Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM

Mathematics, Journal Year: 2024, Volume and Issue: 12(5), P. 627 - 627

Published: Feb. 20, 2024

The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the too high, it will seriously affect survival state aquaculture. Therefore, prediction control in advance essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) Long Short-Term Memory neural network (LSTM) to predict mariculture. Firstly, weight assignment strategy was improved, number correction iterations introduced retard shortcomings data error accumulation caused by AdaBoost basic algorithm. Then, XAdaBoost algorithm generated several LSTM su-models concentration. Finally, there were two experiments conducted verify effectiveness model. In experiment, compared with other comparison models, RMSE XAdaBoost–LSTM reduced about 0.89–2.82%, MAE 0.72–2.47%, MAPE 8.69–18.39%. stability RMSE, MAE, decreased 1–1.5%, 0.7–1.7%, 7–14%. From these experiments, evaluation indexes superior which proves that has good accuracy lays foundation monitoring regulating change future.

Language: Английский

---

Does artificial ecosystem recharge make sense? based on the coupled water orbit research framework

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112496 - 112496

Published: Aug. 19, 2024

Language: Английский

---

Transforming PFAS management: A critical review of machine learning applications for enhanced monitoring and treatment

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 106941 - 106941

Published: Jan. 15, 2025

Language: Английский

---

Evaluation and prediction of groundwater quality for irrigation using regression and machine learning models

Water quality research journal., Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

ABSTRACT This study evaluates and predicts six water quality indices such as sodium adsorption ratio (SAR), Kelly's (KR), percentage (%Na), permeability index (PI), exchangeable (ESP), irrigation (IWQI) using multivariate regression models (MLR, PLSR, PCR, WLSR) machine learning (ML) algorithms (ANN, SVM, CART, CRRF, KNN). The analyzes data from 360 dug wells in Sundargarh district, India, during 2014–2021 with 70% used for training 30% testing. Spatial mapping of SAR, KR, ESP, PI exhibits higher suitability groundwater. Mann–Kendall test trend analysis shows a monotonic increasing decreasing %Na, PI, IWQI, respectively, at p > 0.05 2014–2021. Principal component discriminant identify Na+, the most influential WQ variables affecting groundwater this area. MLR WLSR are superior predicting SAR while ANN is best-suited ML model ESP. CRRF IWQI relatively accuracy. These findings demonstrate effectiveness improving assessment, providing valuable insights groundwater-based crop management.

Language: Английский

---

Dissolved Oxygen Prediction in the Dianchi River Basin with Explainable Artificial Intelligence based on Physical Prior Knowledge

Environmental Modelling & Software, Journal Year: 2025, Volume and Issue: 188, P. 106412 - 106412

Published: March 5, 2025

Language: Английский

---