Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation

Mahmoudi, Neda; Majidi, Arash; Jamei, Mehdi; Jalali, Mohammadnabi; Maroufpoor, Saman; Shiri, Jalal; Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Zaher Mundher

doi:10.1016/j.agwat.2021.107342

Cited by 18 publications

(13 citation statements)

References 54 publications

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“…Mahmoudi et al [33] built new data intelligence models. Several hybridized models, including grey wolf optimization, were developed and tested in various regions in order to determine their performance.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

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Section: Introductionmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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“…This system achieves 95% and 93% accuracy using classification and regression techniques. The authors of [23] propose a location-based optimized irrigation system using a genetic algorithm with the help of previous data. The location-based water-requirement analysis for irrigation systems using the KNN algorithm with an intelligent IoT sensor is used to plan irrigation systems [5].…”

Section: Irrigation Techniquesmentioning

confidence: 99%

“…IoT framework [5,8,9,16,23] The site-specific variable-rate sprinkler irrigation [9] (SS-VRT) used crop and soil conditions for irrigation, KMO [23] is used to analyse the factors of irrigations, SWAMP [5] architecture provides better scalability, Federated learning [16] is used for irrigation without sharing the data, using machine learning.…”

Section: Previous Models Advantages Limitationsmentioning

confidence: 99%

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

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“…For each iteration, it selects the global best solution, after that each artificial scout bee is computed by using (9). en, the value of fw is examined to decide whether it is 1 or 0 and if x i,t fitness � 0. pace is generated by utilizing (10). But, a random number denoted by r of range [−1, 1] will be generated if fw >0 and fw <1.…”

Section: Single-objectivementioning

confidence: 99%

“…Heuristic algorithms use the trial-and-error method to find a solution, and it is supposed that these algorithms will consume reasonable time to reach a solution [ 9 , 10 ]. Moreover, the aim of heuristic algorithms is to utilize various methods in local examinations and techniques of randomization [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

Tahir

Rashid

Rauf

et al. 2022

Computational Intelligence and Neuroscience

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Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E−12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration.

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Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation

Cited by 18 publications

References 54 publications

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

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