Novel Genetic Algorithm (GA) based hybrid machine learning-pedotransfer Function (ML-PTF) for prediction of spatial pattern of saturated hydraulic conductivity

VK Singh, KC Panda, A Sagar, N Al-Ansari… - Engineering …, 2022 - Taylor & Francis
Saturated hydraulic conductivity (Ks) is an important soil characteristic that controls water
moves through the soil. On the other hand, its measurement is difficult, time-consuming, and …

Machine learning-based intelligent modeling of hydraulic conductivity of sandy soils considering a wide range of grain sizes

Z ur Rehman, U Khalid, N Ijaz, H Mujtaba, A Haider… - Engineering …, 2022 - Elsevier
This study presents novel intelligent modeling of the hydraulic conductivity (k) of sandy soil
by employing machine learning (ML) algorithms ie, artificial neural network (ANN), multi …

Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model

A Malik, A Kumar, S Kim, MH Kashani… - Engineering …, 2020 - Taylor & Francis
The potential of several predictive models including multiple model-artificial neural network
(MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM) …

Machine learning applications for water-induced soil erosion modeling and map**

H Sahour, V Gholami, M Vazifedan, S Saeedi - Soil and Tillage Research, 2021 - Elsevier
Assessment of water-induced soil erosion as a crucial part of soil conservation plans is
costly and time-consuming when applied to an extensive area. In this study, we propose a …

A novel approach to predict CO2 emission in the agriculture sector of Iran based on Inclusive Multiple Model

E Shabani, B Hayati, E Pishbahar, MA Ghorbani… - Journal of Cleaner …, 2021 - Elsevier
Due to the significant effects of CO 2 emissions on climate change and global warming, as
well as its serious hazards to human health, the prediction of CO 2 emission is a vital issue …

Use of artificial neural network to evaluate cadmium contamination in farmland soils in a karst area with naturally high background values

C Li, C Zhang, T Yu, X Liu, Y Yang, Q Hou, Z Yang… - Environmental …, 2022 - Elsevier
In recent years, the naturally high background value region of Cd derived from the
weathering of carbonate has received wide attention. Due to the significant difference in soil …

Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in …

K Li, G Guo, D Zhang, M Lei, Y Wang - Journal of Hazardous Materials, 2024 - Elsevier
Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for
soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution …

Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran

V Gholami, MJ Booij - Journal of Cleaner Production, 2022 - Elsevier
Increased nitrate concentration is one of the main groundwater quality problems today that
needs to be measured and monitored. Water quality testing and monitoring are time …

A comparison of BPNN, GMDH, and ARIMA for monthly rainfall forecasting based on wavelet packet decomposition

W Wang, Y Du, K Chau, H Chen, C Liu, Q Ma - Water, 2021 - mdpi.com
Accurate rainfall forecasting in watersheds is of indispensable importance for predicting
streamflow and flash floods. This paper investigates the accuracy of several forecasting …

Application of BP-ANN model in evaluation of soil quality in the arid area, northwest China

W Shao, Q Guan, Z Tan, H Luo, H Li, Y Sun… - Soil and Tillage …, 2021 - Elsevier
Soil quality evaluation is an effective way to improve ecological environment quality of soil
and to perfect management system as well as keep its productivity of the soil sustainable …