Classification of Water Turbidity and Depth of Secchi Disk using Convolutional Neural Network

Among the important parameters in water quality, are the amount of turbidity and the depth of light penetration in water. One common way to determine water turbidity is to use a Secchi disk, but this method is time-consuming and expensive, so an alternative method should be considered. Deep learning methods can play an important role in this field. The purpose of this study was to classify water quality based on turbidity and Secchi disk depth using a convolutional neural network method implemented in a Python programming environment. For this purpose, a simulated reservoir was used in the laboratory and the turbidity was increased step by step by increasing the clay in the reservoir water. Simultaneously with measuring the depth of the Secchi disk and water turbidity, the samples were imaged. These images were given to the convolutional neural network together with the obtained data. The results showed that the convolutional neural network with 300 epochs, can estimate the water quality class with 95% accuracy and 93% kappa statistic, and it has only a 5% error rate

Estimation of daily soil temperature via data mining techniques in semi-arid climate conditions

This paper investigates the potential of data mining techniques to predict daily soil temperatures at 5-100 cm depths for agricultural purposes. Climatic and soil temperature data from Isfahan province located in central Iran with a semi-arid climate was used for the modeling process. A subtractive clustering approach was used to identify the structure of the Adaptive Neuro-Fuzzy Inference System (ANFIS), and the result of the proposed approach was compared with artificial neural networks (ANNs) and an M5 tree model. Result suggests an improved performance using the ANFIS approach in predicting soil temperatures at various soil depths except at 100 cm. The performance of the ANNs and M5 tree models were found to be similar. However, the M5 tree model provides a simple linear relation to predicting the soil temperature for the data ranges used in this study. Error analyses of the predicted values at various depths show that the estimation error tends to increase with the depth.Este artículo investiga el potencial de las técnicas de búsqueda y procesamiento de datos para pronosticar las temperaturas diarias del suelo a profundidades que van de los 5 a los 100 cm con propósitos agrícolas. Se utilizó la información climática y de temperatura del suelo de la provincia Ishafan, ubicada en el centro de Irán y de clima semiárido, para el proceso de modelamiento. Se usó un enfoque de agrupamiento sustractivo para identificar la estructura del Sistema de Inferencia Neuronal Difuso Adaptado (ANFIS, del inglés Adaptive Neuro-Fuzzy Inference System) y el resultado del acercamiento propuesto se comparó con redes artificiales neuronales (ANN) y el modelo tipo árbol M5. Los resultados sugieren un desempeño mejorado al usar el enfoque ANFIS en la predicción de las temperaturas del suelo en varios puntos de profundidad, excepto en los 100 cm. El desempeño de las redes artificiales neuronales y los modelos de árbol M5 fueron similares. Sin embargo, el modelo tipo árbol M5 provee una relación linear simple para predecir los rangos de datos de la temperatura del suelo utilizados en este estudio. Los análisis de error de los valores predichos a varias profundidades muestran que la estimación de error tiende a incrementarse con la profundidad

Comparative Analysis of Estimating Monthly Reference Evapotranspiration Using Kernel and Tree-Based Data Mining Models Versus Empirical Methods

Because direct measurement of evapotranspiration is costly and time-consuming, researchers have turned to the estimation of evapotranspiration via indirect approaches. The aim of this study is to investigate the capability of kernel-based, tree-based, bagging-based data-driven, and empirical models to estimate reference evapotranspiration. For this purpose, data related to meteorological parameters such as average temperature, hours of sunshine, maximum and minimum temperature, wind speed, precipitation, and relative humidity were collected over a period of 39 years. A correlation matrix, relief algorithm, and trial and error based on the author’s own experience were used to select input scenarios. The performance of these methods was evaluated using correlation coefficient (R2), root mean square error (RMSE), scattering index (SI), Nash Sutcliffe (NS), and Wilmot indexes (WI). Based on the results, scenario 13 includes maximum temperature and monthly time index based on the relief algorithm was selected as the best scenario, also on the other hand the random tree model with R=0.99, RMSE=0.04 mm/day, and SI=0.01 was selected as the superior method. Thus, the maximum temperature was defined as the efficient meteorological parameter for the reference evapotranspiration modeling

Use Of Fuzzy Logic For Risk/Benefit Assessment In Medical/Biological Cases

In recent decade safety of medical and biological products has been concerned in the light of benefit/risks and risk assessment. For new medical products and new drugs, unanticipated side effects that rise after consuming the new product is a dominant factor in decision making. The aim of this project is to design a fuzzy inference system for risk assessment of medical cases. Classical risk assessment in the crisp space precisely determines boundary sharply dissevers safe state from unsafe one. In contrary, fuzzy set shows smooth change from safe to unsafe state. It indicates that safety can be considered as a fuzzy issue because plant safety cannot be strictly classified as safe or unsafe, as inherent hazards always occur. Hafshejani M K, Sattari Naeini M, Mohammadsharifi A, Yahiapoor M. Use of Fuzzy Logic for Risk/Benefit Assessment in Medical/Biological Cases. Life Sci J 2012; 9(3): 2270-2273] (ISSN: 1097-8135). http://www.lifesciencesite.com. 40

Performance Analysis of Hydrological and Data Based models in Estimation of Suspended Sediment Rate

