Agricultural Meteorology
Nazila Shamloo; Mohammad Taghi Sattari; Khalil Valizadeh Kamran; Halit Apaydin
Abstract
Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information ...
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Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.
Materials and Methods
In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.
Results and Discussion
This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.
Conclusion
In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.
Irrigation
M. Babaei; M.T. Sattari
Abstract
IntroductionDevelopment of reservoirs helps to meet food and energy needs by supplying water for agriculture and hydropower plants. Efficient management of water resources is important and vital to overcome the problems of water leakage and meet agricultural, industrial and drinking needs. Each of these ...
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IntroductionDevelopment of reservoirs helps to meet food and energy needs by supplying water for agriculture and hydropower plants. Efficient management of water resources is important and vital to overcome the problems of water leakage and meet agricultural, industrial and drinking needs. Each of these requirements creates limitations in the way the reservoir is operated, which requires accurate information on the changes in the reservoir storage and other influential components during the operation period. In order to manage and plan water resources at country scale, using reservoir simulation models as a suitable tool in simulating processes related to dams, such as the operation of water reservoirs, will be very effective. Reservoir simulation models such as the HEC-ResSim model provide the opportunity to simulate the natural and hydrological processes related to the water resources system and the relationships between the supply and demand sectors by implementing a schematic structure of a real reservoir. Two scenarios of water savings of 20 and 30 percent were used in the current investigation. Additionally, using this method, the objectives of water resource management can be assessed.Materials and MethodsIn the present study, the use of the Latian reservoir in real conditions was simulated using the HEC-ResSim model. The simulation was carried out according to the river's inflow from 1968 to 2018, downstream water needs, energy production capacity by turbines, physical characteristics and reservoir building. The implementation of the HEC-ResSim model is summarized in three steps. The Watershed Setup module is used to introduce the general outline of the watershed. In this module, the shape and geographical location of the basin and related elements such as rivers, reservoirs, hydrometric stations and other projects in it should be specified. The Reservoir Network module is used to introduce the desired reservoir network and to enter the physical characteristics and how to use them. The Simulation module is designed to introduce the simulation period and display the model outputs. In this module, the simulation time and period and the operation pattern should be determined.Results and DiscussionAccording to the results obtained from the reservoir simulation model, the average storage capacity of Latian dam for the simulation period was estimated to be 41 million cubic meters, which shows a significant drop of 49% compared to the normal level (83 million cubic meters). Additionally, for the same period, it was estimated that the average discharge was equivalent to 5.4 cubic meters per second and the average inflow to the reservoir of the Latian dam was equal to 5.7 cubic meters per second. This is in contrast to the period's average demand, which for the area downstream of the Latian Dam is 12.1 cubic meters. The findings indicate that the reservoir of the dam frequently, and particularly at the conclusion of the simulation period, is unable to satisfy the needs of the downstream. Additionally, according to the findings of the current study, the Latian dam power plant's (Kalan) average annual hydro-electric energy production was projected to be 68,000 MWh, and the results show that in accordance with the policy of operating the Latian dam in the majority ofthe years, the Kalan power plant is able to supply the electricity required in the study area. According to the results, the average reservoir volume of Latian dam for the entire period in the first and second scenario was estimated to be 49 and 63 million cubic meters, respectively. Also, by applying the first and second water saving scenarios, the Latian dam reservoir will be able to generate 66,000 and 63,000 MWh of energy annually.ConclusionIn this study, the functioning and operation of the Latian dam reservoir was used by applying the Hec-ResSim reservoir simulation model. After entering data such as the elevation and length of the dam, surface-volume-elevation curve, evaporation from the surface of the reservoir, elevation and uncontrolled outlet coefficient, dam storage areas, rule curve, were simulated by the model. In the present study, the values of inactive volume and conservation volume of Latian Dam were estimated as 28 and 83 million cubic meters, respectively. The average water release of Latian dam for the first and second 25 years of operation was equal to 6.1 and 3.7 cubic meters per second, respectively, which met 50 and 32% of the downstream demand on average. The results indicate that the success rate of Latian dam in supplying drinking, industry and downstream environment for the period of operation is 42%. Also, 16 years out of 50 years of operation, Kalan hydropower plant has fully met 100% of the needs. On average, the large power plant is able to provide 80% of the energy needs of the study area for the entire simulation period.
