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Assignment on of Estimating crop water requirements by ANN and meteorological variables

Paper's Detail

Category Engineering
Paper Type Assignment Writing
Reference Type APA
Words 2800

Introduction about water and estimating crop water requirements

The main constituents of the Earth hydrosphere are the water, which is the transparent, inorganic, odorless, and colorless chemical substance.  It is very significant for all the known forms of life, even though it is providing organic nutrients and calories. In the world economy, water plays a significant role. By the human, approximately 70% of the water is used, and it goes to agriculture. Mostly in the oceans and the sea, the water covers approximately 70% of the Earth surface. So water is the basic need of the crops, and proper supply of water to different crops according to their condition is very important to determine because water is the necessity of a life of all elements.

The crop water requirements are defined as “ The water depth in (mm) which is required to meet the consumptions of water by the Evapotranspiration’s () through the disease of the free crop by growing the large fields under the conditions of non-restricting oil, and it involves the fertility and soil water plus it obtaining the full production of potential below the growing environments. It is also very important to estimate the water requirement of every crop because different crops have different production processes, and they need water according to their required production processes. However, it is also important to understand what water requirement for crops is. Whereas the Evapotranspiration’s () is defined as the rate of Evapotranspiration’s () for the given crop is influenced through the growing stages of the conditions of environments as well as the management of the crops. And the rate of Evapotranspiration’s () in  ()   could be computed as;

Whereas = latent heat of vaporizations  in ; and  is the net balance of energy ; ;  is the mean air density ;  specific heat constant ;  the slope of saturation vapor ;  aerodynamic resistance (Pereira & et al, 2013).

The purpose of the paper is to demonstrate the feasibility of the Artificial Natural network (ANN), which is being applied for the estimation of the crop water requirements.  The crop water requirements are estimated by the use of the FAO model crop because it is a major crop in the various regions of the states.  It is also used the Penman-Monteith method in the below equation for determining the reference of  crop evapotranspiration  and also gives the matches values through the actual needs of the crop water and the use the data of worldwide (Kumari & et al, 2017). The evapotranspiration is calculated a s;

Method on Machine Learning

Ø  Deep learning of Method on Machine Learning

For the data analysis as well as image processing Deep learning is the constitution of modern techniques by the promising result of the great potential. In the different domains, deep learning has been successfully applied, and it is entered in the domain of agriculture. According to the investigations, there is 40 research that employed deep learning methods for different agriculture and the production of the challenges. Deep learning belongs to machine learning by the computational fields, and it is similar to the ANN. Thus the Deep learning is regarding the “deeper” neutral network, which also provides the hierarchical representations of data through the means of different convolutions. It also presents the larger learning capabilities as well as higher performance along with the precisions. The strong benefits of deep learning are feature learning, which has the automatic extractions of features from the raw data, then by these features form high levels of the hierarchy, which is being formed through the compositions of the lower levels of the features (Kamilaris, 2018).    

Ø  Long Short Term Memory (LSTM) neural network

LSTM is the method which is related to the memory cell as well as in what way it is related to the old context  by the new context . Whereas  is the memory cell . then the input gate of the discharge level of the data has the memory cell, which results is in the ( input gate ) then this input is used for the next sequences. The first step to determine the LSTM is the explicit formula of the LSTM-RNN is (Sudriani & et al, 2019);

Whereas the

There are some definitions for the above equations is shown in the below screenshots;

The ions of the nutrients solution are influenced through the accumulations by the time series factors like the environment greenhouse, and the supply of water, as well as drainage of water along with the plant growth. By using the LSTM methods, estimate the crop requirements by the ANN and the meteorological variables.

Ø  Gated recurrent units(GRU)

GRU is the newly developed variations for the LSTM units, where both of the variants have the RNN. Then by the empirical evidence, these methods have been proved through the effective and wide variety of machine learning tasks like natural language processing. The GRU –RNN also reduces the gating signals by the two forms of the LSTM models, then these two gates are also known as the update gate . (Dey & et al, 2017) Then the model of the GRU RNN is presented as;


Then these two gates presented as;

The application of deep learning is also predicting the water level for the estimation of the crop requirements. The forecasting model GRU is developed the base the  RNN to predict the water level from one to the four-time steps.

