Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils
Abstract
:1. Introduction
2. Materials and Methods
2.1. Soil Samples
2.2. Artificial Neural Networks (ANNs)
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Wösten, J.H.M.; Lilly, A.; Nemes, A.; Le Bas, C. Development and use of a database of hydraulic properties of European soils. Geoderma 1999, 90, 169–185. [Google Scholar] [CrossRef]
- De Melo Moreira, T.; Pedrollo, O.C. Artificial neural networks for estimating soil water retention curve using fitted and measured data. Appl. Environ. Soil Sci. 2015, 2015, 535216. [Google Scholar] [CrossRef]
- Jana, R.B.; Mohanty, B.P.; Springer, E.P. Multiscale Pedotransfer Functions for Soil Water Retention. Vadose Zone J. 2007, 6, 868–878. [Google Scholar] [CrossRef]
- Zacharias, S.; Wessolek, G. Excluding Organic Matter Content from Pedotransfer Predictors of Soil Water Retention. Soil Sci. Soc. Am. J. 2007, 71, 43–50. [Google Scholar] [CrossRef]
- Van Genuchten, M.T. A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 1980, 44, 892–898. [Google Scholar] [CrossRef]
- Dexter, A.R.; Czyz, E.A.; Richard, G.; Reszkowska, A. A user-friendly water retention function that takes account of the textural and structural pore spaces in soil. Geoderma 2008, 143, 243–253. [Google Scholar] [CrossRef]
- Wosten, J.H.M.; van Genuchten, M.T. Using texture and other soil properties to predict the unsaturated soil hydraulic functions. Soil Sci. Soc. Am. J. 1988, 52, 1762–1770. [Google Scholar] [CrossRef]
- Schaap, M.G.; Bouten, W. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 1996, 32, 3033–3040. [Google Scholar] [CrossRef]
- Scheinost, A.C.; Sinowski, W.; Auerswald, K. Regionalization of soil water retention curves in a highly variable soilscape, I. Developing a new pedotransfer function. Geoderma 1997, 78, 129–143. [Google Scholar] [CrossRef]
- Minasny, B.; McBratney, A.B.; Bristow, K.I. Comparison of different approaches to the development of pedotransfer functions for water retention curves. Geoderma 1999, 93, 225–253. [Google Scholar] [CrossRef]
- Wösten, J.H.M.; Pachepsky, Y.A.; Rawls, W.J. Pedotransfer functions: Bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol. 2001, 251, 123–150. [Google Scholar] [CrossRef]
- Brooks, R.H.; Corey, A.T. Hydraulic properties of porous media and their relation to drainage design. Trans. ASAE 1964, 7, 0026–0028. [Google Scholar]
- Campbell, G.S. A simple method for determining unsaturated hydraulic conductivity from moisture retention data. Soil Sci. 1974, 177, 311–314. [Google Scholar] [CrossRef]
- Wang, G.; Zhanga, Y.; Yu, N. Prediction of soil water retention and available water of sandy soils using pedotransfer functions. Procedia Eng. 2012, 37, 49–53. [Google Scholar] [CrossRef]
- Pachepsky, Y.A.; Rawls, W.J. Accuracy and reliability of pedotransfer functions as affected by grouping soils. Soil Sci. Soc. Am. J. 1999, 63, 1748–1757. [Google Scholar] [CrossRef]
- Haghverdi, A.; Öztürk, H.S.; Cornelis, W.M. Revisiting the pseudo continuous pedotransfer function concept: Impact of data quality and data mining method. Geoderma 2014, 226, 31–38. [Google Scholar] [CrossRef]
