration (Doctoral dissertation, Kansas State University).‏
Zou, X. J., Ma, Z. M., Zhao, X. H., Hu, X. Y., & Tao, W. L. (2014). B-scan ultrasound imaging measurement of suspended sediment concentration and its vertical distribution. Measurement Science and Technology, 25(11), 115303.‏