Irrigation
M.T. Sattari; S. Javidan
Abstract
Introduction
Surface and underground waters are one of the world's most important problems and environmental concerns. In the last few decades, due to the rapid growth of the population, the water needs have increased, followed by the input load to the water. In order to classify the quality of underground ...
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Introduction
Surface and underground waters are one of the world's most important problems and environmental concerns. In the last few decades, due to the rapid growth of the population, the water needs have increased, followed by the input load to the water. In order to classify the quality of underground water and water level according to the type of consumption, there are many methods, one of the most used methods is the use of quality indicators. Considering the facilities available in water quality monitoring stations and the need to save time and money, using alternative methods of modern data mining methods can be good for predicting and classifying water quality. The process of water extraction for domestic use, agricultural production, mineral industrial production, electricity production, and ester methods can lead to the deterioration of water quality and quantity, which affects the aquatic ecosystem, that is, the set of organisms that live and interact. Therefore, it is very important to evaluate the quality of surface water in water-environmental management and in monitoring the concentration of pollutants in rivers. The aim of the current research was to estimate the numerical values of the drinking water quality index (WQI) using the tree method and investigate the effect of wavelet transformation, the Bagging method, and principal component analysis.
Materials and Methods
In this research, to calculate the WQI index from the quality parameters of the Bagh Kalaye hydrometric station including total hardness (TH), alkalinity (pH), electrical conductivity (EC), total dissolved solids (TDS), calcium (Ca), sodium (Na), Magnesium (Mg), potassium (K), chlorine (Cl), carbonate (CO3), bicarbonate (HCO3) and sulfate (SO4) were used in the statistical period of 23 years (1998-2020). Quantitative values calculated with the WQI index were considered as target outputs. By using the relief and correlation method, the types of input combinations were determined. The random tree method was used to estimate the numerical values of the WQI index. Then, the capability of the combined approach of wavelet, principal component analysis, and Bagging method with random tree base algorithm was evaluated. To compare the values obtained from the data mining methods with the values calculated from the WQI index, the evaluation criteria of correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and modified Wilmot coefficient (Dr) were used.
Results and Discussion
The use of the wavelet transform method and the Bagging method has improved the modeling results. Considering that the Bagging classification method with the random tree base algorithm is a combination of the results of several random trees, so using this method has increased the accuracy of the RT model. So, in general, it was concluded that the use of wavelet transformation and classification methods increases accuracy and reduces errors. The best scenario with the highest accuracy and the lowest error was related to scenario 10 of the W-B-RT model with Total Hardness, Electrical Conductivity, Total Dissolved Solid, Sulphate, Calcium, Bicarbonate, Magnesium, Chlorine, Sodium, and potassium parameters. The results showed that the effect impact of pH in estimating the numerical value of the WQI index is considered lower than other parameters. When the principal component analysis method was used, by reducing the value of the eigenvalue from F1 to F12, the value of the factor also decreased; As a result,so F1, F2, and F3 factors were selected as the basic components. Considering 3 main factors, modeling was done employed and R=0.98, RMSE=2.17, MAE=1.52, and Dr=0.97 were obtained. In general, the results showed that the PCA method, despite reducing the dimension of the input vectors and simplifying it, can improve the accuracy and speed of the model and is introduced as the best method for estimating the numerical value of the WQI index.
Conclusion
The results obtained from the present research showed that the use of wavelet transform, Bagging and PCA methods had a positive effect on improving the results and increasing higherthe accuracy. In estimating the numerical values of WQI index, PCA-B-RT method considering 3 main factors, with correlation coefficient equal to 0.98, root mean square error equal to 2.17, average absolute value error equal to 1.52 and tThe modified Wilmot coefficient equal to 0.97 had the highest accuracy. Considering that all the methods used in the estimation of quantitative values had acceptable accuracy, therefore, in case of lack of data and lack of access to all chemical parameters, it is possible to obtain appropriate and acceptable results by using a limited number of parameters and data mining methods achieved.
