Soil Quality Change after Reclaiming Subsidence Land with Yellow River Sediments

Duo, Linghua; Hu, Zhenqi

doi:10.3390/su10114310

Cited by 21 publications

(10 citation statements)

References 25 publications

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“…Reclaiming subsidence land with Yellow River sediment appears to be a promising method (Hu, Duo, & Wang, 2018). For mines near the Yellow River, this approach is an important method for restoring cultivated land lost due to coal mining subsidence (Duo & Hu, 2018). F. Shao et al (2013) conducted field infiltration experiments of reclamation farmland soil filled with the Yellow River sediment and found that Yellow River sediment layer did poorly in water retention.…”

Section: Discussionmentioning

confidence: 99%

Restoring subsided coal mined land to farmland using optimized placement of Yellow River sediment to amend soil

Wang

McSweeney

et al. 2022

Land Degrad Dev

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Underground coal mining causes land subsidence and large areas of cultivated land are destroyed. The Yellow River interlayer filling reclamation technology is a powerful way to restore cultivated land. Understanding the mechanism of action of interlayers in reconstructed soil filled with Yellow River sediment is essential to achieving sustainable land management in the Yellow River regions. Column experiments and field experiments were conducted to explore optimum interlayer placement in reconstructed soil with Yellow River sediment for restoring subsided land. Our findings show that the inclusion of interlayers in the sediment reduced water leakage and moisture evaporation and improved the water-holding capacity of the material in comparison to a conventional reconstructed soil profile (CK2). The optimal profile with the highest water-holding capacity was constructed using two 30 cm thick interlayers (T6). In comparison to CK2, the migration rate of the wetting front decreases by 32.16%, the cumulative evaporation decreases by 16.29%, the volumetric water content of the filling layer (θ fl ) increases by 121.56%, and the water-holding coefficient (C WR ) increases by 59.47%. The same trend is also demonstrated by field experiments. The wheat and maize yields of T6 improved 45.85% and 60.69%, respectively, as compared with CK2, that more closely resembles undisturbed farmland (CK1). This study provides a valuable framework for subsided land reclamation regarding the method of placing interlayers into Yellow River sediment for enhancing water retention and productivity.

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Section: Discussionmentioning

confidence: 99%

Restoring subsided coal mined land to farmland using optimized placement of Yellow River sediment to amend soil

Wang

McSweeney

et al. 2022

Land Degrad Dev

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“…New studies suggest that dredged material-poorly graded sand-from the YRD can be mixed with the saline, clayey flood plain soils to improve agricultural productivity (Mao et al, 2016(Mao et al, , 2018. Additionally, YRD sediments are deposited on agricultural lands adjacent to underground coal mines to combat subsidence and reduce waterlogging (Duo & Hu, 2018).…”

Section: Mitigating Flood Vulnerability-dredge Spoil Repurposing Vs T...mentioning

confidence: 99%

Flood risk of embanked areas and potential use of dredge spoils as mitigation measures in the southwest region of the Ganges–Brahmaputra–Meghna Delta, Bangladesh

Valentine

Wilson

Rahman

2022

Earth Surf Processes Landf

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In the 1960s, earthen embankments, locally known as polders, were first constructed in the Ganges–Brahmaputra–Meghna (GBM) Delta with the intention of protecting agricultural lands from salinity; however, unintended consequences of the barriers have negatively impacted the region. Once‐active distributary channels have morphed into terminal, headless tidal channels as the sediment‐laden water that was once dispersed across the tidal delta plain is now restricted by embankments. Elevation loss within embankments and channel siltation adjacent to embankments are deleterious consequences of hard engineering and pose significant flood risk when effective sea level rise is considered. The purpose of this study is to assess the current and future flood risks of the embanked areas in the southwest region of the GBM Delta and to expand on the potential for local‐based solutions (e.g. mud plinths and embankments). We propose a novel source of material for these structures—repurposed dredge spoils. Project sites are along terminal tidal channels, proximal to coastal towns in the southwest region of the delta. We calculate here that as the effective sea level rise will continue to increase in the years to come, the flood risk will correspondingly increase (>67%). Geospatial analyses reveal that several prominent tidal channels have reduced in width over the last 30+ years, and recent dredging operations removed ~1.7 × 106 m3 of sediment infill. Using conservative values, we estimate that the dredge spoils could be used to elevate ~20 000 houses on mud plinths or raise 18 km of existing embankments. On a delta‐wide scale, if all infilled tidal channels were dredged to 10% of their original widths, ~547 000 houses could be raised above the flood levels and ~500 km of embankments could be elevated. With a mean household population of 4.3 people, repurposing dredge spoils can positively impact ~2.4 million people within the vulnerable southwest region of the GBM Delta.

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“…In this approach, the subjectivity of assigning weights to evaluation indices is eliminated [19], substantially improving the efficiency of the CLQ evaluation. Many machine learning models are available, but those most frequently used for CLQ evaluation include backpropagation neural network (BPNN) [20,21], decision tree (DT) [22,23], random forest (RF) [18,24], and support vector machine (SVM) [25,26].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction

Wang

et al. 2022

IJERPH

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The integrated use of remote sensing technology and machine learning models to evaluate cultivated land quality (CLQ) quickly and efficiently is vital for protecting these lands. The effectiveness of machine-learning methods can be profoundly influenced by training samples. However, in the existing research, samples have mainly been constructed by random point (RPO). Little attention has been devoted to the optimization of sample construction, which may affect the accuracy of evaluation results. In this study, we present two optimization methods for sample construction of random patch (RPA) and area sequence patch (ASP). Differing from RPO samples, it aims to include cultivated land area and its size into sample construction. Based on landsat-8 Operational Land Manager images and agricultural land grading data, the proposed sample construction methods were applied to the machine learning model to predict the CLQ in Dongtai City, Jiangsu Province, China. Four machine learning models (the backpropagation neural network, decision tree, random forest (RF), and support vector machine) were compared based on RPO samples to determine the accurate evaluation model. The best machine learning model was selected to compare RPA and ASP samples with RPO samples. Results determined that the RF model generated the highest accuracy. Meanwhile, a high correlation was noted between the cultivated land area and CLQ. Thus, incorporating cultivated land area in the sample construction attributes can improve the prediction accuracy of the model. Among the three sample construction methods, the ASP yielded the highest prediction accuracy, indicating that the use of a large, cultivated land patch as the sample unit can further elevate the model performance. This study provides a new sample construction method for predicting CLQ using a machine learning model, as well as providing a reference for related research.

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Soil Quality Change after Reclaiming Subsidence Land with Yellow River Sediments

Cited by 21 publications

References 25 publications

Restoring subsided coal mined land to farmland using optimized placement of Yellow River sediment to amend soil

Restoring subsided coal mined land to farmland using optimized placement of Yellow River sediment to amend soil

Flood risk of embanked areas and potential use of dredge spoils as mitigation measures in the southwest region of the Ganges–Brahmaputra–Meghna Delta, Bangladesh

Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction

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