- Mukhlisin, M.; El-Shafie, A.; Taha, M.R. Regularized versus non-regularized neural network model for prediction of saturated soil-water content on weathered granite soil formation. Neural Comput. Appl. 2012, 21, 543–553. [Google Scholar] [CrossRef]
- Patil, N.G.; Pal, D.K.; Mandal, C.; Mandal, D.K. Soil water retention characteristics of vertisols and pedotransfer functions based on nearest neighbour and neural networks approaches to estimate AWC. J. Irrig. Drain. Eng. 2012, 138, 177–184. [Google Scholar] [CrossRef]
- Minasny, B.; Hartemink, A.E. Predicting soil properties in the tropics. Earth-Sci. Rev. 2011, 106, 52–62. [Google Scholar] [CrossRef]
- Patil, N.G.; Singh, S.K. Pedotransfer functions for estimating soil hydraulic properties: A review. Pedosphere 2016, 26, 417–430. [Google Scholar] [CrossRef]
- Brooks, R.H.; Corey, A.T. Properties of porous media affecting fluid flow. J. Irrig. Drain. Div. 1966, 92, 61–90. [Google Scholar]
- Aiello, R.; Bagarello, V.; Barbagallo, S.; Consoli, S.; Di Prima, S.; Giordano, G.; Iovino, M. An assessment of the Beerkan method for determining the hydraulic properties of a sandy loam soil. Geoderma 2014, 235–236, 300–307. [Google Scholar] [CrossRef]
- Antinoro, C.; Bagarello, V.; Ferro, V.; Giordano, G.; Iovino, M. A simplified approach to estimate water retention for Sicilian soils by the Arya–Paris model. Geoderma 2014, 213, 226–234. [Google Scholar] [CrossRef]
- Bagarello, V.; Iovino, M. Testing the BEST procedure to estimate the soil water retention curve. Geoderma 2012, 187, 67–76. [Google Scholar] [CrossRef]
- Gee, G.W.; Bauder, J.W. Particle-size analysis1. In Methods of Soil Analysis: Part 1—Physical and Mineralogical Methods, (Methodsofsoilan1); American Society of Agronomy-Soil Science Society of America: Madison, WI, USA, 1986; pp. 383–411. [Google Scholar]
- Shirazi, M.A.; Boersma, L. A Unifying Quantitative Analysis of Soil Texture 1. Soil Sci. Soc. Am. J. 1984, 48, 142–147. [Google Scholar] [CrossRef]
- Nelson, D.W.; Sommers, L.E. Total carbon, organic carbon, and organic matter. In Methods of Soil Analysis Part 3—Chemical Methods, (Methodsofsoilan3); American Society of Agronomy-Soil Science Society of America: Madison, WI, USA, 1996; pp. 961–1010. [Google Scholar]
- Van Genuchten, M.V.; Leij, F.J.; Yates, S.R. The RETC Code for Quantifying the Hydraulic Functions of Unsaturated Soils; Research Report n. EPA/600/2-91/065; U.S. Salinity Laboratory, USDA-ARS: Riverside, CA, USA, 1991; 93p.
- D’Emilio, A.; Mazzarella, R.; Porto, S.M.C.; Cascone, G. Neural networks for predicting greenhouse thermal regimes during soil solarization. Trans. ASABE 2012, 55, 1093–1103. [Google Scholar] [CrossRef]
- Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19, 716–723. [Google Scholar] [CrossRef]
- Panchal, G.; Ganatra, A.; Kosta, Y.P.; Panchal, D. Searching most efficient neural network architecture using Akaike’s information criterion (AIC). Int. J. Comput. Appl. 2010, 1, 41–44. [Google Scholar] [CrossRef]
- Chang, C.H.; Wu, S.J.; Hsu, C.T.; Shen, J.C.; Lien, H.C. An evaluation framework for identifying the optimal raingauge network based on spatiotemporal variation in quantitative precipitation estimation. Hydrol. Res. 2017, 48, 77–98. [Google Scholar] [CrossRef]