Agricultural Meteorology
S. Javidan; M.T. Sattari; Sh. Mohsenzadeh
Abstract
IntroductionPrecipitation is one of the most important components of water cycle. Accurate precipitation measurement is essential for flood forecasting and control, drought analysis, runoff modeling, sediment control and management, watershed management, agricultural irrigation planning, and water quality ...
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IntroductionPrecipitation is one of the most important components of water cycle. Accurate precipitation measurement is essential for flood forecasting and control, drought analysis, runoff modeling, sediment control and management, watershed management, agricultural irrigation planning, and water quality studies. Determining the correct amount of precipitation in cities and rural areas is also important for managing floods. The precipitation process is completely non-linear and involves randomness in terms of time and space. Therefore, it is not easy to explain that with simple linear models due to various climatic factors and may contain major errors. Therefore, various methods and models have been proposed to evaluate, and predict precipitation. This study aimed to estimate the daily precipitation of Tabriz based on hybridized tree-based and Bagging methods by using neighboring stations.Materials and MethodsIn the present study, the rainfall data of adjacent stations in Urmia lake basin (Sahand, Sarab, Urmia, Maragheh and Mahabad) were employed in 1986-2021 to estimate the daily rainfall in Tabriz. About 70% of data were considered for calibration and 30% of data were applied for validation. Using the correlation matrix and Relief algorithm, various input components were identified. Modeling was performed using tree-based data mining methods including M5P, RT and REPT and Bagging method. The daily precipitations of Tabriz was decomposed into their components by seasonal-trend analysis method. Its components, including trend, seasonal and residual, were used in different input scenarios to investigate the effect of these components on improving the modeling results. To evaluate the modeling performance, the indices of correlation coefficient, Root Mean Square Error, Nash-Sutcliffe Efficiency and modified Wilmot coefficient were applied.Results and DiscussionRT and REPT methods increased the accuracy of the model and decreased its error when they were used as the basic algorithm of the Bagging method. This was not the case with the M5P method, as the results were slightly weaker. It was also observed that Tabriz rainfall is largely influenced by Sahand rainfall, as the most models gave reliable estimates by using the rainfall data for Sahand station. This can be explained by the high correlation between Tabriz rainfall and Sahand. The results showed that the first scenario (Sahand) for M5P, RT, REPT and B-M5P method, the fifth scenario (Sahand, Sarab, Urmia, Maragheh and Mahabad) for the B-RT method, and the fourth scenario (Sahand, Sarab, Urmia and Mahabad) for the B-REPT method were the best scenarios. The best performance was found for the scenario 1 of the M5P decision tree model, followed by the Bagging method with the M5P base algorithm. In general, it was concluded that application of the Bagging method produced reliable results. Modeling without considering the decomposition components was compared with modeling with decomposition components. Adding seasonal, trend and residual components to the modeling input combinations significantly improved the accuracy of the results. Application of Bagging method in most cases also increased the modeling accuracy. The first scenario (Sahand and residual) for M5P and B-M5P methods, the tenth scenario (residual, trend, seasonal, Sahand and Sarab) for RT, REPT and B-REPT methods, and the eighth scenario (residual, trend and Sahand) for B-RT method were selected as the best scenarios. As a result, among the stations, Sahand, due to proximity and high correlation, and Sarab, due to greater correlation, had a great impact on precipitation in Tabriz. In general, the Bagging method with the basic M5P algorithm (B-M5P) was best suited in the first scenario. Thus, adding precipitation analysis components and using the Bagging method improve the modeling results with tree-based data mining methods.ConclusionOur results showed that Bagging method provided acceptable results in most cases. In the first case, the first scenario of M5P method including Sahand precipitation data was selected as the superior method and scenario. As a result, Sahand was the most effective station in estimating Tabriz rainfall with the highest correlation and the shortest distance from Tabriz. In the second case, with the decomposition components, the accuracy of the results increased significantly. The Bagging method with the basic M5P algorithm, the parameters of Sahand precipitation and the residual of Tabriz precipitation was considered as the best modeling algorithm. It can be concluded that using Bagging method and decomposition components with the closest station to the studied station results in the highest accuracy. Therefore, Bagging models with tree-based algorithm can be considered as simple and widely used methods.
