SOIL MOISTURE MODELING USING ARTIFICIAL NEURAL NETWORKS

Transcription

1 Int'l Conf. Artificial Intelligence ICAI' SOIL MOISTURE MODELING USING ARTIFICIAL NEURAL NETWORKS Dr. Jayachander R. Gangasani Instructor, Department of Computer Science, Dr. Monday Mbila (Contact Author) Associate Professor, Department of Soil Science, Dr. Yinshu Wu Assistant Professor, Department of Mathematics, Dr. Jian Fu Professor, Department of Computer Science, ABSTRACT An artificial neural network (ANN) based algorithm was implemented, tested, and compared with regression models for soil moisture estimation. The research focused on developing soil moisture estimation capability by using an Artificial Neural Network (ANN) based model. The ANN model was calibrated (trained) and validated (tested) with soil moisture profiles measured from the Alabama Mesonet (ALMNet). The performance of the ANN model was evaluated by direct comparison between the soil moisture calculated from regression models, and Mesonet soil moisture measurements. Correlations were observed between the ANN estimates and Mesonet annual measurements. Less strong correlations were observed between the ANN estimates and Mesonet monthly measurements, while the least correlations were observed for the regression model. KEY WORDS: Alabama Mesonet, Artificial Neural Networks, Soil moisture prediction, and Regression models Type of the submission: Regular Research Paper

2 242 Int'l Conf. Artificial Intelligence ICAI'17 INTRODUCTION Soil moisture measurements in agricultural settings provide important information for drought early warning (Thober et al, 2015) as well as predicting floods (Zink et al., 2016). But soil moisture measurement methods are laborious, time consuming, expensive, and limited in application due to point-sampling in spite of heterogeneous distribution of moisture in soils. On the other hand, largescale satellite-based remote measurement techniques do not yet provide the required resolution to resolve spatiotemporal variability present at the scale needed. Therefore exploring the application of predictive models to soil moisture studies is imperative. The use of Artificial Neural Network for modeling soil moisture is a potential solution to this problem, and has already provided promising results (Pandey et al., 2010). Artificial Neural Networks (ANN) is a part of Computer Science that is analogous with artificial intelligence. ANNs are considered to be more relevant and useful than genetic algorithms and fuzzy logic system in dealing with soil and agricultural issues (Jain et al., 1996). Artificial neural networks is the type of network that sees the nodes as artificial neurons thus, these are called artificial neural networks (ANNs). An artificial neuron is a computational model inspired in the natural neurons. Natural neurons receive signals through synapses located on the dendrites or membrane of the neuron. When the signals received are strong enough (surpass a certain threshold), the neuron is activated and emits a signal though the axon. This signal might be sent to another synapse, and might activate other neurons (Carlos, 2003). Neural networks in general use machine learning based on the concept of self-adjustment of internal control parameters. Artificial neural networks are pliable mathematical structures that are capable of identifying complex non-linear relationships among input and output data sets. The principal differences between the various types of ANNs are arrangement of neurons and the many ways to assess the weights and functions for inputs and neurons (training). There are a variety of ANN architectures, such as multi-layer perceptron. The multilayer perceptron (MLP) neural network has been designed to function well for non-linear phenomena. A feed forward MLP network consists of a layer of input neurons and output layer with selected number of input and output neurons, respectively with one or more hidden layers in between the input and the output layer with some number of neurons on each (Melesse, 2006). The objectives of this research were: a) to develop an ANN model that estimates soil moisture content based on data from meteorological stations in Alabama such as precipitation(x0), atmospheric temperature(x1), solar radiation(x2), and wind speed (xp) as the input variables; and b) to evaluate the performance of the ANN model by comparison with other soil moisture models, and with measured soil moisture. DESIGN AND METHODOLOGY Location of the Study Area and Research Sites The study was carried out with data from the Alabama A&M University Main Campus Weather Station. This station is located about three hundred feet from the campus East gate at Latitude: 34 deg; 47 min N; and Longitude: 86 deg; 33 min W. The Station is recognized by the USDA Soil Climate Analysis Network (SCAN) Data & Products resources as: AAMU-JTG; Madison County.

3 Int'l Conf. Artificial Intelligence ICAI' The weather station has the following sensors installed for measuring the variables: soil hydra probes for measuring soil moisture and temperature; propeller-type anemometer for measuring wind speed and direction; pyranometer for solar radiation; tipping bucket rain gauge for measuring precipitation; thermometer for measuring air temperature; and humidity probe for measuring relative humidity. Selection of input variables We evaluated the correlation among the variables and soil moisture based on the following conceptual model: precipitation (x0), soil temperature (x1), solar radiation (x2), wind speed (xp) and soil moisture (y +k). Data Preprocessing After data download, three data preprocessing procedures were conducted to train the ANNs more efficiently for: a) solving the problem of missing data; b) Normalizing the data; and c) Randomizing the data. The missing data was replaced by the average of neighboring values during the same week. Normalization procedure before presenting the input data to the network was needed since mixing variables with large magnitudes and small magnitudes will confuse the learning algorithm on the importance of each variable. Mixing large and small magnitudes could force the procedure to finally reject the variable with the smaller magnitude (Tymvios et al., 2008). Building the Artificial Neural Network At this stage, building the program required specifying the number of hidden layers, neurons in each layer, transfer function in each layer, training function, weight/bias learning function, and performance function. Since this Program aimed to predict the seasonal/monthly soil moisture, the idea was to design the program into a multi-class classification problem using neural network. The Procedure: Training Data: Year Test Data: Year 2014 Training the Network During the training process, the weights of the variables were adjusted in order to make the actual outputs (predicted) close to the target (measured) outputs of the network. In this study, the 10- year data period (from 2004 to 2013), 7-year data period (from 2005 to 2011), and 5-year data period (from 2005 to 2009) from the ALABAMA MESONET were used for training. Programming the Actual Neural Network Model For this work, MATLAB (R2013b) was used to write script files for developing MLP and RBF ANN models and performance functions for calculating the model performance error statistics such as R2, RMSE and MBE. RESULTS AND DISCUSSION Soil Moisture Prediction Using Artificial Neural Network ANN learns to process data by arbitrary classification of the data and comparing the data with known actual classification of the data. The errors from the initial classification of the data is fed