- Laio, F.; Di Baldassarre, G.; Montanari, A. Model selection techniques for the frequency analysis of hydrological extremes. Water Res. Res. 2009, 45, 1–11. [Google Scholar] [CrossRef]
- Rossel, R.V.; Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 2010, 158, 46–54. [Google Scholar] [CrossRef]
- Minasny, B.; McBratney, A.B. The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Sci. Soc. Am. J. 2002, 66, 352–361. [Google Scholar] [CrossRef]
- Jain, S.K.; Singh, V.P.; van Genuchten, M.T. Analysis of soil water retention data using artificial neural networks. J. Hydrol. Eng. 2004, 9, 415–420. [Google Scholar] [CrossRef]
- Merdun, H.; Cinar, O.; Meral, R.; Apan, M. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Tillage Res. 2006, 90, 108–116. [Google Scholar] [CrossRef]
- Pachepsky, Y.; Schaap, M.G. Data mining and exploration techniques. Dev. Soil Sci. 2004, 30, 21–32. [Google Scholar]
- Haghverdi, A.; Cornelis, W.M.; Ghahraman, B. A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data. J. Hydrol. 2012, 442, 46–54. [Google Scholar] [CrossRef]
Site | n | Clay (%) | Silt (%) | Sand (%) | dg (mm) | OC (g·kg−1) | ρb (Mg·m−3) | φ |
---|---|---|---|---|---|---|---|---|
Palermo | 3 | 18.0 (±1.7) | 28.6 (±2.5) | 53.4 (±3.5) | 0.10 (±0.02) | 3.4 (±1.19) | 1.1.2 (±0.04) | 0.58 (±0.01) |
Bulgherano | 32 | 16.4 (±3.8) | 27.1 (±3.9) | 56.5 (±4.1) | 0.13 (±0.03) | 2.1 (±0.52) | 1.25 (±0.10) | 0.53 (±0.04) |
Caccamo | 1 | 7.4 | 18.0 | 74.6 | 0.02 | 1.51 | 1.25 | 0.53 |
Castelvetrano | 5 | 35.3 (±7.9) | 24.0 (±4.6) | 40.7 (±4.0) | 0.04 (±0.02) | 2.0 (±0.50) | 1.31 (±0.07) | 0.51 (±0.03) |
Comiso | 1 | 28.2 | 46.5 | 25.3 | 0.03 | 2.8 | 1.09 | 0.59 |
Corleone | 6 | 41.2 (±19.1) | 32.4 (±2.5) | 26.4 (±21.1) | 0.04 (±0.06) | 2.2 (±0.67) | 1.07 (±0.17) | 0.60 (±0.06) |
Etna | 1 | 0.5 | 9.7 | 89.9 | 0.70 | 1.86 | 1.37 | 0.48 |
Dirillo | 85 | 20.6 (±11.1) | 33.6 (±15.9) | 45.7 (±25.7) | 0.15 (±0.17) | 1.1 (±0.73) | 1.40 (±0.16) | 0.47 (±0.06) |
Menfi | 82 | (±11.4) | (±10.1) | 47.0 (±18.4) | 0.12 (±0.10) | 1.5 (±0.21) | 1.26 (±0.14) | 0.52 (±0.05) |
Mineo | 2 | 21.8 | (±2.3) | 32.5 (±6.6) | 0.04 (±0.02) | 1.5 (±0.66) | 1.26 (±0.03) | 0.52 (±0.01) |
Monreale | 1 | 5.4 | 22.7 | 71.9 | 0.31 | 0.3 | 1.26 | 0.53 |
Palazzelli | 32 | 10.5 (±3.8) | (±5.8) | 69.7 (±7.6) | 0.26 (±0.09) | 1.2 (±0.27) | 1.25 (±0.08) | 0.53 (±0.03) |
Pettineo | 1 | 24.9 | 34.2 | 40.9 | 0.05 | 4.6 | 1.14 | 0.57 |
Pollina | 2 | 24.8 (±4.17) | (±8.9) | 33.8 (±13.1) | 0.04 (±0.03) | 3.6 (±0.18) | 1.15 (±0.02) | 0.57 (±0.01) |
Ramacca | 2 | 29.7 (±4.4) | (±2.7) | 35.5 (±7.1) | 0.04 (±0.01) | 0.7 (±0.46) | 1.32 (±0.00) | 0.50 (±0.00) |
Rapitalà | 2 | 28.3 (±11.7) | (±11.4) | 34.8 (±23.1) | 0.05 (±0.05) | 1.6 (±0.22) | 1.30 (±0.10) | 0.51 (±0.04) |
Resuttano | 6 | 51.1 (±17.5) | (±13.1) | 7.1 (±5.9) | 0.01 (±0.01) | 1.6 (±1.17) | 1.30 (±0.15) | 0.51 (±0.06) |
Santa Ninfa | 52 | 20.5 (±18.4) | (±16.0) | 21.6 (±9.9) | 0.04 (±0.02) | 3.4 (±1.38) | 1.13 (±0.09) | 0.57 (±0.03) |
San Michele | 40 | 46.7 (±6.6) | (±6.2) | 36.3 (±9.0) | 0.02 (±0.01) | 2.5 (±0.49) | 1.27 (±0.08) | 0.52 (±0.03) |