Paper 2

Reservoir inflow estimating by deep learning

Introduction of Method on Machine Learning

Importance of water

It can be noted that water is one of the most important elements on earth. It is also playing a vital role in the human body. The next thing is that there is no survival without water. We can easily survive without food for some weeks, but it will be extremely difficult to live for 2 days without water. This is because every tissue and system in the body requires water for proper functioning.  Moreover, water is also involved in carrying all nutrients all over the body and also providing oxygen to the whole brain. Furthermore, water is also absorbing some important minerals for our bodies. The next thing is that water can also flush out toxin material and waste. This element is also helping to maintain the temperature of the body. Additionally, water is extremely important for the soil to grow at a certain temperature. If there is no water, then it will become extremely difficult for the plant to perform the process of photosynthesis. Another thing is that water is also one of the most important lubricants for the movement of muscles. This shows that the value of water is extremely high. This is why the world is working on solving the issues related to saving this element for future use (Hans Lambers, 2008)

Importance of Forecast of inflow of Method on Machine Learning

In the flood season, there is a need for reservoir inflow forecasting that is extremely important for proper management of the reservoir. This type of forecasting is extremely important for such a season. This forecasting must be accurate, and on time, this is because it will allow the dam managers to release water on time to control the flood in the downstream areas. It can be seen that in the past few years, there are different methodologies and models are presented for the inflow forecasting with accurate and absolute results. There are also some defects in some models for forecasting. For that case, there is a need to overcome the weakness with perfection. Then a variation analog method is implemented that can be considered as analog-based forecasting. Due to this forecasting, it will become extremely easy to save different downstream areas (Amnatsan, Yoshikawa, & Kanae, 2018).

Importance of Deep learning

Before going brief in the subject, it is important to get useful information related to the importance of deep learning. It can be noted that its importance is extremely recognized by the average person. Besides this, according to the different researchers, its benefits are playing a game-changing role. The next thing is that in real-world applications, it is extremely important because it is capable of proving to be practical. This system is also involved in using unsupervised learning to increase its important benefits. In contrast, the machine learning program is involved in depending on various labels for the processing of data in an efficient way. This shows that it will require extra labor to process data, but on the other hand, deep learning can easily learn with the help of structureless data (Ian Goodfellow, 2016).

There are different methods for reservoir inflow estimating through deep learning. All of these methods are given below.

Bidirectional LSTM of Method on Machine Learning

It can be noted that it is one of the most advanced types of RNN. It is totally related to such information that flows from one part to the other and vice versa. The world bidirectional shows that the information moves in both directions. The next thing is that this method is useful for time series forecasting. It can be noted that there are different types of LSTM models, and all of these models can be used for solving the time series forecasting problem. But it can be seen that if there is a need for bidirectional LSTM, then it will solve the sequence prediction problem for the whole system. It is extremely important for the whole system because it will allow the model to learn useful information related to the input sequence. Then it will help the system in both directions. For the implementation of the model, it must be important that the input values must be arranged in series form. This model will check each and every value in both directions. Then by combining these values, it will become extremely easy to solve the time-variant problem in the reservoir inflow before flooding (Brownlee, 2020).

Gated recurrent unit of Method on Machine Learning

The gated recurrent unit is also one of the efficient methods of deep learning. For efficient use of the water resources for humans and also other organization, the reservoir is one of the most important engineering measures. For that case, there is a need for an effective operating plan for implementing its functions. Now for exploring its main application, there are different methods applied related to deep learning, and the Gated recurrent unit is one of them. This unit is implemented for the prediction of outflow through the reservoir. Now there is a need for such a forecasting model that is completely based on the recurrent neural network. This model will help to predict the level from four-time ahead.

