Forecasting Soil Moisture Based on Evaluation of Time Series Analysis

Singh, Sukhwinder; Kaur, Sanmukh; Kumar, Parteek

doi:10.1007/978-981-15-0313-9_11

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…In the field of agriculture, smart farming and precision agriculture are major technological advancements that incorporate cloud computing and machine learning algorithms [133]. In this context, Singh et al proposed a model for forecasting moisture in soil by using time series analysis [134]. Data generated from various sources like wind direction predictors, GPS-enabled tractors, and crop sensors are used to elevate agricultural operations.…”

Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

107

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

107

View full text Add to dashboard Cite

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“…Soil moisture prediction is one of the most important tasks for an automatic irrigation system. Many researchers contributed various methodologies and algorithms for this task ( Adeyemi et al, 2018 ; Sinwar et al, 2020 ; Singh, Kaur & Kumar, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage

Bhoi¹,

Nayak²,

Bhoi

et al. 2021

PeerJ Computer Science

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In the traditional irrigation process, a huge amount of water consumption is required which leads to water wastage. To reduce the wasting of water for this tedious task, an intelligent irrigation system is urgently needed. The era of machine learning (ML) and the Internet of Things (IoT) brings it is a great advantage of building an intelligent system that performs this task automatically with minimal human effort. In this study, an IoT enabled ML-trained recommendation system is proposed for efficient water usage with the nominal intervention of farmers. IoT devices are deployed in the crop field to precisely collect the ground and environmental details. The gathered data are forwarded and stored in a cloud-based server, which applies ML approaches to analyze data and suggest irrigation to the farmer. To make the system robust and adaptive, an inbuilt feedback mechanism is added to this recommendation system. The experimentation, reveals that the proposed system performs quite well on our own collected dataset and National Institute of Technology (NIT) Raipur crop dataset.

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“…Wang et al (2023) studied ARIMA and Back Propagation neural network model and found that a combination of the two gives superior forecasting accuracy than individual models [21]. Singh et al (2020) used LSTM for regional soil moisture forecasting based on previous history for 5-25 cm soil depth [22]. A hybrid CNN-GRU model, a combination of CNN (Convolutional Neural Network) and GRU, was developed by Yu et al (2021) to predict soil moisture in the corn root zone.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data

Basir,

Noel,

Buckmaster

et al. 2024

Agriculture

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Subsurface soil moisture is a primary determinant for root development and nutrient transportation in the soil and affects the tractability of agricultural vehicles. A statistical forecasting model, Vector AutoRegression (VAR), and a Long Short-Term Memory network (LSTM) were developed to forecast the subsurface soil moisture at a 20 cm depth using 9 years of historical weather data and subsurface soil moisture data from Fort Wayne, Indiana, USA. A time series analysis showed that the weather data and soil moisture have a stationary seasonal tendency and demonstrated that soil moisture can be forecasted from weather data. The VAR model estimates volumetric soil moisture of one-day ahead with an R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.698, 0.0561, 0.0046, and 0.0382 for 2021 corn cropping season, whereas the LSTM model using inputs of previous seven days yielded R2, MAE (m3m−3), MSE (m6m−6), and RMSE (m3m−3) of 0.998, 0.00237, 0.00002, and 0.00382, respectively as tested for cropping season of 2020 and 0.973, 0.00368, 0.00003 and 0.00577 as tested for the cropping season of 2021. The LSTM model presents a viable data-driven alternative to traditional statistical models for forecasting subsurface soil moisture.

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Forecasting Soil Moisture Based on Evaluation of Time Series Analysis

Cited by 7 publications

References 19 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage

Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data

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