An ISaDE algorithm combined with support vector regression for estimating discharge coefficient of W-planform weirs

Emami, Somayeh; Parsa, Javad; Emami, Hojjat; Abbaspour, Akram

doi:10.2166/ws.2021.112

Cited by 11 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the most appropriate input parameters, seven different combinations were examined. In the first step, all parameters were selected as model input, and then one of the input parameters was removed, and the proposed hybrid model was rerun (Figure 5) [1,35].…”

Section: Input Parametersmentioning

confidence: 99%

“…Currently, one of the appropriate solutions to calculate the performance and phonological characterization of crops is intelligent techniques. Increased costs, and time-consuming and human errors have led to the use of 3D and computer models [1]. Various studies on modeling and estimating yield and water productivity (WP)…”

Section: Introductionmentioning

confidence: 99%

“…Evaluation Criteria R 2 , RMSE, SI, σ, and NSE indices were utilized to assess the proposed hybrid model (Table5)[1]:…”

mentioning

confidence: 99%

See 2 more Smart Citations

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Dehghanisanij

Emami

Achite

et al. 2021

Water

Self Cite

View full text Add to dashboard Cite

Water productivity (WP) of crops is affected by water–fertilizer management in interaction with climatic factors. This study aimed to evaluate the efficiency of a hybrid method of season optimization algorithm (SO) and support vector regression (SVR) in estimating the yield and WP of tomato crops based on climatic factors, irrigation–fertilizer under the drip irrigation, and plastic mulch. To approve the proposed method, 160 field data including water consumption during the growing season, fertilizers, climatic variables, and crop variety were applied. Two types of treatments, namely drip irrigation (DI) and drip irrigation with plastic mulch (PMDI), were considered. Seven different input combinations were used to estimate yield and WP. R2, RMSE, NSE, SI, and σ criteria were utilized to assess the proposed hybrid method. A good agreement was presented between the observed (field monitoring data) and estimated (calculated with SO–SVR method) values (R2 = 0.982). The irrigation–-fertilizer parameters (PMDI, F) and crop variety (V) are the most effective in estimating the yield and WP of tomato crops. Statistical analysis of the obtained results showed that the SO–SVR hybrid method has high efficiency in estimating WP and yield. In general, intelligent hybrid methods can enable the optimal and economical use of water and fertilizer resources.

show abstract

Section: Input Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Dehghanisanij

Emami

Achite

et al. 2021

Water

Self Cite

View full text Add to dashboard Cite

show abstract

Evaluating performance indicators of irrigation systems using swarm intelligence methods in Lake Urmia basin, Iran

Dehghanisanij

Emami

et al. 2023

Environ Dev Sustain

View full text Add to dashboard Cite

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

et al. 2022

View full text Add to dashboard Cite

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m3, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m3/ha, which suggests a decrease of 5019.20 and 2509.6 m3/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.

show abstract

An ISaDE algorithm combined with support vector regression for estimating discharge coefficient of W-planform weirs

Cited by 11 publications

References 36 publications

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Estimating Yield and Water Productivity of Tomato Using a Novel Hybrid Approach

Evaluating performance indicators of irrigation systems using swarm intelligence methods in Lake Urmia basin, Iran

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Contact Info

Product

Resources

About