 This will provide efficient progress for controlling the flow of water and refrain from floods. The next thing is that this method is also considered as a simple variation of the LSTM network. This is because both are involved in producing the same kind of results. But there is some similarity present between these models. This is the reason why this system is involved in solving the gradient problem in a proper way. This structure can be demonstrated in the figure given below

According to the researcher, it can be noted that there is no cell that contains separated memory like considered with LSTM cell. Moreover, the next thing is that there are only two gating layers are present in GRU. The one is related to update, and the next one is the reset gate. On the other hand, the reset gate is involved in determining the information from a previous memory. Also, the update gate works similarly like in the LSTM cell. Now the hidden state in the GRU is defined in the form of differential equations that are given below (Le, Ho, & Lee, 2019)

Long short-term memory of Method on Machine Learning

In the above section, there is proper information related to the importance of water. But if there is an excessive amount of water assembles at a given place, then it will result in flooding. From refraining from such an issue there is a need for forecasting for integrating the water resource in an effective way. For that case, there is a need flood forecasting network, and it can be done easily through the help of long short-term memory neural network. In that particular network, there is information related to daily discharge, and rainfall data can be used as the input. The next thing is that the characteristics data set may affect the working of the model.

The next most important thing about this model is that its predictive ability is extremely impressive.  According to the information, this method was presented by Schmidhuber and Hochreiter. Furthermore, this method is solving the problems that are present in the RNN. This can be done through the help of additional interaction. This means that LSTM is one of the upgraded types of RNN. Moreover, this model is organized and arranged in the form of a chain. But on the other hand, the repeating module has various structures. The next thing is that it also has four interacting layers that contain a unique method for communication. This system can be defined in the form figure

The next thing  is that a proper LSTM network consists of different memory blocks that are named as cells. All of these two streams are transferred to the  next cell, hidden, and state cell. For the main chain of data flow, state cell is used, and the next thing is that the data can be easily added and remove from the cell in a proper way. The proper LSTM network can be constructed by identifying the information that is not applied from the cell. In that network, the forget gate is playing an important role, and this can be defined as

For updating the new cell, this information can be updated in the new cell through

Simple recurrent neural network of Method on Machine Learning

It can be noted that for forecasting the flood, the simplest model was presented in the late 1980s. it is also known as the traditional method for forecasting. The structure of this method consists of different layers that include input, hidden, and output layers. All of these layers are extremely important for estimating the time. The next thing is that it also contains chain-like structures. Moreover, the next thing is that it also contains a feedback loop, and this loop is involved in accepting the sequence of inputs (Le, Ho, Giha, & Sungho, 2019).

This can be explained with the help of figure

References of Method on Machine Learning

Amnatsan, S., Yoshikawa, S., & Kanae, S. (2018). Improved Forecasting of Extreme Monthly Reservoir Inflow Using an Analogue-Based Forecasting Method: A Case Study of the Sirikit Dam in Thailand. Water 2018.

Brownlee, J. (2020). How to Develop LSTM Models for Time Series Forecasting. Retrieved from https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/

Dey, R., & et al. (2017). Gate-variants of Gated Recurrent Unit (GRU) neural networks. IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).

Hans Lambers, F. S. (2008). Plant Physiological Ecology. Springer Science & Business Media,.

Ian Goodfellow, Y. B. (2016). Deep Learning. MIT Press.

Kamilaris, A. (2018). Andreas Kamilaris Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 70–90.

Kumari, M., & et al. (2017). Crop Water Requirement, Water Productivity and Comparative Advantage of Crop Production in Different Regions of Uttar Pradesh, India. International Journal of Current Microbiology and Applied Sciences, 6(7), 2043-2052.

Le, X.-H., Ho, H. V., & Lee, G. (2019). APPLICATION OF GATED RECURRENT UNIT (GRU) NETWORK FOR FORECASTING RIVER WATER LEVELS AFFECTED BY TIDES. Proceedings of the 10th International Conference on Asian and Pacific Coasts.

Le, X.-H., Ho, H. V., Giha, L., & Sungho, J. (2019). Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. water 2019.

Pereira , L., & et al. (2013). Crop Water Requirements. Reference Module in Earth Systems and Environmental Sciences. Change History.

Sudriani, Y., & et al. (2019). Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia. IOP Conference Series: Earth and Environmental Science

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