Design and implementation of a hybrid MLP-FFA model for soil salinity prediction

Pouladi, Nastaran; Jafarzadeh, Ali Asghar; Shahbazi, Farzin; Ghorbani, Mohammad Ali

doi:10.1007/s12665-019-8159-6

Cited by 18 publications

(10 citation statements)

References 28 publications

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“…Additionally, Habibi et al 104 proved the potential of hybrid ML model i.e., ANN-GA (artificial neural network-genetic algorithm) against the ANN, PSLR (partial least square regression), and DT (decision tree) models for soil salinity prediction in central Iran. Pouladi et al 105 predicted the soil salinity in Miandoab city of Iran by employing the MLP-FFA (multilayer perceptron-firefly algorithm) model using remote sensing and topography data. The outcomes of MLP-FFA model were compared with MLP model based on several statistical indices.…”

Section: Application Results and Analysismentioning

confidence: 99%

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

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The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl− (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

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Section: Application Results and Analysismentioning

confidence: 99%

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

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“…To estimate the accuracy of RF predictions, we used the coefficient of determination (R 2 ), RMSE and mean relative error (MRE) between the measured and predicted values to assess the model performance ( Childs, Coffey & Travis, 2007 ; Pouladi et al, 2019 ). The formulas are:…”

Section: Methodsmentioning

confidence: 99%

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

Feng¹,

Lin²,

Yu³

et al. 2021

PeerJ

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In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

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“…MLP is a feedforward supervised neural network that has been applied successfully for complex and nonlinear problems. The backpropagation learning algorithm is commonly used for training MLP models, but it may get trapped in a local optimum (Pouladi et al 2019). The MLP model uses multiple layers with a nonlinear activation function to learn the relationship between input and output datasets.…”

Section: -4-2-multilayer Perceptron (Mlp) Modelmentioning

confidence: 99%

GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables

et al. 2021

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Accurate prediction of soil temperature (Ts) is critical for efficient soil, water and field crop management. In this study, hourly Ts variations at 5, 10, and 30 cm soil depth were predicted for an arid site (Sirjan) and a semi-humid site (Sanandaj) in Iran. Existing machine learning models have high performance, but suffer from uncertainty and instability in prediction. Therefore, GLUE approach was implemented to quantify model uncertainty, while wavelet coherence was used to assess interactions between Ts and meteorological parameters. Standalone machine learning models (adaptive neuron fuzzy interface system (ANFIS), support vector machine model (SVM), radial basis function neural network (RBFNN), and multilayer perceptron (MLP)) were hybridized with four optimization algorithms (sunflower optimization (SFO), firefly algorithm (FFA), salp swarm algorithm (SSA), particle swarm optimization (PSO)) to improve Ts prediction accuracy and reduce model uncertainty. For both arid and semi-humid sites, ANFIS-SFO produced the most accurate performance at studied soil depths. At best, hybridization with SFO (ANFIS-SFO, MLP-

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Design and implementation of a hybrid MLP-FFA model for soil salinity prediction

Cited by 18 publications

References 28 publications

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables

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