A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran

Nalivan, Omid Asadi; Tayebi, Seyed Ali Mousavi; Mehrabi, Mohammad; Ghasemieh, Hoda; Scaioni, Marco

doi:10.1007/s00477-022-02368-y

Cited by 10 publications

(2 citation statements)

References 66 publications

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“…By providing a flexible non-linear space, ANNs can map the relationship between the building properties and required thermal loads. Some examples of other fields in which machine learning models have promisingly served can be predicting engineers parameters such as streamflow [19], material strength [20], groundwater potential [21], and pan evaporation [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

Peng,

Chen

2023

PLoS ONE

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As is known, early prediction of thermal load in buildings can give valuable insight to engineers and energy experts in order to optimize the building design. Although different machine learning models have been promisingly employed for this problem, newer sophisticated techniques still require proper attention. This study aims at introducing novel hybrid algorithms for estimating building thermal load. The predictive models are artificial neural networks exposed to five optimizer algorithms, namely Archimedes optimization algorithm (AOA), Beluga whale optimization (BWO), forensic-based investigation (FBI), snake optimizer (SO), and transient search algorithm (TSO), for attaining optimal trainings. These five integrations aim at predicting the annual thermal energy demand. The accuracy of the models is broadly assessed using mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) indicators and a ranking system is accordingly developed. As the MAPE and R2 reported, all obtained relative errors were below 5% and correlations were above 92% which confirm the general acceptability of the results and all used models. While the models exhibited different performances in training and testing stages, referring to the overall results, the BWO emerged as the most accurate algorithm, followed by the AOA and SO simultaneously in the second position, the FBI as the third, and TSO as the fourth accurate model. Mean absolute error (MAPE) and Considering the wide variety of artificial intelligence techniques that are used nowadays, the findings of this research may shed light on the selection of proper techniques for reliable energy performance analysis in complex buildings.

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Section: Literature Reviewmentioning

confidence: 99%

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

Peng,

Chen

2023

PLoS ONE

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“…ML algorithms utilize past experiments to establish successful relationships for data inputs, rebuild the knowledge schema, and process them for future prediction [15,16]. They are used in agriculture [17][18][19], especially in determining soil quality [20][21][22], to enhance efficiency, reduce production costs, and minimize environmental impact [23,24]. Machine learning methods can be supervised [25], unsupervised [26], or reinforcement learning [27].…”

Section: Introductionmentioning

confidence: 99%

An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

El Behairy,

El Arwash,

El Baroudy

et al. 2024

Agriculture

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Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R2) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO3, pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.

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A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping

Razavi-Termeh,

Hatamiafkoueieh,

Sadeghi-Niaraki

et al. 2023

Stoch Environ Res Risk Assess

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Landslides pose a significant threat to human life and infrastructure, underscoring the ongoing need for accurate landslide susceptibility mapping (LSM) to effectively assess risks. This study introduces an innovative approach that leverages multi-objective evolutionary fuzzy algorithms for landslide modeling in Khalkhal town, Iran. Two algorithms, namely the non-dominated sorting genetic algorithm II (NSGA-II) and the evolutionary non-dominated radial slots-based algorithm (ENORA), were employed to optimize Gaussian fuzzy rules. By utilizing 15 landslide conditioning factors (aspect, altitude, distance from the fault, soil, slope, lithology, rainfall, distance from the road, the normalized difference vegetation index (NDVI), land cover, plan curvature, profile curvature, topographic wetness index (TWI), stream power index (SPI), and distance from the river) and historical landslide events (153 landslide locations), we randomly partitioned the input data into training (70%) and validation (30%) sets. The training set determined the weight of conditioning factor classes using the frequency ratio (FR) approach. These weights were then used as inputs for the NSGA-II and ENORA algorithms to generate an LSM. The NSGA-II algorithm achieved a root-mean-square error (RMSE) of 0.25 during training and 0.43 during validation. Similarly, the ENORA algorithm demonstrated an RMSE of 0.28 in training and 0.48 in validation. The findings revealed that the LSM created by the NSGA-II algorithm exhibited superior predictive capabilities (area under the receiver operating characteristic curve (AUC) = 0.867) compared to the ENORA algorithm (AUC = 0.844). Additionally, a particle swarm optimization (PSO) algorithm was employed to determine the importance of conditioning factors, identifying lithology, land cover, and altitude as the most influential factors.

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A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran

Cited by 10 publications

References 66 publications

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

Integrative soft computing approaches for optimizing thermal energy performance in residential buildings

An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

A GIS-based multi-objective evolutionary algorithm for landslide susceptibility mapping

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