Sparacia | 2 | 17.2 (±7.8) | (±2.0) | 62.3 (±5.7) | 0.15 (±0.07) | 0.5 (±0.0) | 1.40 (±0.11) | 0.47 (±0.04) |
Ventimiglia | 1 | 36.3 | 29.8 | 33.9 | 0.03 | 1.3 | 1.25 | 0.53 |
All | 359 | 23.9 | 31.3 | 44.8 | 0.11 | 2 | 1.25 | 0.53 |
Network | Input Data 1 |
---|---|
ANN1 | Cl, Si, dg, φ |
ANN2 | Cl, Sa, ρb |
ANN3 | Cl, Sa, OC |
ANN4 | Cl, Sa, Si, OC |
ANN5 | Cl, Sa, OC, ρb |
ANNs | Performance | θr | θs | α | N |
---|---|---|---|---|---|
ANN1 | RMSE | 0.0679 | 0.0660 | 0.0973 | 0.2135 |
NRMSE | 0.2751 | 0.1135 | 0.0692 | 0.1277 | |
MAE | 0.0571 | 0.0523 | 0.0596 | 0.1607 | |
NMAE | 0.2314 | 0.0899 | 0.0424 | 0.0962 | |
Min AE | 0.0039 | 0.0030 | 0.0021 | 0.0012 | |
Max AE | 0.1710 | 0.1984 | 0.4797 | 0.7833 | |
r | 0.2762 | 0.6612 | 0.2926 | 0.6944 | |
ANN2 | RMSE | 0.0671 | 0.0677 | 0.0957 | 0.2113 |
NRMSE | 0.2718 | 0.1164 | 0.0681 | 0.1264 | |
MAE | 0.0571 | 0.0533 | 0.0582 | 0.1581 | |
NMAE | 0.2314 | 0.0917 | 0.0414 | 0.0946 | |
Min AE | 0.0047 | 0.0004 | 0.0007 | 0.0016 | |
Max AE | 0.1673 | 0.1953 | 0.4817 | 0.7828 | |
r | 0.2927 | 0.6402 | 0.2766 | 0.7003 | |
ANN3 | RMSE | 0.0650 | 0.0701 | 0.0967 | 0.2238 |
NRMSE | 0.2635 | 0.1206 | 0.0688 | 0.1339 | |
MAE | 0.0555 | 0.0562 | 0.0574 | 0.1634 | |
NMAE | 0.2248 | 0.0966 | 0.0408 | 0.0978 | |
Min AE | 0.0024 | 10−5 | 0.0015 | 0.0019 | |
Max AE | 0.1845 | 0.2339 | 0.4873 | 0.8500 | |
r | 0.3789 | 0.6109 | 0.1586 | 0.6574 | |
ANN4 | RMSE | 0.0610 | 0.0681 | 0.0972 | 0.2197 |
NRMSE | 0.2470 | 0.1172 | 0.0691 | 0.1315 | |
MAE | 0.0500 | 0.0554 | 0.0556 | 0.1560 | |
NMAE | 0.2025 | 0.0952 | 0.0396 | 0.0933 | |
Min AE | 0.0018 | 0.0011 | 0.0003 | 0.0029 | |
Max AE | 0.1764 | 0.2149 | 0.4908 | 0.8782 | |
r | 0.5004 | 0.6400 | 0.1673 | 0.6734 | |
ANN5 | RMSE | 0.0657 | 0.0641 | 0.1001 | 0.2120 |
NRMSE | 0.2663 | 0.1102 | 0.0712 | 0.1268 | |
MAE | 0.0552 | 0.0515 | 0.0611 | 0.1567 | |
NMAE | 0.2236 | 0.0885 | 0.0434 | 0.0937 | |
Min AE | 0.0013 | 0.0013 | 0.0003 | 0.0004 | |
Max AE | 0.1788 | 0.1856 | 0.4666 | 0.7842 | |
r | 0.3764 | 0.6887 | 0.2318 | 0.6992 |
Network | MAE | RMSE | r2 |
---|---|---|---|
ANN1 | 0.030 | 0.074 | 0.75 |
ANN2 | 0.032 | 0.076 | 0.74 |
ANN3 | 0.032 | 0.089 | 0.65 |
ANN4 | 0.026 | 0.069 | 0.79 |
ANN5 | 0.016 | 0.074 | 0.72 |
Network | vG Parameters | Water Retention Curve | ||
---|---|---|---|---|
RSS | AIC | RSS | AIC | |
ANN1 | 5.75 | −468.3 | 5.14 | −2887.8 |
ANN2 | 5.74 | −472.4 | 5.48 | −3095.5 |
ANN3 | 6.23 | −464.9 | 7.43 | −2811.6 |
ANN4 | 5.88 | −466.4 | 4.56 | −2999.4 |
ANN5 | 5.63 | −470.2 | 5.23 | −2871.8 |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
D’Emilio, A.; Aiello, R.; Consoli, S.; Vanella, D.; Iovino, M. Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils. Water 2018, 10, 1431. https://doi.org/10.3390/w10101431
D’Emilio A, Aiello R, Consoli S, Vanella D, Iovino M. Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils. Water. 2018; 10(10):1431. https://doi.org/10.3390/w10101431
Chicago/Turabian StyleD’Emilio, Alessandro, Rosa Aiello, Simona Consoli, Daniela Vanella, and Massimo Iovino. 2018. "Artificial Neural Networks for Predicting the Water Retention Curve of Sicilian Agricultural Soils" Water 10, no. 10: 1431. https://doi.org/10.3390/w10101431