4 244 Int'l Conf. Artificial Intelligence ICAI'17 back into the network, and used to modify the networks algorithm the second time around, and so on for many iterations. Table 1: Results of the ANN prediction Year Precipitation (inches) Soil Temp ( F) Radiation (watts) Wind Speed (mph) Soil Moisture (%) Class Class Class Class Class Class Class Class Class Class Class #? Table 2: Comparison of the Results of the Artificial Neural Network Predicted and Measured Monthly Soil Moisture Discussion Months Measured Predicted % Error Jan Feb Mar Apr May Jun Jul Aug Sep Oct Soil Moisture Prediction Using Multiple Regression Models Artificial Neural Network Model Prediction

5 Int'l Conf. Artificial Intelligence ICAI' The error difference between the actual soil moisture value and the predicted soil moisture from the monthly training data ranged for 3% to 19% with an average of 10% error for the year. Therefore the error for the monthly training data, while larger than that of the 10-year training data, was quite variable. Differences in error for the model are probably due to data size and gaps. This observation suggests that yearly soil moisture prediction may be more accurate, because the 10 days of bad data has greater impact in monthly soil moisture prediction than in yearly soil moisture predictions. For instance, 10 days of bad data due to missing data from bad receivers or sensors in one month will have 10/30 error proportion. This is equal to 33% approximately, meaning that 33% of the data is missing or wrong. However, for yearly soil moisture prediction, 10 days of missing or wrong data translates to 10/365 (2.7%). This means that there is only 2.7% error from missing or bad data in yearly soil moisture prediction compared to 33% error in monthly soil moisture prediction.

6 246 Int'l Conf. Artificial Intelligence ICAI'17 In general, regression models predicted soil moisture variations but those models explained a very low percentage of the soil moisture variability. Multiple regression models explained more of the soil moisture variability (46%) than any of the single variables that were investigated (Table 3). Solar radiation and soil temperature each individually explained about 40% of the variability in soil moisture changes. Wind speed and rainfall explained much less of the soil moisture variability with 20% and 3%, respectively. Artificial neural network predicted soil moisture variations and explained a high percentage (52%) of the soil moisture variability (Figure 1). Figure 1: Comparison of ANN Predicted and Measured Soil Moisture CONCLUSION The study was carried out with data from the Alabama Agricultural and Mechanical University Main Campus Weather Station where a meteorological station is installed to gather information on rain gauge, air temperature and relative humidity, soil moisture and temperature, wind speed and direction, and solar radiation. Data collected for ten years from the station was analyzed and used to develop and train the ANN and regression models to estimate soil moisture content. The impacts of solar radiation, rainfall, wind speed, and soil temperature were considered by using regression and the multiple regression models. Regression models predicted soil moisture variations, but those models explained a low percentage of the soil moisture variability. Multiple regression models explained more of the soil moisture variability than any of the single variables that were investigated (46%). Solar radiation and soil temperature each individually explained about 40% of the variability in soil moisture changes. Wind speed and rainfall explained much less of the soil moisture variability with 20% and 3%, respectively. The ANN model contained between 3% and 19% error difference between the ANN predicted, and the measured soil moisture value. Using the ANN explained 52% of the variability in measured soil moisture content. Overall, the ANN network, show better potential for predicting soil moisture changes using meteorological data generated by the weather stations.

7 Int'l Conf. Artificial Intelligence ICAI' REFERENCES Carlos Gershenson , Neural and Evolutionary Computing (cs.ne); Artificial Intelligence (cs.ai). Jain, A.K., Mao, J., Mohiuddin, K.M., Artificial neural networks: a tutorial. Comput. IEEE March, Melesse Assefa, and Xixi Wang Multitemporal Scale Hydrographic Prediction using Artificial Neural Networks, Journal of the American Water resources Association. Pandey, A, S. K. Jha, J. K. Srivastava, and R. Prasad Artificial neural network for the estimation of soil moisture and surface roughness. Russian Agricultural. Thober S., R. Kumar, J. Sheffield, J. Mai, D. Schafer, and L. Samaniego Seasonal Soil Moisture Drought Prediction over Europe Using the North American Multi-Model Ensemble (NMME). Journal of Hydrometeorology. 16: DOI: /JHM- D Tymvios, F., Michaelides, S. and C. Skouteli Estimation of Surface solar radiation with Artificial neural networks, In: Modeling Solar Radiation at the Earth Surface, Viorel Badescu, pp. ( ). Springer, ISBN , Germany. Zink, M, L. Samaniego, R. Kumar, S. Thober, J. Mai, D. Schafer, and Marx The German drought monitor. Environ. Res. Lett. 11: Pp 9.