Data driven models are proposed as an alternative to hydrological methods in sediment estimation calculations. The aim of this study was to compare the performance and accuracy of hydrological and data-based methods in estimating the amount of suspended sediment. For this purpose, discharge and sediment data were collected in the period of 20 yr (2001-2011) and then the amount of suspended sediment of Bagh Kalayeh hydrometric station on Alamut River in Qazvin province was estimated. In this study hydrological methods including Smearing, FAO and Sediment Rating Curves versus data driven methods including Gene Expression Programming, Instance-Based Learning with parameter K and Linear Regression methods were used. The model performances were compared using two statistical methods of RRMSE and NS. The results showed that two techniques such as IBK model with evaluation criteria of (R = 0.94, RRMSE = 0.29 and NS = 0.24) and the GEP model with (R = 0.85, RRMSE = 0.59 and NS = 0.65) estimated suspended sediment in more accurate way than other studies methods. Thus, the superiority of data-driven methods in estimating the amount of suspended sediment in the study area was proved. Therefore, the use of data-based techniques as a competitor and alternative to hydrological methods to estimate the amount of suspended sediment in areas similar to the study area is recommended

Modeling pan evaporation using Gaussian Process Regression, K-Nearest Neighbors, Random Forest, and Support Vector Machines: Comparative analysis

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters

Investigating the Effect of Managing Scenarios of Flow Reduction and Increasing Irrigation Water Demand on Water Resources Allocation Using System Dynamics (Case Study: Zonouz Dam, Iran)

Meeting the healthy nutrition needs of the increasing population in the arid and semi-arid climates of the different regions of the world such as Iran has become very important for the agriculture ministry and water resources managers. In this study, the system dynamics approach was used in the Vensim software environment to allocate the water of the Zonouz dam reservoir for irrigation purposes in the northwest of Iran. For this purpose, the existing surface water resources in the basin and the amounts of agricultural water and environmental water demands were determined and a water allocation plan was developed. In the first stage of the study, it was found that if the existing water resources and demands will not change, the amount of water stored in the reservoir will provide approximately 91% of irrigation water demands and approximately 99% of environmental water needs. The model created in the study was found to be sensitive to reservoir inputs and irrigation water demands. Within the scope of this study, the impact of two different scenarios that may occur as a result of climate change and irrigation management in the operation of the reservoir was evaluated. The decrease in the amount of water entering the reservoir in the first scenario and the increase in irrigation water needs in the second scenario are assumed within the next 10 years. According to the simulation results of the first scenario, irrigation water demands will not be met sufficiently with the decrease in the amount of water to be stored in the reservoir due to the decrease in the amount of water entering the reservoir in the next 10 years. According to the results of the second scenario, in the next 10 years due to possible climate change or if the cultivated area increases due to some new agricultural policies; The amount of water stored in the reservoir will not meet the irrigation demands and there will be water shortage in the system. In this case, it is necessary to make changes in irrigation water management and use new irrigation systems to save water. Based on the findings of the study, it has been observed that the impact of all types of irrigation water policies can be successfully evaluated within the scope of the system dynamics approach

Estimation of monthly precipitation based on machine learning methods by using meteorological variables

Aims: The aim of this study is to estimate monthly precipitation by support vector regression and the nearest neighbourhood methods using meteorological variables data of Chabahar station. Methods and Results: Monthly precipitation was modelled by using two support vector regression and the nearest neighbourhood methods based on the two proposed input combinations. Conclusions: The results showed that the support vector regression method using normalized polynomial kernel function has higher accuracy and it has lower estimation error than the nearest neighbour method. Significance and Impact of the Study: Precipitation is one of the most important parts of the water cycle and plays an important role in assessing the climatic characteristics of each region. Modelling of monthly precipitation values for a variety of purposes, such as flood and sediment control, runoff, sediment, irrigation planning, and river basin management, is very important. The modelling of precipitation in each region requires the existence of accurately measured historical data such as humidity, temperature, wind speed, etc. Limitations such as insufficient knowledge of precipitation on spatial and temporal scales as well as the complexity of the relationship between precipitation-related climatic parameters make it impossible to estimate precipitation using conventional inaccurate and unreliable methods

Environmental Impact Assessment of the Sanitation Project of AjiChay River by Two Methods of Pastakia and Weighted Checklist

AjiChay is one of the most important rivers in the Urmia catchment area, which collects relatively large regional waters from the East Azerbaijan province and sends them to Lake Urmia. In recent years, in order to revive Lake Urmia, parts of the river have been rehabilitated and reorganized to transfer water to the main body of Lake Urmia. In this research, the effects of environmental damage on the improvement of the AjiChay River in physical, biological, social, economic and cultural environments were studied and evaluated using two methods of organizing and non-organizing of Pastakia matrix and weighted checklist (scaled). The study of the construction and operation of the project based on the results of the method of Pastakia showed that the positive effects of the project are 61% and its negative effects are 31.7%, and 7.3% of the works are not effective. The results of the checklist method also showed that the failure to implement the Aji Chai River Arrangement Plan would result in 13 positive effects versus 29 negative ones. Positive effects are limited to natural and physical environments and biological environments, but negative effects will occur in addition to those environments in the economic and social environments. In general, the results of both methods show that the most positive effects of this plan are related to socio-economic aspects. Ultimately, the environmental impact assessment shows that the Ajichay scheme has succeeded in achieving its primary goals